full course - law and regulation of machine intelligence - bar ilan university 2016
TRANSCRIPT
Law and Regulation of Machine Intelligence
Prof. Nicolas Petit ©; Twitter: @CompetitionProf
Fall 2016, Bar Ilan University
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Prospective issues A robot surgeon misdiagnoses your illness, who’s liable? You lease a robot to a large corporation to milk your herd of cows. The robot kills
one cow, who is liable? A robot programme is hired to select the next salesforce of a company: only returns
male profiles, and refuses to shortlist female profiles? A robot spies on your spouse who cheats, shall he report? Data protection issue Trolley problem: Google’s car runs into a kid or an old woman? A kid with 5% high
injury risk v a tree with 99% high injury risk for 4 passengers? A dog v another car? A robot creates a new song: who owns it? What if the song sounds similar to that of
a copyrighted work? Who’s liable for the infringement? Bots: http://motherboard.vice.com/read/the-best-things-a-random-bot-bought-on-
the-darknet You dont want to drive an autonomous car, but the insurance company refuses to
provide a contract: is this ok? A robot in human form is getting beat in the street by police officers
Right to dignity?; but the robot has killed someone: right to shoot him down? Death penalty for Bots? Right to procreate, to dignity, to decent funerals?
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Asimov’s three laws of robotics (1950)
Device that is well-suited for work that is too dull, dirty or dangerous for real humans
Safety feature introduced in all bots LAW 1: A robot may not injure a human being or, through inaction, allow a
human being to come to harm;
LAW 2: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law;
LAW 3: A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws
In later fiction where robots had taken responsibility for government of whole planets and human civilizations, Asimov also added a fourth, or zeroth law, to precede the others:
LAW 0: A robot may not harm humanity, or, by inaction, allow humanity to come to harm
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Not science fiction – NHTSA, 4 February 2016
National Highway Traffic Safety Administration (NHTSA) is US Highway Safety Agency in charge of enforcing the Federal Motor Vehicle Safety Standards (FMVSSs)
Provides certification for new vehicles by automotive producers
Number of FMVSSs requirements
All buit around the notion of “driver”, and “driver’s position” or “driver’s seating position”
“MVSS No. 101 contains requirements for location, identification, color, and illumination of motor vehicle controls, telltales, and indicators. S5.1.1 requires the controls listed in Tables 1 and 2 of the standard to be located so that they are operable by the [belted] driver”.
“S5.3.1, which states that service brakes shall be activated by means of a foot control”
49 CFR 571.3 defines “driver” as the occupant of a motor vehicle seated immediately behind the steering control system.
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Google’s “SDS”
“Google seeks to produce a vehicle that contains L4 automated driving capabilities, and removes conventional driver controls and interfaces (like a steering wheel, throttle pedal, and brake pedal, among many other things)”.
“Expresses concern that providing human occupants of the vehicle with mechanisms to control things like steering, acceleration, braking, or turn signals, or providing human occupants with information about vehicle operation controlled entirely by the SDS, could be detrimental to safety because the human occupants could attempt to override the SDS’s decisions”
“Google’s design choices in its proposed approach to the SDV raise a number of novel issues in applying the FMVSSs. Those standards were drafted at a time when it was reasonable to assume that all motor vehicles would have a steering wheel, accelerator pedal, and brake pedal, almost always located at the front left seating position, and that all vehicles would be operated by a human driver. Accordingly, many of the FMVSSs require that a vehicle device or basic feature be located at or near the driver or the driver’s seating position. For vehicles with an AI driver that also preclude any occupant from assuming the driving task, these assumptions about a human driver and vehicle controls do not hold”.
“Google has asked who or what is to be considered the driver and which seating position is considered to be the driver’s seating position in its SDV.”
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A Car has a Driver, a Driver need not behuman
Options
1) “NHTSA could interpret the term “driver” as meaningless for purposes of Google’s SDV, since there is no human driver, and consider FMVSS provisions that refer to a driver as simply inapplicable to Google’s vehicle design”;
2) “NHTSA could interpret “driver” and “operator” as referring to the SDS”
NHTSA As a foundational starting point for
the interpretations below, NHTSA will interpret driver in the context of Google’s described motor vehicle design as referring to the SDS, and not to any of the vehicle occupants.
If no human occupant of the vehicle can actually drive the vehicle, it is more reasonable to identify the driver as whatever (as opposed to whoever) is doing the driving. In this instance, an item of motor vehicle equipment, the SDS, is actually driving the vehicle.
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Consequences
“The controls listed in Tables 1 and 2 may simply be operable by the SDS and need not be located so that they are available to any of the human occupants of the motor vehicle”.
For more, see http://isearch.nhtsa.gov/files/Google%20--
%20compiled%20response%20to%2012%20Nov%20%2015%20interp%20request%20--%204%20Feb%2016%20final.htm
http://spectrum.ieee.org/cars-that-think/transportation/self-driving/an-ai-can-legally-be-defined-as-a-cars-driver
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Schedule
Topics Date
Technology and Society 22-11-16
Theory of Regulation; Liability 23-11-16
Robotic Warfare; Market Conduct 25-11-2016
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Aims of the course
Basic questions
Should we regulate AIs and robots?
If yes, how should we regulateAIs and robots?
Goals
Identify problems more thansolutions
Think of frameworks/methodsto mindmap those issues
Learn from you
Class I – Technology and Society
1. State of the Art
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Examples
Robotic cars
Drones
Hive and swarm robots
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An old field, AI and robotics
1950s: « Game AI », Arthur Samuel’s checker-playing program
1954: Turing test
1955: Newell and Simon, « Logic Theorist », proves 38 out of 52 mathematical problems
Dartmouth Summer Research Project of 1956
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Initial ideas
Any physical process including the mind process can bemodelized as a computable algorithm (Church Turing thesis)
A software is a set of computable algorithms. No reasonwhy it could not reach outcomes similar to thosegenerated by the mind (artefact)
Machines can learn: “Learning is any process by which a system improves performance from experience”, Herbert Simon
Ultimate ambition is to have computers do what humansdo well (they already know how to do things humanscannot do): heuristic, seeing, learning
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Milestones
In 1997, DeepBlue beats Gary Kasparov at chess
In 2005, Stanford robot wins DARPA Grand Challenge by driving autonomously for 131 miles along unrehearsed desert trail
In February 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeats the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings
In January 2016, Researchers from Google DeepMind have developed the first computer able to defeat a human champion at the board game Go
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2020s, Personal computers will have the same processing power as human brains.
2030s, Mind uploading becomespossible.
2040s, Human body 3.0 (as Kurzweil calls it) comes into existence; People spend most of their time in full-immersion virtual reality
2045s, The Singularity occurs as artificial intelligences surpass human beings as the smartest and most capable life forms on the Earth; The extermination of humanity by violent machines is unlikely
Kurzweil, « The Singularity is Near », 2005
Law of accelerating returns: technology progressing toward Singularity at exponential rate (each transition occurs more rapidly than the last)
Functionality of the brain is quantifiable in terms of technology
Baby boomers will live long enough to see singularity. Nanobotswill eventually be able to repair and replace any part of the body that wears out
Strong Artificial Intelligences and cybernetically augmented humans will become the dominant forms of sentient life on the Earth
Source: Wikipedia
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Why now?
Moravec paradox solved? High level reasoning (playing
chess, using mathematics, etc.) easier than low-level sensorimotor skills (moving across space, recognizing speech, etc.)
This is because high level reasoning demands less computational power
See Moravec, H. (1998). When will computer hardware match the human brain. Journal of Evolution and Technology, 1.
Technological evolution
Brute force computationalpower now available (due to Moore’s law)
Vast troves of data nowavailable, and distributed(cloud)
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Various subfields of AI, and examples
Deep learning (or machine learning) Algorithms that enable robots to learn tasks through trial and error using a process
that more closely approximates the way humans learn: spam filtering Neural networks
Emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses
Speech recognition Uses sound metrics along with domain and context specific language to respond to
voice commands Natural language processing
Robots interacting and responding through interpretations of natural languageinstructions
Artificial vision Object recognition, which allow robots to interact and measure their environment .
Can include different features: visual object recognition and tracking, image stabilization, visual-based serving, human-to-machine interaction etc. => recognize x-rays, MRI scans, battlefield robots recognize kids, automated cars,
Knowledge representation
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Philosophical debate
Turing Dialogue with a human and a
machine through teletype If a machine could carry on a
conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then it was reasonable to say thatthe machine was "thinking”
Machine's ability to exhibitintelligent behavior equivalent to, or indistinguishable from, that of a human
A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-460
Searle « Chinese room » argument
You do not speak Chinese You’re in room with two slits, a book,
and some scratch paper Someone slides Chinese characters
through first slit You follow the instructions in the
book, correlate characters as instructedby book, and slide the resulting sheet in the second slit.
It appears you speak Chinese, yet youdo not understand a word of Chinese
No need to understand Chinese totranslate it. Fact that computer is ableto do it does not create strong AI
Weak AI that simulates thought
http://www.iep.utm.edu/chineser/
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Scientific debate
Is this a computational problem only (Kurzweil)?
Or is there an algorithm that we do not know?
