ai andthe future of free journalism

8/14/2019 AI andThe Future of Free Journalism

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The Future of Journalism:

Artificial Intelligence

And Digital Identities

Noam Lemelshtrich Latar

Sammy Ofer School of CommunicationsIDC Herzliya

Israel

David Nordfors

Stanford Center for Innovation and Communication

Stanford University

Feb 2011


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Table of Contents1 INTRODUCTION

32 DEFINING JOURNALISM IN THE DIGITAL AGE 73 ESTABLISHING THE DNA OF JOURNALISTIC CONTENT 93.1 CONTENT BASED IMAGE RETRIEVAL (CBIR) .................................................... 10

3.2 VIDEO INFORMATION RETRIEVAL ...................................................................... 11

3.3 HUMAN CENTERED CONTENT ANALYSIS ......................................................... 11

3.4 THE DNA OF LITERATURE ............................................................................ 12

4 JOURNALISM CONTENT AND CONSUMER ENGAGEMENT 124.1 THE CONCEPT OF MEDIA ENGAGEMENT .......................................................... 12

4.2 BEHAVIORAL TARGETING AND JOURNALISTIC CONTENT .................................... 15

4.3 BEHAVIORAL TARGETING IN SOCIALNETWORKS ............................................... 15

4.4 PROJECT SMART PUSH ................................................................................ 16

5 AI: DIGITAL IDENTITIES AND BEHAVIORAL TARGETING ENGINE17

5.1 MANAGING DIGITAL IDENTITIES DEVELOPING A UNIVERSAL STANDARD .......... 17

5.2 DIGITAL IDENTITIES AND SOCIALNETWORKS .................................................... 18

5.3 SOCIO-GENETICS AND DIGITAL IDENTITY ....................................................... 19

5.4 BEHAVIORAL TARGETING AI ENGINE BASED ON JOURNALISTIC CONTENT AND

CONSUMER DIGITAL IDENTITY ................................................................................ 20

6 DIGITAL IDENTITIES AND WEBLINING217 DIGITAL IDENTITIES AND THE PRACTICE OF JOURNALISM228 PRINCIPLES OF JOURNALISM AND DIGITAL IDENTITIES248.1 PRINCIPLES FOR USING DIGITAL IDENTITIES FOR JOURNALISM .......................... 25

8.2 NEED FOR FURTHER DISCUSSION BETWEEN STAKEHOLDERS IN SOCIETY ......... 25

REFERENCES................... 27ABOUT THE AUTHORS 30END NOTES ........................ 30


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The Future of Journalism:

Artificial Intelligence and

Digital IdentitiesInteraction between journalism, the Internet and social communities is

familiar and intensely discussed, helping us understand how journalism can

raise our collective intelligence. We discuss how artificial intelligence (AI)

will add to that picture and thus influence the future of journalism. We

describe 'Digital Identities' and their future interaction with journalism. Wesummarize state-of-the-art AI methods usable to establish the 'DNA' of

journalistic content, how matching that content with digital identities enables

behavioral targeting for consumer engagement. We review the driving forces

such procedures may introduce to journalism and show an example of a

journalistic behavioral-targeting engine. We highlight some concerns and

discuss how using digital identities and AI can be complex versus current

journalistic principles. We stress the need for ethical principles in using

digital identities in journalism, and suggest examples of such principles. We

issue a call for stakeholders to jointly explore the potential effects of AI

algorithms on the journalism profession and journalism's role in a

democratic society and suggest questions to be explored.

1. Introduction

Computer-assisted intelligence is part of life: augmented intelligenceiof

individuals using personal computers and collective intelligence of groups whennetworking. Finally, there is Artificial Intelligence (AI), when computers act

intelligently without human interaction, mimicking human intelligence (Turing, 1950)

These intelligences are blending and converging. Augmented individual

intelligence, Collective intelligence and AI are co-evolving. The Internet is

becoming part of our minds and our minds are becoming part of the Internet.

Journalism is part of IT-assisted intelligence. Personal computing entered

journalism in the 80s, the Internet in the 90s, and we are now seeing the explosion

of social interaction enter journalism, ranging from reader comments to crowdsourcing.


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The interaction between journalism, the Internet and social interaction is familiar

and intensely discussed, helping us understand how journalism can help increase

our collective intelligence. Here we study how AI may contribute through

algorithms being developed for rating news, based on mixing systems for

aggregating crowd opinions (collective intelligence) and smart algorithms for

contextual analysis (AI).

Ratings help control societal systems. Any recognized rating method influences

societal development; people will try to improve their ratings. A rating that

changes peoples lives represents a complex issue. Even if everyone finds a rating

annoying and counterproductive, it will still influence the system, given that people

think others recognize the rating. Indeed, journalism must scrutinize and challenge

rating systems and explore alternatives. Intelligent algorithms rating journalism,

such as TechMemeii, strive to share in public perception of which tech journalism

matters more than others. This may incent journalism to optimize stories to rank

high. This is only one example of how AI is co-evolving with journalism.

Journalisms role is to focus attention on stories that interest the public. For

journalism to remain meaningful, it should also empower the audience. So how

does it interact with individuals augmented intelligence, societys collective

intelligence and machine AI? Ideally, journalism raises intelligenceempowering

the audienceas it uses higher intelligence around it, i.e. the audience and the

machines.

AI algorithms are changing professional journalism and related academic research.

AI is penetrating journalisms traditional pillars: journalistic content (via automatic

content analysis in all media formats and delivery systems) and advertising (by

measuring consumer attention and targeting ads per user digital identity or

personality, measured by behavior). Both content and advertising are changing

dramatically.

The new media and AI technology based on computings growing power are the

change agents. Interactive new media is permitting, for the first time, accurate

measurement of the attention each user gives to journalistic content. Advertiserswill demand full validation of consumer ratings. Existing measuring methods will


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vanish. Fierce competition will arise in selling consumer attention to advertisers,

whose ROI (Return On Investment) will determine the fate of channels for

advertising, including journalism paid by ads, across all media formats.

Journalistic content is undergoing major changes via interactive platforms that

make media content available continually, everywhere. Until recently, the mass

media for distributing content were controlled by the same companies that

produced content. The traditional business model for news and entertainment

included controlling and bundling both medium and content. But with the Internet,

a new generation of media incumbents is arising. Companies such as Twitter,

Facebook or Google consciously avoid producing content. They do not do

journalism; they only provide access to journalism.

Journalism is separating from the media (Nordfors, 2008)iii

. The latest generation of

producers of journalism is no longer involved in the processes or infrastructures of mass

communication. They focus on producing content and publishing on-line,

delivering it via the infrastructures of the new content-neutral media entities. The

Huffington Post and TechCrunch, started as blogs, are now large and important

publications, without controlling the infrastructure for spreading content.

Traditional media spend hugely to measure readerships, estimating their sizes and

attention probabilities and creating statistics and probabilities for advertisers. On

the Internet, the new media offer content producers and advertisers not

probabilities but hard data: which user looked at what, where, when and for how

long. Advertisers know if a reader clicked their ad. Traditional media ads are

indiscriminate, broadcast to all consumers and costing the same, regardless of how

many people pay attention or act. The Internet enables contextual advertising,

where advertisements shown to each user are selected and served by automated

systems based on content displayed to the user.

Monitoring users and adapting content and ads to individuals is revolutionizing

content, media and marketing. In a digital-interactive world, marketing must

account for media spending. The ROI in advertising and targeting content is

becoming a science, driving development of advertising, media and content. In

2007, global ad spending was estimated at $385B

iv

, equal to the 2008 GDP of theworlds 26th largest economyv.


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Targeting content per consumer digital identity will require AI engines to analyze

multi-dimensional content vs. attributes of the engaging experience and a

consumers total beingrelate human DNA, content DNA and context DNA

(attempts to identify successful music and literature DNA already exist). Research

in biology, genetics and psychology that explore and identify links between

individuals genetic codes, cognitive attributes and pro-/anti-social behavior is

merging with data mining relating to Web 2.0 social-network activities aimed at

consumer profiling. Digital Identities will integrate a person's genetic code with

data derived from web clicks. People will pay with privacy for social networks

benefits.

New AI algorithms analyze contenttext, video, audio and still imagesto

annotate (tag) content automatically. Global efforts are creating unified digital-

identity standards to individuals and use AI engines to target, code and annotate

content automatically vs. digital identity. This will affect journalistic content

significantly and may revolutionize journalism and its academic research.

Journalism must adapt and investigate new business models (Lemelshtrich Latar & Nordfors,

2009)vi.

In this article, we describe digital identities and new global standards for digital

identities, the use of social networks, genetics and virtual worlds for creating

digital identities and the new AI research being used for adapting content to digital

identities. Scientists are converting journalistic content to math formulations

(signatures) to understand content and context. We probe the popular concept of

media engagement and its derivativesbehavioral targeting, contextual targeting,

and how AI is used in social networks to target content and ads. An AI engine

that can filter and target journalistic content based on the consumers digital

identity, to maximize the ROI of every dollar spent on advertising will be

described.

We highlight some concerns and discuss how using digital identities and AI can be

complex versus current journalistic principles. We stress the need for ethical

principles in using digital identities in journalism, and suggest examples of such

principles. We issue a call for stakeholders to jointly explore the potential effectsof AI algorithms on the journalism profession and journalism's role in a democratic


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hold on to dated one-to-many media technologies. Their business models, based on

controlling the medium and the content, have been difficult to move to the Internet.

In cases where new business models for ads have succeeded, such as Google, eBay or

Craigslist, the brokering of ads is not integrated with the practice of journalism.

Journalisms essence is described in principles of journalism, as suggested by the

Pew Research Centers Project for Excellence in Journalism (PEJ) and the

Committee of Concerned Journalistsviii

:

1 Journalisms first obligation is to the truth;2 Its first loyalty is to the citizens;3 Its essence is a discipline of verification;4 Its practitioners must maintain independence from those they cover;5 It must serve as an independent monitor of power;6 It must provide a forum for public criticism and compromise;7 It must strive to make the significant interesting and relevant;8 It must keep the news comprehensive and proportional;9 Its practitioners must be allowed to practice their personal conscience.

These principles remain, even when we no longer know what the media are.

Consider a new, short definition of journalism, separating it from the media,

connecting journalistic principles based on the relation between journalism and its

audience, rather than on its relation to the communications medium it uses (which

is what is causing the confusion today). Take, for example, the following

suggestion (Nordfors, 2009)

Journalism is the production of news and feature stories, bringing public

attention to issues that interest the public. Journalism gets its mandate from

the audience.

Journalism must act on behalf of its audience, not its sources or advertisers.

Journalism often has business models based on attention work (Nordfors, 2006). generating

and brokering attention, such as selling ads. Therefore, much journalism is attention work

performed with a mandate from the audience. When attention work is done with a mandatefrom the sources, it is public relations and publicity, not


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content automatically searches for consumers based on their digital identities. But

first we describe current research on automatic knowledge retrieval from

journalism multimedia content. Most research tools used by the different

communities aim at dividing content into small content digital units, analyzing

them, tagging these sub-content units and then carrying an integrative analysis to

conceptualize the entire content meaningfully for the consumers. Some researchers

convert the visual content into mathematical formulations that can then be

subjected to analysis employing AI algorithms(Jeon, Laverenko, and Mammatha, 2007).

3.1 Content Based Image Retrieval (CBIR)

The primary method used in the search for image retrieval or automatically

conceptualizing visual content is dividing the visual frames into smaller

sections/regions termed blobs. This is achieved by using statistical tools such as

clustering. Each blob is annotated with text. The visual image is described by

employing categories such as color, texture, shapes, and structures. Statistical

theories are used to associate words with image regions that are then compared

with human manual annotations of similar images (Smeulders et al., 2000). Attempts are

made to describe images using vocabulary of blobs as proposed by Duygulu et al. (2002).

Jeon et al. (2007)proposed a method for using training set of annotated images for cross

media relevance model for images.

CBIR researchers are developing mathematical descriptions of images defined as signatures.

The signatures describe an image in mathematical formulations to let researchers measure

content similarities between image frames. Statistical methods such as clustering and

classification form image signatures that will allow automatic similarity measurements by

machines. Images are segmented by features such as color, texture differences, shapes and

other salient points.


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3.4 The DNA of Literature

Before the invention of computers (but after Boolian Logic and Bayes Theorems

laid the mathematical foundations of modern computers and algorithms), in the

mid 19th century, the French writer George Polti, (b.1868) analyzed the elements of

successful literature, its DNA. Polti listed thirty-six dramatic situations in good

drama, including prayer to the supernatural, crime pursued by vengeance, loss or

recovery of a lost one, disaster, remorse, revolt against a tyrant, enigma and others.

Poltis list remains popular and writers often use it in developing stories. Terry

Rusio, the Shrek scriptwriter, said he referred to Poltis list to resolve a situation

in the films plot. To create his list, Polti analyzed classical Greek texts and French

literature. His analysis of the DNA of drama was followed by other writers.

These attempts to discover good story elements and persuasive drama were not

written from an information-retrieval perspective but provide the literary blobs

that will let researchers dissect, in our case, a journalism story along its content

elements.

These content elements, found in texts, should receive mathematical formulations

that will allow computer-based analysis. One should be able to retrieve content

based, for example, on Poltis thirty six situations or preferably other sets of

situations, which may be determined by modern data analysis of journalism stories,

for comparative analysis or for marketing news stories to consumers based on their

digital identities.

4. Journalism Content and Consumer

Engagement

4.1. The Concept of Media Engagement

The economic engine driving journalism in the non-public service media has until

now been advertising-based. Journalism companies, regardless of media platform

(paper, video, audio) have sold consumer attention to advertisers. Though

inaccurate, rating was the key measuring tool until the Internet. No pre-Internet

rating technique can measure real attention by individual consumers to specificcontent. New media platforms, with the advance of interactive new media, make


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the competition for consumer attention fierce and complex. The journalism

industry now needs to develop new ways to measure consumer attention in

multiple parameters, including the consumer cognitive and behavioral profiles and

context parameters. The interactive nature of the new media platforms begins to

allow for scientific measurement of consumer attention along personal dimensions.

In this new battle for consumer attention the concept of engagement, a relatively

new term, is being used to describe the new relations between consumers and

journalistic content. The Advertising Research Council (ARC) has devised the

following definition of media engagement: Engagement is turning on a prospect

to a brand idea enhanced by the surrounding context the working definition

proposed by ARC encapsulates the ultimate objective of linking positive effects

towards a brand with brand advertising within the environment of the program

content" (Kilger, and Romer, 2007).

Context within which content is delivered is becoming of prime importance. Kilger

and Romer identified three mechanisms that enhance consumer engagement in a

journalistic content:

Cognitive (relevance of the program and advertisement to the consumer)

Emotional (the extent to which one likes the content and advertising)

Behavioral (paying attention to the program and advertising content) (ibid) .

The main hypothesis is that the more engaged consumers are the more they will

spend on the advertised product. This recognition by the advertising world, that

engagement in journalistic content involves consumer cognition, emotional profile

and behavior, provides relevance to computer-based information retrieval as

applied to content analysis.

Research by Kilger and Romer (ibid) about the relationship between media engagement and

product-purchase likelihood reveals that as engagement measures increased so did the mean

likelihood of products advertised in the media to be purchased. Three media platforms were

studiedtelevision, Internet and printed magazines. All three exhibited similar findings.

Internet and magazines exhibited very close response curves, while TV followed a similar

path but slightly lower mean of purchase likelihood." (ibid).


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The personal parameters Kilger and Romer examined were those traditionally used in social-

science research: gender, age, education, income, race and marital status.

Age, income and race mattered. In the TV and Internet, people with lower

education expressed higher levels of trust in the media and older people reported

lower engagement. The finding that personal attributes affect media engagement is,

as can be expected, of great relevance regarding digital identities. Digital identities

are valuable to advertisers who will not hesitate to take advantage of them once

available on a large scale and accessible automatically. The road to influence

journalistic content in the direction of higher consumer engagement is short. Kilger

and Romer considered a limited number of personal parameters, as a larger number was too

many to fill within the space constraints of this article." (ibid).

The Internet offers ways to measure and broker not only consumer attention and

engagement but also consumer interaction. A pay-per-click model does this, as

advertisers will pay not for being visible but for consumer clicks, an action. This

can be taken further. For example, a click on an ad will usually lead to a sales site

and may result in further interaction between the consumer and the vendor,

including a purchase. So ads, thus journalistic content, could in principle be paid

by finders fees. This could, however, introduce business incentives for journalists

that might jeopardize journalistic principles.

To convert the content-engagement/product-purchase relations into a science

requires the analysis of many variables including contextual ones, and requires

automation and the introduction of artificial intelligence: Excelling during an era

of frugality in high expectations requires digital marketers to be accountable for

every dollarThe ROI focus will force agencies to improve effectiveness and

we see increased dependence on automation recent shifts [in the liberal

direction] in user privacy perceptions have created a window for marketers to use

AI to run efficient campaigns."(ibid).

The ultimate goal of engagement as perceived by the advertising industry is to

target advertisements to consumers based on contextual and personal parameters as listed by

the Kilger group: cognition, emotions and behavior. This today is being

done and researched in the new media channels, termed Behavioral Targeting' by

academic researchers, journalists and the advertising industry.


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4.2 Behavioral Targeting and Journalistic Content

In the late 90s a new marketing field gained academic and industry attention:

Behavioral Targeting. Recent advances in Internet and Web 2.0 interactivity,

characterized by consumers becoming content creators and providers, have opened

new frontiers for targeting ads to consumers based on interactive behavior.

Behavioral Targeting is the ability to deliver ads to consumers based on their

behavior while viewing web pages, shopping on-line for products and services,

typing keywords into search engines or combinations of all three(Aho Williamson, 2005).

Many Internet companies are involved in behavioral targeting, including Google,Microsoft and Yahoo. M. Kassner (2009) surveyed Googles extensive use of behavioral

targeting. Google confirms this in its official website. Google uses two separate systems,

Adwords and AdSense. Adwords targets ads based on the search subject matter by identifying

search keywords. AdSense targets ads based on website content the consumer views for

example if you visit a gardening site, ads on that site may be related to gardening." (ibid).

AdSense was extended to searching annotated images and videos in YouTube. According to

Kassner, Google is also trying to present relevant advertisements in the Gmail

applicationby scanning every Gmail message for spam and sending ads based on the

keywords the whole process is automated and involves no human matching ads to the

Gmail content." (ibid).

Googles rationale is that by making ads more relevant to customers it brings them

more value. So far, AdSense and Adwords, in all their applications, are still based

on text analysis. Once image and video content are analyzed and annotated

automatically, behavioral targeting will likely be applied to all journalistic content.

4.3 Behavioral Targeting in Social Networks

Social networks characterized by voluntary profiling by members uploading

personal data in texts, pictures and videos are ripe for behavioral targeting. Social

network members profiles include lists of friends, hobbies, demographics and

other interests. Behavioral targeting is growing rapidly in social networks. Startups

are devising behavioral-targeting technologies developed for social networks.

Stefanie Olson (2008)describes one example: 33Across.com. The New York-basedcompanys algorithms can follow consumer behavior patterns in social networks,


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identify sociograms among members and identify for advertisers the more

influential members and the viral propagators by studying message dynamics.

Universal Pictures is using 33Across to study how people share studio trailers or

content with their friends (ibid).Other companies that started to use behavioraltargeting on social networks for marketing advertisements include Reverence

Science and Tacoda Systems (bought by AOL, now a full subsidiary). Yahoo

launched SmartAds, to combine behavioral information with demographic data for

targeting ads. Behavioral targeting ad spending is projected at $1B in 2010,

growing to $3.8B by 2011 (Mills, 2007).

Behavioral targeting raises serious privacy issues discussed extensively in

academic literature and political circles. The issue of privacy vis-a-vis consumer

profiling is beyond the scope of this paper. Tim Berners-Lee, credited with

inventing the World Wide Web, spoke before the U.K. parliament on privacy and

the Internet. He said that he came to raise awareness to the technical, legal and

ethical implications of the interception and profiling by ISPs in collaboration with

behavioral targeting companies.(Watson, 2009). He continued: It is very important that

when you click, you click without a thought that a third party knows what we are

clicking on I have come here to defend the Internet as a medium. (ibid).

But surveys by TRUSTe (a privacy company) shows that the public show a

willingness to submit to monitoring and enhanced content delivery.(Olsen, 2008).This is

a remarkable finding that should be followed.

4.4 Project Smart Push

Davitz of SRI applies machine-learning techniques to study communications in

social networks as part of a multimillion dollar project funded by the Defense

Advance Research Project Agency (DARPA) of the U.S. Department of Defense.

Davitzs objective was to automatically monitor peoples interest and influence in

military communities to identify the influencers then to ensure that they see

relevant information in news feed to that topic." (Oslen, 2008)Davitz calls this targeting of

news according to members interest profiles Smart Push. According to Olsen, SRI is

looking at commercial applications for it not related to advertising you can already learn

more about people from MySpace and Facebook." (ibid).


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When a powerful research institute like SRI promotes concepts like Smart Push,

news media, when rating is king, will adjust journalistic content to fit consumers

digital profiles. This may be done by using an AI engine to filter or webline

services based on digital identities.

5. AI: Digital Identities and Behavioral

Targeting Engine

5.1. Managing Digital Identities Developing aUniversal Standard

The consumers digital identity is a vital component in this process and will

directly affect the type of services and information he or she will receive.

Today, the global knowledge industry invests great resources in developing and

improving management techniques of digital identities. Digital-identity

management is developing rapidly and is called federated identity management.

The term federated identity refers to various components of users profiles

gathered while they surf on different sites and consolidated into uniform profiles

according to a global standard. The term is also used for adoption of standards for

the consumer-identification process on the various platforms. Currently, the most

acclaimed standard for constructing digital identity is called SAML2, Security

Assertions Markup Language 2.0;ixit enables consolidation of digital identities of

surfers on various platforms and management of those identities; and it allows

mobilizing various parts of the surfers identity definition, defined on different

social networks, and merging them into one virtual profile. The standard was

successfully assimilated in financial organizations, academic institutions, the

American electronic government and more.

Adoption of international standards for defining digital identities is significant. It

will enable researchers to follow surfers in any site in cyberspace and carry out

widespread studies on the connection between the users digital identities and their

personalities, fields of interest and cognitive abilities. Every surfer has a uniquelydynamic way of surfingderived from the person's ability to make decisions,


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memory and additional cognitive factorsrendered to automatic cognitive

diagnosis through AI algorithms.

Soon AI algorithms will be able to construct a personal digital identity for every

person performing actions on the Internet. Data-mining robots will be able to

analyze texts, video and audio contents and transform them into sociological DNA

(SDNA) that will describe the individual personality (Lemelshtrich Latar, 2004).

Constructing the digital identity is a dynamic process updated as long as the person is active

on the Web.

5.2 Digital Identities and Social Networks

One of the main Internet uses is activity in social networks. Today, millions of

people belong to social networks that answer many needs, social, economical and

political. A social network is a group that maintains connection to exchange

information in text, video, photos or voice or for social purposes. Every network

member must give personal details about themselves, and these are exposed to the

other network members or part of them, according to the users choice(Boyd andEllison,

2007). Some major networks, originally constructed as reservoirs for content to serve the

surfers, see their purpose today in providing services, information and products adapted to

members digital identities. In September 2007 the network Myspace informed its

shareholders that it intended to undertake data mining, using the profiles and blogs of

approximately one hundred million of its members, to direct advertisements and services to

them. Thus, this is the start of a screening system that will provide services and information to

members according to their digital identity(Abramovitch, 2007). The declared objective is to

improve the membership experience on the network, to add value to the user experience

(almost a paraphrase of Aldous Huxley in Brave New World).

Social networks create a substantial and dangerous expansion of the digital-identity

notion to include complete mapping of surfers social and professional

connections. This mapping will accompany the surfers in all human activities and

may become a powerful filter that will limit the information and possibilities

presented to them, without them being aware of it.

5.3 Socio-Genetics and Digital Identity


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The mind and the body hang together, and science is constantly improving the

knowledge about it. We know today that social behavior is linked to genetics.

Understanding these connections, and how they work in a social context, is

powerful for constructing digital identities and can be valuable for analyzing the

body, mind and ecosystem surrounding them: society. So information about

peoples genetic codes may be as rewarding for constructing digital identities as

the information from social networks.

Research and instrumentation for mapping mans genetic code, gene sequencing,

are developing rapidly at leading research institutes and large commercial

companies worldwide. Their main objective is to identify genes associated with

hereditary diseases and to develop medication based on genetic treatment. Since

the completion of the Human Genome Project in 2001, commercial competition

has arisen between companies for producing machines that map the genetic code of

man. The main research project in this field is the Personal Genome Project. x

The connection between genes and human traits, and the entry of information-age

giants such as Google and leading research centers such as Harvard and Cornell

into the field of genetic research, should close the knowledge research gaps much

faster. The large volume of participants in these studies, the vast databases holding

participants digital identities and data mining peoples social behavior on the

Internet, together with the use of smart algorithms is helping science to begin to

predict social behavior, both pro-social and anti-social, according to the genetic

mapping of humanity.


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The model shows a dynamic learning model constantly updated as it learns the

consumer profile and content preferences. Unknown factors are expressed by

probabilities constantly updated in the learning process. Journalistic content will

be monitored constantly as consumers interact and make choices. The AI engine

will also monitor context parameters and consumers emotional state during

interaction by analyzing verbal or other reactions. A brief description of the

information flow:

Step One: All journalistic content is analyzed by AI smart algorithms and receive

automatic annotations (tags);

Step Two: Consumers digital identities and annotated content are fed to the

Assessment Rule Engine for initial content determination; proper ads are sent to

consumers based on their profiles;

Step Three: Consumers interact with the content and advertisements; this

interactivity is monitored constantly and consumer attention measured;

Step Four: The Learning Engine analyzes consumer feedback and automatically

adjusts the probabilities to better describe consumer behavior; new content is sent

to consumers;

Step Five: The Learning Engine transmits updated information to a Personal

Memory database where a consumer media profile is created and constantly

updated;

Steps four and five continue indefinitely to allow the AI engine to accurately

predict consumer content and product interests/choices in varying contextsthe

Learning Process section.

6. