Putting Humans in the Loop: Human Computation for Natural

Resources Management

New Developments in IT & WaterAmsterdam, Nov 5 2012

Piero Fraternali

Politecnico di Milano, Italy

piero.fraternali@polimi.it

Outline

• Human Computation– Origins– Forms

• Crowdsourcing• Games with a purpose• Solution space exploration • Social Mobilization• Examples in Natural Resource & Water Management

– Open Issues– Research Projects– Conclusions and outlook

Human Computation: a definition

• According to Von Ahn• Combine humans and computers to solve large-scale problems

that neither can solve alone taking advantage of the human cycles

• According to Wikipedia:• Human-based computation is a computer science technique in

which a computational process performs its function by outsourcing certain steps to humans. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human-computer interaction.

Early example: CAPTCHA• Stands for “Completely Automated Public

Turing test to tell Computers and Humans Apart”

• Luis von Ahn et al. coined the term in 2000• A Program that can tell

whether a user is a human or a computer

• Humans and machineshave complementaryskills

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The disciplines of HC

Forms of HC: crowdsourcing• Crowdsourcing is a distributed model that

assigns tasks traditionally undertaken by employees or contractors to an undefined crowd

– Split the task into micro-tasks– Assign them to performers in the crowd– Collect partial results into the final one

Paid Crowdsourcing: Amazon Mechanical Turk

Forms of HC: GWAPS• Games with a Purpose (GWAPs)

– Exploiting the billions of hours that people spend online playing with computer games to solve complex problems that involve human intelligence [vA06,LvA09].

– Useful tasks are embedded in a playful experience where human judgment is exploited consciously or unconsciously

Types of Games[Luis von Ahn and Laura Dabbish, CACM 2008]

Three generic game structures

• Output agreement: – Type same output

• Input agreement: – Decide if having same input

• Inversion problem: – P1 generates output from input– P2 looks at P1-output and guesses P1-input

Output Agreement: ESP Game• Players look at common input• Need to agree on output

Input Agreement: TagATune• Sometimes difficult to type identical output

(e.g., “describe this song”)• Show same or different input, let users

describe, ask players if they have same input

Inversion Problem: Peekaboom

• Non-symmetric players• Input: Image with word• Player 1 slowly reveals pic• Player 2 tries to guess word

Sketchness• Puzzle Game, Guess and

Draw (Pictionary, iSketch…)

• Players take turns drawing the shapes of objects inside an image to make the other players guess the object

• Two roles: Sketcher & Guesser

• Objectives: Object detection, garment segmentation and tagging

Forms of HC: space exploration

• Combinatorial problems with intractable solutions spaces, in which humans can help the heuristic core in pruning– Protein folding: Proteins fold

from long chains into small balls, each in a very specific shape

– Shape is the lower-energy setting, which the most stable

– Fold shape is very important to understand interactions with out molecules

– Extremely expensive computationally! (too many degrees of freedom)

• A Mason-Pfizer monkey virus retroviral protease was modeled by FoldIT gamers in just three weeks

Forms of HC: social mobilization

• Social Mobilization– Problems with time constraints, where the

efficiency of task spreading and of solution finding is essential

– An example of the problem and of the techniques employed to face it is the Darpa Network Challenge [PRP+10]

– The solution comes from the nature of the reward mechanism and social ties of humans

HC & Natural Resource Management

• Objectives– Collect and validate data– Extract information from data– Involve people in resource usage planning and management– Change people’s behavior

• Approaches– Passive: mine information from existing user’s activity traces– Active: engage people in ad hoc tasks

• Ultimate goals – Obtain “better data” for predictive models, planning and

management tool: more accurate, at finer time/space resolution, in real time …

– Take “better decisions”: more participative, less conflicting, capable of promoting social change

Monitoring waterways: CreekWatch

• Problem: obtain simple yet useful parameters on water shed conditions in a vast territory at low cost

• Solution: geo localized mobile+Web application – Developed at IBM Research Almaden, 4000+ users, 25 countries– The city of San Jose, CA, uses it to prioritize pollution cleanup efforts

• Collected data are found to have good quality

Predicting population dynamics with twitter data

• Problem: obtaining impact of population on territory at high temporal resolution

• Can be used to detect events, estimate water consumption bursts, waste production, etc

• Solution: using low cost geo-localized data sources (e.g., tweets) together with structured and high cost sources (e.g., mobile phone traces)

http://www.streamreasoning.org/demos/london2012

Predicting snow level with Flickr images

• Problem: predicting the incidence of natural phenomena using user generated content

• Solution: using Flickr photos tagged with “snow” to estimate snow fall (precision 100% with 7 snow photos)– H Zhang, M Korayem, DJ Crandall, G LeBuhn: Mining

photo-sharing websites to study ecological phenomena. WWW 2012

Using social deliberation tools for partipatory planning

• Problem: letting a large crowd of citizens propose solutions or deliberate on proposals about public goods

• Solution: large scale deliberation and idea management tools– IdeaScale.com,

MIT’s Deliberatorium…

Open problems

• Humans, like machines, can make errors– Cognitive bias, fatigue

• Unlike machines humans can cheat– Classification of attacks– Spammer detection

• Quality of output improvement techniques are in use

• Voting schemes• Workers quality modeling and vote weighing (requires

ground truth or machine learning models and iterative / selective labeling of data)

• Micro-flows, worker’s pre-task testing• Task to worker assignment, active learning

Example of ongoing projects

Politecnico di Milano

The CrowdSearcher crowd engagement framework

Human task design:Tips on workplaces from friends

Human task execution with Facebook & Doodle

CUbRIK Project

• FP7 Integrating Project• Goals:

– Advance the architecture of multimedia search

– Exploit the human contribution in multimedia search

– Use open-source components provided by the community

– Start up a search business ecosystem

• http://www.cubrikproject.eu/

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Multimedia processing with crowd

Detecting logo images in videos

Experimental evaluation

• Three experimental settings:–No human intervention–Logo validation performed by domain experts–Non-expert crowd on FaceBook

• Experiment size–40 people involved–50 task instances generated–70 collected answers

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Experimental evaluation30

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Reasons for the wrong inclusion• Geographical location of the

users• Expertise of the involved users

Experimental evaluation32

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Precision decreases• Similarity between two

logos in the data set

Future directions & outlook

• Find problems where crowd support can be useful, e.g., – Urban water demand prediction: smarter meters are

costly and not deployed. Household data can be used to build models

• Design crowd interaction– Non only IT: engagement, incentives, ethical and legal

issues• Collect and clean-up data• Integrate crowd model and data with (e.g.,

water) system models• Check validity

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References• Managing Crowdsourced Human Computation, Panos

Ipeirotis, New York University Praveen Paritosh, Google

• [LvA09] Edith Law and Luis von Ahn. Input-agreement: a new mechanism for collecting data using human computation games. In Proc. CHI 2009, 2009.

• [vA06] Luis von Ahn. Games with a purpose. Computer, 39:92{94, 2006.

• [vAMM+08] Luis von Ahn, Ben Maurer, Colin McMillen, David Abraham, and Manuel Blum. recaptcha: Human-based character recognition via web security measures. Science, 321(5895):1465~1468, 2008.[

References• Galen Pickard, Iyad Rahwan, Wei Pan, Manuel Cebrian, Riley

Crane, Anmol Madan, and Alex Pentland. Time critical social mobilization: The darpa network challenge winning strategy. CoRR, abs/1008.3172, 2010.

• Trant J., Exploring the potential for social tagging and folksonomy in art museums: proof of concept. New Rev. Hypermed. Multimed. 12(1), 83–105

• Firas Khatib et al, Crystal structure of a monomeric retroviral protease solved by protein folding game players, NATURE, 2011

• S. Kim, C. Robson, T. Zimmerman, J. Pierce, and E. M. Haber. Creek watch: pairing usefulness and usability for successful citizen science. In Proceedings of the 29th Int Conf on Human Factors in Computing Systems, pages 2125–2134, New York, NY, 2011.