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Challenges (1) Supervised learning v unsupervised learning
Human feeds neural network with inputs (image) and output/label (face, dog, lamp, etc.), and the AI comes up with a statistical rule that correlates inputs with the correct outputs => no causal explanation + error or reward signal
AI does identify patterns Pornography example: Justice Potter Stewart, SCOTUS: « I know it when I see it » But all this is supervised learning, and is finite resource => but Internet providers have
pre-labelled data Natural language
« Recognize speech » v « Wreck on the beach » Questions on Siri, Wolfram alpha, etc. generate different responses if different syntax is
used Disambiguation: « I ate spaghetti with meatballs » v « I ate spaghetty with chopsticks » (see R.
Mooney podcast) Artificial vision and the towel problem:
https://www.youtube.com/watch?v=gy5g33S0Gzo Common sense?
Magellan problem Optimizing transportation algorithm
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Robotics
Robotics is meta-technology
Robotics is about space motion
A robot is an « agencified » AI
Mechanical engineering issues are not relevant, thoughthey constitute significant limits Power
Haptic technology
Motricity
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Survey of most relevant applications, RoboLaw, 2014
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Related technologies
Human enhancement (and transhuman sciences)
Bionics (see J. Fischman, National Geographic, 2010) and prosthetics
Exosqueletons
Emulations
Mind uploading
Care robots, social robots, etc.
Augmented reality
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Definitional problem
1921, Karel Capek, R.U.R.
Rossum’s Universal Robots: Capek’s play makes first use of the word “robot” to describe an artificial person
Capek invented the term, basing it on the Czech word for “forced labor” or “servitude”: http://www.wired.com/2011/01/0125robot-cometh-capek-rur-debut/
2014, Robolaw
“In fact, the term “robot” can mean different things to different people, since there is no agreement on its meaning neither among professional users (i.e. roboticists) nor among laypeople”
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Definitions
“Actuated mechanism programmable in two or more axes (4.3) with a degree of autonomy (2.2), moving within its environment, to perform intended tasks”, ISO 8373:2012(en)
“A robot is a constructed system that displays both physical and mental agency, but is not alive in the biological sense ”, Neil M. Richards and William D. Smart, 2016
“Robots are mechanical objects that take the world in, process what they sense, and in turn act upon the world”, Ryan Calo, 2015.
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ISO 8373:2012(en)
“a degree of autonomy? ” Full autonomy means unexpected decisions in unexpected situations
“Moving within its environment”? Space motion is critical Softbots do not move? Need “Hard”-bot seated behind a computer? Human being who makes online transactions does not move, yet may create
harm “to perform intended tasks”
Intention of whom? Very confusing Robot with sufficient autonomy to intentionally resist to harmful third party
intentional order Drone will not launch bomb on hospital Google car will not crash in school Industrial bot refuses to operate if safety risk
Localizing intention: initial intention or subsequent command?
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« Constructed system that displays both physical and mental agency », Richards and Smart But robots created by
robots? Are theyconstructed?
How much mental agency? Semi unmanned drones
Robots w/o physicalagency: softbots (Hartzog, 2015) News generating bot Robot advertisers Computer composers:
http://www.gizmag.com/creative-artificial-intelligence-computer-algorithmic-music/35764/
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« Sense – Think – Act » spectrum?
Sense (acquire and process information)
Think (process whatwas sensed)
Act (locomotion and kinematics)
Low Industrial robots thatpaint or weld car parts
Exosqueleton, DaVinci Robot (teleoperated)
Augmented reality devices (Hololens), Medical diagnosisrobot
Medium Mars Rover, Drones
High Vacuum cleaner Social Robots Driverless car; HoopsRobotic basketball-shooting arm; Airport security check systems
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Robolaw typology
No definition, but 5 categories of relevant items: Use or task: service or industrial
Environment: physical (road, air, sea, etc.) v cyberspace
Nature: embodied or disembodied (bionic systems)
Human-Robot Interaction (HRI)
Autonomy
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Calo, 2015
Embodiement
Robot as « machine » or « hardware »
Softbots (eg, robot traders)?
Mooney thinks this is irrelevant; true from scientists perspective, but not necessary true from a society perspective
Emergence
New forms of conduct, including welfare enhancing behaviour => problemsolving robots
« Social valence »
Robots stimulate reactions from society: « social actors » Soldiers jeopardize themselves to preserve robots in military field
People write love letters to Philae
Often correlated to anthropomorphic embodiement (Honda Asimo)
2. Economics
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Pew Research Survey
48%
Tech pessimists
“a massive detrimental impact on society, where digital agents displace both blue- and white-collar workers, leading to income inequality and breakdowns in social order”
52%
Tech optimists
“anticipated that human ingenuity would overcome and create new jobs and industries”
Source: http://www.futureofwork.com/article/details/rise-of-intelligent-robots-will-widen-the-social-inequality-gap
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A. Smith, An Inquiry into the Nature and Causes of the Wealth of Nations, 1776 “A great part of the machines made use of in those manufactures
in which labour is most subdivided, were originally the inventions of common workmen, who, being each of them employed in some very simple operation, naturally turned their thoughts towards finding out easier and readier methods of performing it. Whoever has been much accustomed to visit such manufactures, must frequently have been shewn very pretty machines, which were the inventions of such workmen, in order to facilitate and quicken their own particular part of the work”.
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D. Ricardo, On Machinery, 1821
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J-M. Keynes, “Economic Possibilities for our Grandchildren”, 1930 “We are being afflicted with a new disease of which some readers may not yet have
heard the name, but of which they will hear a great deal in the years to come--namely, technological unemployment. This means unemployment due to our discovery of means of economising the use of labour outrunning the pace at which we can find new uses for labour. [...] But this is only a temporary phase of maladjustment. All this means in the long run that mankind is solving its economic problem”.
“Yet there is no country and no people, I think, who can look forward to the age of leisure and of abundance without a dread”
“ Three-hour shifts or a fifteen-hour week may put off the problem for a great while. For three hours a day is quite enough to satisfy the old Adam in most of us!”
Concludes by touting to disappearance of economics as a science
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Empirical studies
Bank of America/Merrill Lynch, 2015
“Robots are likely to be performing 45% of manufacturing tasks by 2025E (vs. 10% today)”
McKinsey Global Institute, Disruptive technologies Advances that will transform life, business, and the global economy, 2013
By 2025, “knowledge work automation tools and systems could take on tasks that would be equal to the output of 110 million to 140 million full-time equivalents (FTEs)” (knowledge work is use of computers to perform tasks that rely on complex analyses, subtle judgments, and creative problem solving).
By 2025, “[w]e estimate that the use of advanced robots for industrial and service tasks could take on work in 2025 that could be equivalent to the output of 40 million to 75 million full-time equivalents (FTEs)”.
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Frey and Osborne, 2013
“47 percent of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two”
“most workers in transportation and logistics occupations, together with the bulk of office and administrative support workers, and labour in production occupations, are at risk” + “a substantial share of employment in service occupations”
Wave IWave II Plateau
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Substitution effect, consequences
Job polarization
Shift in the occupationalstructure
Displaced workers relocate theirlabor supply to low skill service occupations
Other humans resist by investing in skills througheducation (Frey and Osborne, 2014; Cowen, 2013)
This leads to « labour marketpolarization » (Autor, 2014; Cowen, 2013)
Discussion Frey and Osborne, 2014 believe this
model still holds true “Our model predicts …
computerisation being principally confined to low-skill and low-wage occupations. Our findings thus imply that as technology races ahead, low-skill workers will reallocate to tasks that are non-susceptible to computerisation – i.e., tasks requiring creative and social intelligence”
Brynjolfsson and McAfee, 2011 disagree: when technology becomescognitive, substitution can alsooccur for non routine tasks
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Substitution pace
“Technological advances are contributing to declining costs in robotics. Over the past decades, robot prices have fallen about 10 percent annually and are expected to decline at an even faster pace in the near future (MGI, 2013). Industrial robots, with features enabled by machine vision and high-precision dexterity, which typically cost 100,000 to 150,000 USD, will be available for 50,000 to 75,000 USD in the next decade, with higher levels of intelligence and additional capabilities (IFR, 2012b). Declining robot prices will inevitably place them within reach of more users”
Hanson on copies
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Philips brings electric shaversproduction home? https://blogs.cfainstitute.org/investor/2014/06/16/the-robot-revolution-innovation-begets-innovation/
Effect on Developing Economies?
McKinsey Global Institute, 2013 “Effects of these technologies on
developing economies could be mixed. Some countries could lose opportunities to provide outsourced services if companies in advanced economies choose automation instead. But access to knowledge work automation technologies could also help level the playing field, enabling companies in developing countries to compete even more effectively in global markets”.
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Substitution (Engineering) Bottlenecks: Frey & Osborne, 2013
Social intelligence tasks Creative intelligence tasks Perception and manipulation tasks
Negotiation, persuasion and care
Ability to make jokes; recipes ; concepts
Disorganized environment or manipulation of non-calibrated, shifting shapes (towel problem)
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Autor, 2014
Routine tasks: “Human tasks that have proved most amenable to computerization are those that follow explicit, codifiable procedures”
Non routine tasks: “Tasks that have proved most vexing to automate are those that demand flexibility, judgment, and common sense”
Engineers “cannot program a computer to simulate a process that they (or the scientific community at large) do not explicitly understand”
Non routine tasks less exposed to substitution
Tasks that are not exposed may benefit from it, though complementarity effect
In construction, mechanization has not entirely devalued construction workers, but augmented their productivity; but not true for all (worker who knows to use shovel v excavator)
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Typology of D. Autor et al (2003), Autor (2014)Task Description Substitution risk
Routine (incl. skilled work)
Clerical work, bookeeping, back and middle office, factory work
High
Non routine « Abstract » « Manual » Low
Problem solving, intuition, creativity and persuasion
Situational adaptability, in person interaction, visual and languagerecognition
High education, high wage Low education, lowwage
Doctors, CEOs, managers, artists, academics
Housecleaning, flight attendants, foodpreparation, securityjobs
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Findings of D. Autor et al (2003), Autor (2014) Computers are more substitutable for human labour in routine
relative to non-routine tasks (substitution effect);
And a greater intensity of routine inputs increases the marginal productivity of non-routine inputs (complementarity effect)
“Job polarization” effect
Increase of high education, high wage jobs
Increase of non routine low education, low wage jobs
No increase in wages for this later category, given abundance of supply
Autor, D., Levy, F. and Murnane, R.J. (2003), “The skill content of recent technological change: An empirical exploration”, The Quarterly Journal of Economics, vol. 118, no. 4, pp. 1279–1333
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Job Polarization, some evidence (Autor, 2014)
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“Obtaining skills takes time studying in school and learning on the job. Thus skilled workers are disproportionately older workers”
“machine-biased productivity improvements effects a redistribution from younger, relatively unskilled workers to older relatively skilled workers as well as retirees”
“When today’s machines get smarter, today’s young workers get poorer and save less”
“The fall in today’s saving rate means that the next generation will have even lower wages than today”
“In short, better machines can spell universal and permanent misery for our progeny”
Generational effect, Sachs and Kotlikoff, 2012 Long term misery?
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T. Cowen, 2013
Average is over
The rich will get richer, the poor will get poorer
Substitution effect stronger in work w/o consciousness/abilityto train
Freestyle chess metaphor
Random player-machine teams outperform chessmaster-machine teams
Not necessary teams of grand masters!
“In the language of economics, we can say that the productive worker and the smart machine are, in today’s labor markets, stronger complements than before”
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Substitution-Complement Framework
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The model explained
Multi-causal substitution
Exponential decrease in costs of technology
« Deskilling » Replacement by semi-skilled
technologies, throughfragmentation and simplification of tasks (fordism)
« The copy economy » (Hanson, 2014): « the most important features of these artificial brainsis easy to copy »
Two types of complements Complements arising from
substitution (upward slopping curve) AI and Robots-related jobs (those of
Autor and Cowen) Enabling technologies and new jobs Punch cards, typewriters, printers,
calculators, etc. Complements with indifference (L
curve) Indifference on human labour of an
increase in machine labour (horizontal line) « Emerging jobs » new sectors without
human labour Protected sectors, superstars like chefs,
footballers and singers? Indifference on machines of increase in
human labour (vertical line) Bank tellers (ATMs?) => J. Bessen book
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Take aways
MGI, 2013:
“In some cases there may be regulatory hurdles to overcome. To protect citizens, many knowledge work professions (including legal, medical, and auditing professions) are governed by strict regulatory requirements regarding who may perform certain types of work and the processes they use”
“Policies discouraging adoption of advanced robots—for example, by protecting manual worker jobs or levying taxes on robots—could limit their potential economic impact”.
Frey and Osborne, 2013:
“The extent and pace of legislatory implementation can furthermore be related to the public acceptance of technological progress”
Brynjolfsson and McAfee, 2014 citing Voltaire : “Work saves a man from three great evils: boredom, vice, and need.”
3. Legal services and education
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LegalZoom: https://www.legalzoom.com/country/be “LegalZoom provides the legal solutions you need to start a business, run a business, file a
trademark application, make a will, create a living trust, file bankruptcy,change your name, and handle a variety of other common legal matters for small businesses and families. Since the process involved in starting a business can be complicated, we provide services to help start an LLC, form a corporation, file a DBA, and take care of many of the legal requirements related to starting and running a business. If you are interested in protecting your intellectual property, LegalZoom offers trademark and copyright registration services, as well as patent application assistance. It's essential for everyone to have a last will and testament and a living will, and you can get yours done efficiently and affordably through LegalZoom. For those who have more advanced planning needs, our living trust service is available. With all our services, you have the option to speak to a lawyer and tax professional. Let LegalZoom take care of the details so you can focus on what matters most – your business and family”
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Pricing Legal Zoom
Source: http://www.law-valley.com/blog/2014/03/03/legalzoom-le-leader-americain/
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Neota Logic Inc. http://www.neotalogic.com/solutions/
Concept Software company that helps
companies make routine legaldecisions without consulting a lawyer.
Let an employee take family leave(source of employmentdiscrimination claims)?
Input questions, and get results
Customer is business or law firms
Software has been used to answer queries on the European Union’s regulation of financial derivatives
Example: compliance
“Regulations are constantly changing. With a Neota Logic app, you can instantly incorporate changes to regulations and policies ensuring timely compliance. Incorporate apps into your regulatory processes and see how easy it is to ensure consistent methodologies are followed and provide your business with auditable results”
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Drivers of change (Susskind, 2014)
Contextual
More for less challenge Clients of lawyers (in-house
counsels): less staff, less externalcounselling, more compliance and conformity costs
https://www.lexoo.co.uk/
Liberalization http://thejurists.eu/
Structural
Information technology (Katz 2013) Large data power
Immense computing power
Automate and innovate
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Katz, 2013
Wind of change
“Like many industries before it, the march of automation, process engineering, informatics, and supply chain management will continue to operate and transform our industry. Informatics, computing, and technology are going to change both what it means to practice law and to “think like a lawyer.””
Substitution+complement
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Outlook
First generation
E-discovery
Automated document assembly
Online dispute resolution
New generation
Quantitative legal prediction
Contract analyzis
Online drafting wizard (Weagree.com)
Legal risk management
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Quantitative Legal Prediction
Everyday, lawyers make predictions
Do I have a case?
What is our likely exposure?
How much is this going to cost?
What will happen if we leave this particular provision out of this contract?
How can we best staff this particular legal matter?
How high the probability to settle?
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Predicting case outcomes
LexMachina
Lunch between Professor M. Lemley and Bruce Sewell
Create electronic set of patent litigation events and outcomes
https://lexmachina.com/about/
Funded by Apple, Cisco, Genentech, Intel, Microsoft, and Oracle, etc.
More at https://goo.gl/UyB0wU
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QLP and Machine Learning
Predicting outcomes
“An algorithm learning that in workplace discrimination cases in which there is a racial epithet expressed in writing in an email, there is an early defendant settlement probability of 98 percent versus a 60 percent baseline. An attorney, upon encountering these same facts, might have a similar professional intuition that early settlement is likely given these powerful facts”
No gaming (moral hazard)
Discover hidden data
“Imagine, for instance, that the algorithm detects that the probability of an early settlement is meaningfully higher when the defendant sued in a personal injury case is a hospital as compared to other types of defendants”
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TyMetrix
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LawyerMetrics
“Lawyer Metrics makes it possible to replace lower-performing “C players” in your organization with higher performing “B” and “A” attorneys”.
http://lawyermetrics.org/services/human/
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4 disruptive and robotic legal technologies
1. Embedded legal knowledge
2. Intelligent legal search
3. Big data
4. AI-based problem-solving (pb solving, withnatural language processing input)
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Pros and cons
Technique Use big data and computational
power “inverse” or inductive reasoning
Use observables to build model (>< build model, and then try to infer result)
Concept of similarity that is implemented and refined using large bodies of data
Facebook recommending friends, Netflix recommending movies and Amazon recommending books
Pros and Cons Pros
Overcomes anecdotal or unindicative information
Overcomes human cognitive limitations: heuristic, biases, preferences, etc.
Cons Lack of relatedness btw new cases
and past cases Overgeneralization: most rape
cases occured in poor areas => rapedoes not exist in wealthier areas
Capture information in data: change of member on board of regulator
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Stasia Kelly, U.S. comanaging partner of DLA Piper: “I really wantto know the person giving advice”
R. Susskind, Tomorrow’sLawyer, 2014
Often, lawyers regard legalwork as « bespoke »: customized, made to measure, personal
« romantic » vision
Impact on legal professions, and the denialproblem
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Really?
Take employment contract: you dont use a blankcanvas everytime you draft one
French lawyers all use same structure in work(standardized): I-II; AB
Anglo-saxon contracts always have definitions first
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Many of those tasks can besubject to
Outsourcing
Insourcing
Delawyering
Computerizing
Leasing
...
Decomposition
TABLE 4.1. Litigation, decomposed (Sussking, 2014
Document review
Legal research
Project management
Litigation support
(Electronic) disclosure
Strategy
Tactics
Negotiation
Advocacy
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Effect on legal market (Susskind, 2013)
Firms
Elite group of say 20 firms to Big4;
Opportunity for middle size firms;
Small firms will disappear, due to liberalization and competition from banks, accountants and other retailers (“end of lawyers who practice in the manner of a cottage industry”)
Contrast with Kobayashi and Ribstein?
Lawyers
Barristers will remain: “oral advocacy at its finest is
probably the quintessential bespoke legal service”
But not for “lower value” disputes and note that “courtroom appearances themselves will diminish in number with greater uptake of virtual hearings, while online dispute resolution (ODR) will no doubt displace many conventional litigators”
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Disciplines likely to be affected?
Corporate and M&A work Global Merger Analyzis Platform (GMAP):
http://www.ft.com/intl/cms/s/2/a1271834-5ac2-11e5-9846-de406ccb37f2.html#axzz41hY8v2x3
Trademark and copyrights filing
Patent applications
Private international law?
Your take?
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Disciplines likely to be affected
Data intensive
Public information
Searchable
Scalable (global law)
Standardized (labelled input)
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Users perspective
Provides « unmet legal needs » (Wilkins): brings law in consumer markets
Growth of LegalZoom is indicative
For more seehttp://www.slate.com/articles/technology/robot_invasion/2011/09/will_robots_steal_your_job_5.html
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LegalZoom
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Rocketlawyer
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Complementarity effect (Susskind, 2013)
New jobs
Legal knowledge engineer
Legal technologist
Legal hybrid
Legal process analyst
Legal project manager
ODR practitioner
Legal management consultant
Legal risk manager
New employers
Global accounting firms
Major legal publishers
Legal know-how providers
Legal process outsourcers
High street retail businesses
Legal leasing agencies
New-look law firms
Online legal service providers
Legal management consultancies
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Challenges – Education
Forget substitutable work
Routine tasks Memorization
Research and other repetitive data-driven tasks
Non routine manual-tasks? Filing briefs
Taking minutes of meetings
Invest in complements
Train in science, computation, data analytics and technology
Invest in soft skills, incl. leadership, executive training, management: « social bottlenecks »
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“Lawyers who expect to operate in this new environment must understand how technology is reshaping the markets in which their clients compete, as well as the practice of law itself, including the use of “big data,” artificial intelligence, and process management to analyze, structure, and produce legal outcomes” (Heineman, Lee and Wilkins, 2014)
Retraining need
“Clients will not be inclined to pay expensive legal advisers for work that can be undertaken by less expert people … This prediction does not signal the end of lawyers entirely, but it does point to a need for fewer traditional lawyers. At the same time when systems and processes play a more central role in law, this opens up the possibility of important new forms of legal service, and of exciting new jobs for those lawyers who are sufficiently flexible, open-minded, and entrepreneurial to adapt to changing market conditions” (Susskind, Chapter 11)
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« la délivrance automatisée de consultations en ligne n’est autorisée que pour répondre à la demande d’un client déterminé et pour satisfaire des besoins spécifiques » Article 4.12 (M.B. 17.01.2013);
« L’avocat ne délivre aucun service ni ne donne consultation ou avis personnalisés sur un forum de discussion électronique ou tout autre groupe virtuel public » Article 4.13 (M.B. 17.01.2013).
« En l’état actuel de la déontologie, une telle pratique n’est pas admise. L’avocat engage son crédit et sa responsabilité s’il n’adapte pas les actes qu’il rédige à l’examen de la situation particulière d’un client […] » (LB 01-02, n°3, 226)
Not OK?
Challenges – Professional Regulation
Attorney-client confidentiality
Limit to data aggregation by law firms?; Limit to data portability by lawyers?
Unauthorized exercise of profession
Partnership rules
Independence of lawyer?
Fee regulation
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Challenges – Intellectual property?
More IP protection could promote production of new legaltechnology
Too much IP protection prevents production of of new legaltechnology
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Legal preserves
Objections to machines taking over certain types of work?
Passing life sentence?
Other?
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Take away points
Think of tasks, not jobs
Users’ perspective also matters; Not only perspective of suppliers
Where machines are better than humans, expect substitution and/or complementarity
Shall the law prevent machines in legal services? No, substitution (and/or complementarity) not necessarily bad
But need to think about specific tasks for which society wants to preserve humans
Social contract imperative: man-made law necessary for trust?
Class II – Regulation and Liability
1. Regulation
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“Noel Sharkey, a computer scientist at the University of Sheffield, observes that overly rigid regulations might stifle innovation. But a
lack of legal clarity leaves device-makers, doctors, patients and insurers in the dark”
The Economist, 01 September 2012
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Definitions
Roboethics and robolaw
Regulation “State intervention into the economy by making and applying legal
rules”? (Morgan and Yeung) “Exceptionalism” (Calo, 2015): “a technology is exceptional if it invites
a systemic change to laws or legal institutions in order to preserve or rebalance established values”.
Use existing basic legal infrastructure, and deal with issues on a case-by-case basis, through litigation and precedent (common law
approach)
v
Adopt sui generis rules and updates
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Goals of the lecture
Where do we need regulation?
Put differently where do we need to (i) adapt existing law; (ii) introduce new law?
1.1 Overview of Existing Approaches
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Two trajectories
Disciplinary (legal)
In each branch, specialistsidentify frictional HYPOs
Top down
Suggestions for a green paper, 2012: “top down approach that studies for each legal domain the consequences on robotics”
Technology (applications)
For each class of applications, speculation on legal problems
Bottom up Robolaw, 2012
Stanford, Artificial Intelligence and Life in 2030
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1. Disciplinary approach
“fitting” exercize: jacket factory allegory
8 areas of the law: health and safety of machinery;
product liability rules and sale of consumer goods;
intellectual property;
labour law;
data privacy;
criminal law;
contractual and non contractual liability;
e-personhood.
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Intellectual property
Are machine intelligence generated works protectable under intellectual property regimes, and as the case may be, who is their owner?
Copyright law “Creative” or “original” work requirement Means work that reflect the subjective personality of the author Subjective, not objective: two similar songs can be © Can a machine that computes data be capable of creation?
Patent law needs “inventive step ” and “non obviousness” Inventive means unexpected, that does not follow path of technology progress Non obviousness requirement to the skilled person? Is anything non obvious to a super intelligence?
Who is the owner? PETA/Naruto case, before US Courts Not hypothetical given that some IPRs held by abstract legal persons like
corporations?
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Pros and cons
Avoid inconsistencies
Grant strong IP protection in AI field, yet create strict programmer liability in samefield
Speculation on problemscreated by technology underimperfect information
Risk of mistakes, that createnew legal problems
Social planner believes that AI research into biologicaltreatment of Alzheimer is next; creates strong IP; but techno frontier; mechanical (mindupload) would work better; unwanted problems
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2. Functional approach
Determine classes of MI applications, and then assess the legal needs from there.
Bottom up approach geared to technological evolution
Savile row tailors allegory
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Stanford, 2016
8 fields
1. Transport
2. Home/services robots
3. Healthcare
4. Education
5. Low-resources communities
6. Public safety and security
7. Employment and workplace
8. Entertainment
9 sujets juridico-politiques1. Privacy (biases in predictive
algorithm + right to intimacy)2. Innovation (open v patent thickets)3. Liability (civil)
1. Locus: efficiency/fairness2. Foreseeability condition
4. Liability (criminal)1. Intent condition (mens rea)
5. Agency (legal personhood)6. Certification and licensing
requirements7. Taxation (budgets dependent
payroll, income tax; speeding and parking tickets)
8. Labor (working contractsrequirements)
9. Politics
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Robolaw, 2012
Distinct legal issues for various class of applications Self driving cars: primary question relates to impact of liability
rules on manufacturers’ incentives for innovation
For prostheses, the focus is also placed on public law issues, for instance whether a person can identify itself with the prostheses it wears, and whether it can resist depiction in an official document with it, or not
For personal care robots, some basic human rights considerations come also into play, such as the need to protect the right to independent living protected by Article 19 of the UN Convention on the rights of persons with Disabilities: right to refuse robot assistance agst insurance companies?
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Pros and cons
More open to ex ante robo-ethics
Pro-innovation
Obsessive focus on not hindering technologicalevolution
Too much trust in technology success?
Technologicalconvergence will dissipatedifferences btwtechnologies
1.2. Regulatory trade-offs
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Disabling regulation (Pelkmans and Renda)
REACH
Problem with the “imposition of fairly heavy testing requirements for all existing and new substances alike”
“The other feature of REACH, owing to its ambitious precautionary approach of ‘no data, no market’ (access), is that this entire process of testing before being allowed on the market takes no less than 11 years”
GMO regulation
In the EU, only two new GMO products have been allowed to be cultivated: NK603 GM maize and the Amflora potato
This despite reported benefits to farmers and decrease in poverty
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Drones: Disabling regulation?
US FAA
Rules for small unmanedaircraftshttps://www.faa.gov/uas/media/Part_107_Summary.pdf
Standardize visual line of sight (VLOS) flights of unmanned aircraft that weigh less than 55 lbs. (25 kg)
Aeronautical knowledge test
Finland
“Allows BVLOS flights under certain conditions, and it does not require drone operators to possess an aerial work certificate”
https://techcrunch.com/2016/06/28/heres-whats-missing-from-the-new-drone-regulations/
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Civil purpose nuclearenergy
Reproductive cloning and nanotechnologies?
« Knee-jerk » regulation?
“tendency to overreact to risks, accidents and incidents” (Van Tol, 2011)
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Taxi v Uber
Airbn’B v hotel chains
E-cigarette
Rent seeking Bastiat: candle manufacturers
request chamber of deputies to: “pass a law requiring the closing of all windows, dormers, skylights, inside and outside shutters, curtains, casements, bull's-eyes, deadlights, and blinds—in short, all openings, holes, chinks, and fissures through which the light of the sun is wont to enter houses, to the detriment of the fair industries with which, we are proud to say, we have endowed the country [...]”.
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Rent seeking in AI
Taxi, truck and bus drivers
Delivery industry
Insurance companies
Carmakers
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Regulatory timing Collingridge dilemma: Too early to act, not enough
information; too late to act, all information but no longer able to change things
“Regulatory connection” quandary: the risks and opportunities created by emerging technologies cannot be “suitably understood until the technology further develops” … what if it is harmful?
“They're talking about spending 5-10 years to regulate technologies that are already 5-10 years old“ (Garreau)
Amazon, Intel and Google have been very vocal in relation to drones delivery regulation, which is outdated
Bostrom’s treacherous turn
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Enabling regulation (Pelkmans and Renda)
End-of-life vehicles “beyond what a market-based approach
might be expected to achieve” Quantitative targets: “reuse and
recycling of 80 % of the car weight in 2006, up to 85 % by 2015; reuse and recovery at least 85 % in 2006 and 95 % in 2015”
“Innovation takes place at the very beginning of the life cycle of cars, namely at the design & planning stage”
October 2016: Germany’s Bundesrat just passed a resolution to ban the internal combustion engine starting in 2030
Porter Hypothesis
In environment, safety and health, “tough standards trigger innovation and upgrading”
And market opportunities to race for first mover advantage
Counter-example: 2015 Volkwagen NOx (nitrogen oxides) emission scandal
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Regulatory trade offs
Complex relation between technological innovation and government regulation
“The economic literature (starting from the seminal work of Ashford and later with the so-called “Porter hypothesis”) has long recognised that regulation can be a powerful stimulus to innovation and entrepreneurship”(Pelkmans and Renda, 2014)
At the same time, regulation “can and does disable innovation” (Pelkmans and Renda, 2014)
2. Liability Issues (Civil)
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Goals of the lecture
Who should pay for robot generated harm?
How is the issue dealt with under basic legal structure?
Should regulation be adopted?
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Friends pay you a visit
Robot kills friends dog, which itconfuses with an insect
Deems it is a threat for herbalenvironment
Hypothetical scenario
You have a garden Buy a robot gardener Unmanned system with very
high autonomy Can « sense » maturation of
fruits, veggies, plants Robot has « actuators »
Turn irrigating devices « on » Prun the grass Kill mosquitos Carry and spill water,
pesticides and otherliquid/aerial products
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Social goals of liability law
Solution to be found so as to fulfill goals of liability law
Disputed G. Williams, « The Aims of the Law of Tort », 4 Current Legal
Problems, 1951
Corrective/protective Provide solvent target
Deterrence No gain from harmful conduct
Encourage precaution
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S. Lehman Wilzig, « Frankestein Unbound », 1981
Product liability Robot is piece of hardware Liability on producer, plus possible limited liability on importers, wholesalers and
retailers 2 manufacturers problem: « hardware » + « software » « Inherent risk »
Dangerous animals Strict liability only for dangerous species; no liability for « usually harmless species »
Slavery Several regimes: master is liable v slave is liable Roman law: master liable for civil claims, not criminal acts; possibility to eschew if
total absence of complicity What punishment against the bot?
Diminished capacity: independent persons, but not entirely intelligent Children: fully intelligent, but with low moral responsibility Agent Person
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Landscaping of default legal structure
Basic rules (not exclusive) Default liability/tort regimes: IL torts ordinance Litigation in court
Additional rules (not exclusive) Strict liability
Defective products liability (Directive 85/374/EEC on liability for defective products was adopted in 1985
IL: Defective Products Liability Law, 1980 (Defective Products Law). Consumer rights
Directive 2011/83/EC on Consumer Rights
IL: Consumer Protection Law, 1981 (Consumer Protection Law) and the Latent defects
Only for sales Duty of guidance
Only for contracts
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Liability of owner/keeper/user?
Liability of perpetrator?
Liability of manufacturer?
Framing the options
Who may/should pay for robot generated harm?
Classic imputability issue
Default Legal Structure
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Basic rules
Body of rules
Tort law
Vicarious liability law
Imputability
Perpetrator (one is liable for damages caused by his own acts)
Owner, holder, master (one hisliable for damages caused by others’ acts)
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Negligence
Duty of care
Omission that constitutes negligence
Breach of statutory duty
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Employer, corporation, State
But also: parents, masters, owners
And vicarious liability for animals, etc.
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Cumulative
Employer personally liable for employee harm (negligence) and vicariously liable (supervisor)
Employee personally liable and employer vicariously liable
Both are fault-based (+ or – negligence)
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AssessmentOwner/keeper/user imputability (vicarious) Protective of victim => solvent
target
Not necessarily apt to achievedeterrence purpose of liability law
Perverse effects on innovation incentives? => kills market for the purchase of robots?
Robot imputability (tort) Could achieve deterrence, for
robots and AIs can Make cost-benefit analysis Be taught some legal and moral
principles: bonus pater familias But no solvent target
Remedy problem Solvency issue: robots need
registration and to have property(capital endowment)
Transfer the robot to victim (but moral harm?)
Forced sale of the robot (but no market)
Insurance? Legal personhood threshold
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Assessment
Vicarious (owner) Tort (robot)
Provide solvent target Y N
Incentives N Y
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Conclusion
Early cases likely to seek liability of owner/keeper/user
On basis of vicarious liability
Other liability routes would need to passpersonhood threshold, for liability is contingent on third person’s fault
Not fully protective of victims, bc only one solvent target in liability for things!
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Additional rules: Product Liability Law
IL: Defective Products Liability Law, 1980 (Defective Products Law)
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Strict liability on manufacturers product was defective (deficiency or warnings/safety instructions insufficient)
=> undesired injury by normal use “only to bodily injuries and does not extend to indirect, consequential, or pure economic
damages” Limited defences
defect created when the product was no longer under the manufacturer’s control
“state-of-the-art’ defence”: manufacturer could not have known that the design of the product did not comply with reasonable safety standards
Product was released beyond the control of the manufacturer contrary to its desire
Damage: does “not take into account a level of earnings higher than three times the average earnings in Israel. The damages for pain and suffering pursuant to this law are limited. The remaining bases of claim generally do not provide for a maximum amount of liability”
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Assessment into context
Manufacturer liability
Liability without fault
Strict liability
Primary goal is corrective
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What the basic legal structure achieves
Multiplicity of potential regimes are applicable
No legal desert!
Most likely to take place under vicarious liability+defectiveproducts
Less likely to occur under tort liability
Liability both on supply and demand side!
3. Selected liability issues
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Ongoing questions
Immunity
Insurance
Standardization
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1. Immunity
Appeals to limit liability on manufacturers, as a way both to boost innovation in the robotic industry, by reducing the fears of liability-related costs (Calo, 2011)
Strict liability of owner, with cap: owner benefits from introduction of technology, and victim faces tough causality problem (Decker, 2014)
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Ryan Calo, « Open Robotics », 2011 Distinction btw closed and open robots
Closed robots are designed to perform a set task: // dishwasher
Open robots invite third party contribution: multifunction, open to all software, hardware modularity
According to Calo, « open robots » are more « generative » in terms of innovation
But, “open robotics may expose robotics platform manufacturers and distributors to legal liability for accidents in a far wider set of scenarios than closed robotics”
HYPO: Roomba vacuums up and kills an animal
Manufacturer liability if Roomba causes injury in its normal use
If product misuse – attempt to use Roomba as pet locomotion device –no manufacturer liability
With open robots: more applications + no product misuse defense for the robot is not designed to perform predetermined tasks
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Disincentive to investmentsin (open) robotics markets
“Early adopters of robotics are likely to be populations such as the elderly or disabled that need in-home assistance. Other early applications have involved helping autistic children. These populations would make understandably sympathetic plaintiffs in the event of litigation” (http://cyberlaw.stanford.edu/blog/2009/11/robotics-law-liability-personal-robots)
The problem
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Selective immunity
Immunizing manufacturers of open robotic platforms from damages lawsuits arising out of users’ implementation of robots, at least temporarily;
Precedent in aviation industry, crippled by litigation under PLL => General Aviation Revitalization Act (“GARA”) of 1994
// immunity enjoyed by firearms manufacturers and website operators
Websites not liable for users’ postings (re. defamation, for instance)
Selective immunity: “presumption against suit unless the plaintiff can show the problem was clearly related to the platform’s design”
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Insurance
Today, most car accidents are caused by human error
Progress in crash avoidance technology
Risks of accidents unlikely to be completely removed since events are not totally predictable, yet decrease (90%)
Disruption of the « crash economy » (RAND, 2014)
Today, variety of approaches, but imputability on driver: strict, no fault or negligence based liability
Today, compulsory insurance (EU directive)
Two issues:
Who’s liable? Shift in liability from user/owner/driver to manufacturer
EP: “The greater a robot's learning capability or autonomy is, the lower other parties' responsibility should be”
Compulsory v non compulsory insurance?
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Transfer?
European Parliament, Proposal, JURI: “Points out that a possible solution to the complexity of allocating responsibility for damage caused by increasingly autonomous robots could be an obligatory insurance scheme, as is already the case, for instance, with cars; notes, nevertheless, that unlike the insurance system for road traffic, where the insurance covers human acts and failures, an insurance system for robotics could be based on the obligation of the producer to take out an insurance for the autonomous robots it produces”
Compulsory insurance?
Still needed? Maybe, but coverage of losses caused by crashes is likely to be less expensive
Coverage of losses not caused by crashes but by wind, floods and other natural elements and by theft (comprehensive coverage) is less likely to change, yet price will decrease bc cost of repair offset by lower accidents (see http://www.iii.org/issue-update/self-driving-cars-and-insurance)
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Insurance
Nothing changes: no-fault form, in which neither party is at fault, and each car owner’s insurance covers their own vehicle
Prices should go down!
http://www.iii.org/issue-update/self-driving-cars-and-insurance
Changes:
Manufacturer insurance (Volvo)
Shared insurance?
Utility cost with a premium cost based on mileage or usage
Leasing – ridesharing model
Risks involving driverless-car hacks and cybersecurity
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Standardization
Private ordering mechanisms International Standardization organs (ISO, IEC)
ISO/TS 15066:2016(en), Robots and robotic devices — Collaborative robots
US and EU organizations (IEEE, CEN, CENELEC)
Many EU rules on machinery and product safety Example: Directive 2006/42/EC of the European
Parliament and the Council of 17 May 2006 on machinery
4. Should Someone else Pay?
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Coase Theorem HYPO
A uses robot gardener at night, when water and electricitycost less
Neighbour B moves in, creates a boutique hotel With noise at night, B’s clients flee in droves Who should be liable for injury? Standard legal solution: A liable to compensate externality But at the same time, it is B that creates harm if A is
forbidden to use its robot gardener simply bc the former moved
Coase argues that who is liable is to some extent irrelevant: as long as no transaction costs and property rights welldefined, parties will bargain the efficient solution
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This is only possible absent transaction costs, ie when it is easy to negotiate
Often not the case in the real world A is a cooperative of multiple owners,
who are never present, for theiragricultural exploitation is fullyautomated: search costs and negotiationcosts
If B liable, bargaining for €5,000 is not possible. B would contemplate installingdouble glazing: €10,000 loss for society
Not the cheapest cost solution When there are transaction costs, law
should assign liability so as to achieve the cheapest cost solution that would have been found in negotiation
A is liable Negative externality inflicted by
regulatory system should be as littleefficiency harmful as possible
Cheapest cost solution principle Options
A sends robot for mechanicalupdate so it makes less noise: 5,000€
B installs double glazing: extra €15,000.00
Solution Efficient social solution is that
robot gardener is retooled This happens regardless of liability
assignement If town hall assigns B right to
silence, A will pay 5,000€ to retoolbot
If town hall assigns A right to noise, B will pay to A 5,000€ to retool bot
In both cases, the efficient solution is followed, regardless of who isliable
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Application to robotics
Invites to research what is the cheapest cost solution
Not one obvious culprit, avoid moral bias
A, B or someone else?
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Liability on the creator/programmer?
Robot manufacturer to encode ex ante robot prohibition to operate at night?
One line of code: 1€?
If all the Bs of this world could negotiate freely, they wouldcontact all robot gardener producers and ask them to encode thisprohibition
Not possible
Legal system to hold robot producers liable if noise harm at night
But bots would be less valuable for buyers, and price system wouldcorrect this?
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References
R. Coase, “The Problem of Social Cost”, 3 J. Law & Econ. [1960]
R. Coase, “The Federal Communications Commission”, 2 J. Law & Econ. [1959]
Class III: Robotic Warfare; Market Regulation; Regulatory Framework
1. Robotic Warfare
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Goals of the lecture
Can we delegate human killing decision to an autonomous machine?
Yes, no, maybe?
What, if any, conditions should the law set?
Technology
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Technology
Today Robots (with limited autonomy)
are already deployed on the battlefield in areas such as bomb disposal, mine clearance and antimissile systems
No full autonomy, but increasing at rapid pace
30 nations with defensivehuman-supervised autonomousweapons to defend against surprise attacks from incomingmissiles and rockets: IronDome, Phalanx CWIS, etc.
Tomorrow? Lethal Autonomous Weapons
Systems (“LAWS”) aka “robot killers”
Robots that can “select and engage targets without further intervention by a human operator” (US Directive)
“Kalashnikovs of tomorrow”: Unlike nuclear weapons, mass production, easy proliferation and swift circulation => SGR-A1 costs approx. 200,000,00€
Like nuclear weapons, risk of “a military AI arms race”
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UCAVS SWORDS
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Sentinels Swarms
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Typology
Man Machine
Humans in the loop(essential operator)
Non autonomous
Humans on the loop (failsafe)
Partially autonomous
Humans out of the loop (seekand destroy)
Fully autonomous
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Psychological disconnect and self justice (« Good Kill » movie)
Mistakes in visual recognition: identifying someone as a combatant 1960, U.S. missile attack warning
system at NORAD, where an alert was received saying that the United States was under massive attack by 99 Soviet missiles, 99,9% certainty; amateur astronomer: “It’s a beautiful night! There’s a big full moon right in sector three. And I can even see icebergs down in the fjord.”
Hate by design?
Prospects for warfare
Clean war No casualties: explosive detection
bots
No war crimes on the battlefield and outside
Fast war
Economic
Cuts in budget of armedforces, including retirement and public health
R&D
Legal issues
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Legal issues
Standard Since 2004, US program to
search and kill al Qaeda and Taliban commanders Used in Lybia, Pakistan,
Afghanistan, Syria 117 Drone killings in 2010, see
http://www.longwarjournal.org/pakistan-strikes/
In 2013, China flew a drone into contested airspace in the East China Sea. Japan reciprocated by sending a manned fighter aircraft
Act of war? IAC, NIAC?
Ethical Shall a machine be granted a license
to kill without human input?
Are there decisions computers shallnot make without human input?
Need killswitch?
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Ban on LAWs
Human Rights’ Watch: https://www.hrw.org/report/2012/11/19/losing-humanity/case-against-killer-robots
Report of the Special Rapporteur on Extrajudicial, Summary or Arbitrary Executions, 20–21, Human Rights Council, U.N. Doc. A/HRC/23/47 (Apr. 9, 2013)
United Nations held a further round of talks in Geneva between 94 military powers aiming to draw up an international agreement restricting their use
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Today
https://www.stopkillerrobots.org/2016/04/thirdmtg/
Algeria, Bolivia, Chile, Costa Rica, Cuba, Ecuador, Egypt, Ghana, Holy See, Mexico, Nicaragua, Pakistan, State of Palestine, and Zimbabwe
Convention on Conventional Weapons (CCW) Review Conference in December will decide if they will hold a Group of Governmental Experts (GGE) meeting on autonomous weapons systems in 2017
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Outright ban proponents
Moral argument, giving robots the agency to kill humans would cross red line Reduce killing decision to cost-benefit Deontological self limitation in human killing
decision: empathy Duty of human judgment in killing decision, bc
justice hinges on human reasoning (Asaro, 2012) Slippery slope
Decrease the threshold of war Prospect of a generalization of warfare
Existential risk
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Ban skeptics If humanity persists in entering into warfare, which is reasonable assumption,
need to better protect non fighter lives
Automation increases human control: “ In situations where the machine can perform the task with greater reliability or precision than a person, this can actually increase human control over the final outcome. For example in a household thermostat, by delegating the task of turning on and off heat and air conditioning, humans improve their control over the outcome: the temperature in the home”.
Robots are conservative warriors: do not try to protect themselves, in particular in case of low certainty of identification + judgment not clouded by anger or fear
Robots will not replace humans: organic assets like dogs, etc.
“Law as code”: design ex ante LAWs constraints like Watt and the heating machines, upper bound on RPM (upper bound on laser wattage, for instance)
« Against statu quo », pro « moratorium » and « regulate instead of prohibiting thementirely », (Arkin, 2016)
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Rent seeking?
UK position driven by development of Taranis drone
Scientists driven by vested researchinterest? General Leslie Groves (cited in Levy, 2006): “What happened is what I expected, that after they had this extreme freedom for about six months their feet began to itch, and as you know, almost every one of them has come back into government research, because it was just too exciting”
Critical review
Knee-jerk regulation? We already outsource, to specialist
killers that we do not know and over whom we have little control
We are faced with a possiblytransitional question, shall not obscure the possibility of machine to machine war where 0 humancasualties becomes possible
Lethal weaponry already exists. LAW simply makes it accurate: weaponswith 100% success rate (consider« HRW position on Human Rights Watch’s position on the use of precision-guided munitions in urbansettings—a moral imperative »)
Counterfactual issue: existing world is not clean war, but dozens of hidden war crimes
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Outstanding issues (Anderson and Waxman, 2012) Empirical skepticism: can we trust technology to design
safeguards?
Deontological imperative: do we want to take the human « out of the firing loop »?
Accountability: who takes the blame (incl. costs) for war crimes?
Not a yes/no question, but how to regulate?
Default Legal Structure
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Laws of war
Hague Conventions (and regulations) of 1899 and 1907 Convention (II) with Respect to the Laws and Customs
of War on Land and its annex: Regulations concerning the Laws and Customs of War on Land
Mostly about combatants
Provisions on warfare deemed to “contain rules of customary international law”
Article 51 of the UN Charter provides right of self-defence in case of armed attack
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Humanitarian law
In particular, Convention (IV) relative to the Protection of Civilian Persons in Time of War. Geneva, 12 August 1949
Mostly related to civilians protection
Protocol Additional (Protocol I), and relating to the Protection of Victims of International ArmedConflicts, 8 June 1977
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Disarmament law (1)
Convention on Certain Conventional Weapons(CCW) and protocols Under UN Aegis
Compliance mechanism
Since 2013, expert meeting on LAWs
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All prohibitions or restrictions on the use of specific weapons or weapon systems Protocol I on Non-Detectable
Fragments
Protocol II on Prohibitions or Restrictions on the Use of Mines, Booby Traps and Other Devices
Protocol III on Prohibitions or Restrictions on the Use of Incendiary Weapons, etc.
Protocol IV on Blinding Laser Weapons
Protocol V on Explosive Remnants of War
Disarmament law (2)
CCW: “chapeau” convention with general provisions (1980 with 2001 amendment), including scope Article 1 common to the Geneva
Conventions of 12 August 1949. Refers to Article 2 of Geneva
Conventions of 12 August 1949 for the Protection of War Victims: “cases of declared war or of any other armed conflict which may arise between two or more of the High Contracting Parties, even if the state of war is not recognized by one of them.The Convention shall also apply to all cases of partial or total occupation of the territory of a High Contracting Party, even if the said occupation meets with no armed resistance”
Amended in 2001 to cover also “armed conflicts not of an international character occurring in the territory of one of the High Contracting Parties”
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State of discussion
Ban skeptics Those are process, R&D
questions, which ought to beadressed at design level
Not a trial and error question: “significant national investment into R&D already undertaken that will be hard to write off on ethical or legal grounds; and national prestige might be in play” (Anderson and Waxman, 2012)
Ban proponents LAWs violate all provisions of
Geneva conventions designed to protect civilians (HRW allegation: “robots with complete autonomy would be incapable of meeting international humanitarianlaw standards”)
This justifies a new protocolunder CCW to ban all LAWs
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#1. Duty of review Protocol I, Article 36 – “New Weapons”:
“In the study, development, acquisition or adoption of a new weapon, means or method of warfare, a High Contracting Party is under an obligation to determine whether its employment would, in some or all circumstances, be prohibited by this Protocol or by any other rule of international law applicable to the High Contracting Party”
Protocol I, Article 84 – “Rules of Application” “The High Contracting Parties shall communicate to one another, as soon as possible,
through the depositary and, as appropriate, through the Protecting Powers, their official translations of this Protocol, as well as the laws and regulations which they may adopt to ensure its application”
See also Protocol I, Article 35 – “Basic rules”: “1. In any armed conflict, the right of the Parties to the conflict to choose methods or
means of warfare is not unlimited. 2. It is prohibited to employ weapons, projectiles and material and methods of warfare of a nature to cause superfluous injury or unnecessary suffering. 3. It is prohibited to employ methods or means of warfare which are intended, or may be expected, to cause widespread, long-term and severe damage to the natural environment”
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Discussion
On producer and customer States
Conflict of interest?
Home industry
Public subsidies to defense R&D
All signatory States shall apply
Some States have set up formal review (BE), others not
But US is not party to Protocol 1;
Some contend that Article 36 is customary international law
Components and final products?
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HYPO: weaponization scenario
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HRW report, p.31:“a frightened mother may run after her two children and yell at them to stop playing with toy guns near a soldier. A human soldier could identify with the mother’s fear and the children’s game and thus recognize their intentions as harmless, while a fully autonomous weapon might see only a person running toward it and twoarmed individuals” => Visual recognition requires a subjective understanding of intention
“Legal threshold has always depended in part upon technology as well as intendeduse” (A&W, 2012)
#2. Distinction requirement
Article 51(4) Protocol n°1:
“Indiscriminate attacks are prohibited. Indiscriminate attacks are: (a) those which are not directed at a specific military objective; (b) those which employ a method or means of combat which cannot be directed at a specific military objective; or(c) those which employ a method or means of combat the effects of which cannot be limited as required by this Protocol; and consequently, in each such case, are of a nature to strike military objectives and civilians or civilian objects without distinction”
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HRW report, p.33: “A fully autonomous aircraft identifies an emerging leadership target” Pb 1: “if the target were in a city, the
situation would be constantly changing and thus potentially overwhelming”
Pb 2: “weigh the anticipated advantages of attacking the leader”, which may depend on the political context
Rules out systems that “aim at other weapons” + “ethical issue of attachingweights to the variables at stake” (A&W, 2012)
#3. Proportionality principle
Article 51(5) b):
“an attack which may be expected to cause incidental loss of civilian life, injury to civilians, damage to civilian objects, or a combination thereof, which would be excessive in relation to the concrete and direct military advantage anticipated”
Civilian harm shall not outweigh military benefits
Ex ante balancing of civilian and military harm is required
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Khrisnan, 2009 Development of “[t]echnology
can largely affect the calculation of military necessity”; and
“Once [autonomous weapons] are widely introduced, it becomes a matter of military necessity to use them, as they could prove far superior to any other type of weapon”
Who decides if political or military necessity (persuadingthe ennemy to surrender)
#4. “Military necessity” rule (or defense)
Customary principle of humanitarian law
Lethal force only for the explicit purpose of defeatingan ennemy
Only to the extent of winning the war
Respect other rules of IHL: No attack on wounded or surrendering troops
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#5. Martens clause
Article 1(2) of Protocol 1
“In cases not covered by this Protocol or by other international agreements, civilians and combatants remain under the protection and authority of the principles of international law derived from established custom, from the principles of humanity and from dictates of public conscience”
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Reality check?
Robots may not comply with default legal structure, but do humans?
Pentagon Intelligence, Surveillance, and Reconnaissance (ISR) Task Force: standard for drone strikes is not “no civilian casualties,” only that it must be a “low” collateral damage estimate
More at https://theintercept.com/drone-papers/the-assassination-complex/
Upgrading of Default Legal Structure?
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CCW discussions
Wide attendance
Discussion is whether autonomous systems are acceptable
But “neither side has managed to construct a coherent definition for autonomous weapon systems for the purpose of a weapons ban” (Crootof, 2014)
Most States believe that autonomous is ok as long as there is “meaningful human control” (GER: “LAW system without any human control is not in line with our command and control requirements”)
Ban supporters:
Cuba and Ecuador
Ban opponents:
British say existing international humanitarian law (IHL) is “the appropriate paradigm for discussion”, supported by Czechs
Programming is enough
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Options
Stationarity requirements
Only for defensive purposes
Only non human targets
Only non lethal measures
Only in certain areas: high sea v urban areas
Crootof, 2014
Supports intentional, proactive regulation
“An independent treaty might take one of three forms: it might attempt comprehensive regulation (like the Chemical Weapons Convention—which, in addition to banning the development, production, acquisition, stockpiling, retention, transfer, and use of certain defined chemical weapons, also outlines enforcement mechanisms), provide piecemeal regulations of specific activities (like the Nuclear Test Ban or nonproliferation treaties), or serve as a framework treaty intended to be augmented by later protocols (like the CCW itself). All of these have associated benefits and drawbacks”
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Arkin, 2009
Ron arkin has proposed an ethical code, designed to ensure compliance
« Ethical governor »
First step: LAW must evaluate the information it senses and determine whether an attack is prohibited under international humanitarian law and the rules of engagement
Second step: LAW must assess the attack under the proportionality test. According to Arkin, “the robot can fire only if it finds the attack ‘satisfies all ethical constraints and minimizes collateral damage in relation to the military necessity of the target’”
Report of California Polytechnic State University of San Luis Obispo considerthat robot ethical morality is insufficient in complex environments
Other approaches?
Slavery ethics
Self learning and strong AI (McGinnis): highly desirable, but unattainable
Conclusions
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Liability v warfare
Robotic liability
Social desirability of technology(almost) unquestioned
Debate is immunity or not
Focus on default legal structure, and possible ex post adjustmentto the law
National discussion
Possibly because essentiallydiscrete harm issues
Robotic warfare
Social desirability of technologychallenged
Debate is ban or not
On all sides, voices calling for new rules, and ex ante regulation
Robo-ethics driven, « law as code »
International discussion
Possibly because of strongersystemic and existential risk
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References See generally:
http://www.unog.ch/80256EE600585943/(httpPages)/8FA3C2562A60FF81C1257CE600393DF6?OpenDocument
Ronald Arkin, The Case for Banning Killer Robots: Counterpoint, Communications of the ACM, Vol. 58 No. 12, Pages 46-47
Kenneth Anderson and Matthew Waxman, Law and Ethics for Robot Soldiers, 2012
David Levy, Robots Unlimited, A K Peters, Ltd., 2006
Michael C. Horowitz & Paul Scharre, Meaningful Human Control in Weapon Systems: A Primer (Mar. 2015)
Peter Asaro, On banning autonomous weapon systems, International Review of the Red Cross, 2012
Rebecca Crootof, The Killer Robots are Here, Cardozo Law Review, 2015
Markus Wagner, “Taking Humans Out of the Loop: Implications for International Humanitarian Law,” 21 Journal of Law, Information and Science (2011)
2. Market conduct
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Competition policy
Goals
Allocative efficiency
Productive efficiency
Dynamic efficiency
Tools
Prohibition of collusion
Prohibition of abuse of dominance
Prohibition of mergers to monopoly, and others
Israel Antitrust Authority (IAA)
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Perfect competition, 3.0?
Increased transparency Lower search costs: PCWs and aggregators Entry and Expansion
Platforms as enablers, and the midget disruptors Demotion of brick and mortar behemoths
AMZN v Walmart AMZN v GAP AMZN v Publishers
Matching supply and offer Sharing economy, and underutilized assets The long tail
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Predominance of data-hungry business models
Search for data advantage
Offline players join the fray, and search for smart pricing algorithms
Use of personal assistants to make decisions for us
Emergence
Dynamic pricing Use of pricing algorithms
(Lawrence book, Making of a Fly, $23,698,655,93)
Personalized pricing Octo’s insurance quotes
based on drivers’ behavior
Data explosion Cloud computing
IoT
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Ezrachi and Stucke, 2016
« Façade of competition »
Cost of free: « Data as Currency »
From invisible hand, to « digitized hand »
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Collusion
Easy cases « Messenger scenario »: rival
executives collude, and defer to their algorithms to calculate, implement and police the cartel Evidence of horizontal agreement
+ liability: easy « Hub and Spoke »: rival do not
interact, but outsource the pricingdecision to an upstream supplier algorithm Boomerang Commerce Uber Evidence of vertical agreements,
and // conduct, and cumulative effect + liability: quite easy
Tough cases « Predictable agent »
All firms in industry use samepricing algorithm
Used to monitor each other’s listprices, and increase whensustainable
Instant detection => consciousparallelism
« God view and the Digital Eye » Each firm can see entire economy
on giant screen Algorithm not programme to
increase prices, just profit maximizer
Tacit collusion on steroids
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Almost perfect, behavioraldiscrimination Groups of customers
Decoys AAPL watch: $349 to $17,000
Price steering
Drip pricing
Complexity
Imperfect willpower
Behavioral discrimination
Perfect price discrimination
Geographic, demographic, and other situationalinformation
Prices, coupons and vouchers,
Target scandal
Cherry pickers avoidance
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Frenemies
Superplatforms-superplatforms: friends and foes
GOOG v AMZN v FB v AAPL v MSFT
GOOG Android supports 90% of AAPL’s APIs
Superplatforms v Independent Apps
Uber v GOOG and AAPL?
Superplatforms with Independent Apps
Extraction => cooperation during cookie and data identification techplacement
Capture => uneven cut, GOOG 32%
Superplatforms with and v Independent Apps
Brightest flashlight android app
Disconnect
Personal assistants
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Remedies
UK style market investigations
Putting a price on free
Privacy by default remedies
Possible regulation, beyond antitrust
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Bottom lines for competition law
Some strategies don’t raise market failures in antitrust sense
Personal assistants
Some generate classic problems for antitrust, nothing new under the sun
Frenemies
Some invite thinking on goals of competition law
Behavioral discrimination?
Some invite thinking on gaps in competition law
Predictable Agent and God View
Behavioral discrimination
Some may create enforcement difficulties
Tacit collusion: no liability on algorithm
Detection problem
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Competition engineers Antitrust Hacker
Antitrust Standardizer
Antitrust « Digital Half »
Antitrust Shamer
Reinventing enforcement agencies?
Competition doctors Standard mission of agencies
is to remove antitrust infringements from markets
Deterrence, specific and general: carried out ex post with fines
Remediation for the future Behavioral and structural
remedies
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#1: Antitrust Hacker
Scenario Agencies to build programs and
give away software that counteractsvirtual competition
Agency to cooperate withcomputer scientists that buildsoftware so as to technologicallyundermine effectiveness of abovementioned strategies
Software then made widelyavailable to customers and rivalswilling to avail themselves of competitive options
Prospects for business and techcommunities
Interface with consumer agencies
Applications Anti-decoy filters that eliminate
false options Additive data perturbation
software => runs in the back of users’ sessions and visitsrandom websites => noise
Anti-steering filters Policy checking privacy
enhancing tools Anticomplexity software
Clearware.org refine consent content and present it in a more human readable format
Same with pricing?
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De facto standards are mostlikely dominant platformoperators
Impose on gateway players to make it possible for users to define their own level of acceptance for all new software
“Users’ browsers only accept interaction with Web servers whose privacy-preferences correspond to their own local preferences” (Boldt, 2007)
Problem of disconnect betweendominance (platform) and abuse (spyware firm)
#2: Antitrust Standardizer
Agencies to promote ex ante specification of Privacy non-intensive pricing
algorithms Privacy Enhancing Technologies
(identity verification with minimum identity disclosure, etc.)
Antitrust compliance in AI : individually rational v sociallyharmful (Dolmans) + Article 22 GDPR
Advocate introduction of antitrust « standards » withStandard Setting Organizationsand/or de facto standards IETF, IEEE-SA, ISO, etc. « Dominant » platforms: OS,
handsets, browsers and searchengines
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#3: Antitrust Shamer
Instant antitrust popup that warns of systematicbehavioral discrimination on website
Instant antitrust popup that suggests user to disconnector use alternative browser (Tor)
Permanent and updated antitrust list of privacy-intensive websites
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#4: Antitrust « Digital Half »
Scenario « Digital half » of the competition
agency (P. Domingos)
Hidden, anonymous or pseudonymous
Tacit collusion: stealthremediation, agency acts as a maverick, post low prices to trigger price war
Behavioral discrimination: agencymonitors customers on platformsand instantly informs high pricecustomers that other low pricecustomers pay less
Discussion
Pros: possiblity to catch infringements « red-handed »
Cons: due process? Not possible to remedy withoutinfringement, simply monitor
But interim measures? Article 8 R1/2003
Yet, interim measures on infringing firms
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Challenges
Conceptual
Privacy as an antitrust problem: quality competition?
Privacy as a market failure: mistrust causes deadweight loss?
Government as spy in market?
Instrumental
Change the law on behavioraldiscrimination (US v EU)?
Change the law on tacitcollusion (US&EU)?
Remedy without a cause (no infringement)?
Cat and mouse game wheremarket always ahead of agency?
3. Alternative Consequentialist Framework
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Proposed framework
Public interest, beyond market failures
Utilitarian
Externality
Negative Discrete Systemic
Positive Discrete Systemic
Existential
Existernality
Negative Terminator
Positive La formule de dieu
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Framework
Discrete externality (personal; random; rare; endurable)
Negative: harm
Positive: benefits
Systemic externality (local; predictable; frequent; unsustainable)
Negative Substitution effect
Privacy
Positive Complementarity effect
Generative or General Purpose technologies (Zitrain, Bresnahan)
« Existernality » (global; fat tail; terminal)
Negative: existential risk
Positive: pure human enhancement
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Illustration of the framework (Drones)
Discrete externality
A drone crashes on the ceiling of a house, while delivering
Transports an explosive product
Burns the house
Systemic externality
A drone operated delivery system puts employment in the mail industry at risk
Existentialist threat
Drone designed for war
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Discrete externalities
Litigation
Basic legal infrastructure and case-by-case resolution
Decisional experimentation, with fitting exercize
Regulation
Experimentation “Tokku” Special Zone for
Robotics Empirical Testing and Development (RT special zone) from open environments => Test human-robot interface in limited areas => companies entitled to less strict legal standard http://www.economist.com/blogs/banyan/2014/03/economic-zones-japan
Regulatory emulation as States liberalize driverless cars
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Systemic externalities
If discrete externalities become widespread or harmful
Scope for new regulation? Negative externalities
• Tax on robotic-intensive industries
• Private entitlement of rights: laws on privacy
• Safety standards to solve collective action problem
• Mandatory insurance or electronic personhood for robots (Leroux et al.)
Positive externalities• Subsidies for public goods issues: building of controlled environment
infrastructures for driverless cars
• Proactive IPR policy for innovation into robotics technologies
• Immunity from liability for research on certain systemic applications? GARA precedent
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Existernalities
Calls for legal bans on specific applications UN Campaign to stop killer robots:
https://www.stopkillerrobots.org/category/un/
Technical resolution of issues Philosophers: « Creating friendly AIs 1.0 » (Yudkowsky, 2001)
Technologists: Keeping open and competitive technology, https://openai.com/blog/introducing-openai/
Liege Competition and Innovation Institute (LCII)University of Liege (ULg)
Quartier Agora | Place des Orateurs, 1, Bât. B 33, 4000 Liege, BELGIUM
Thank you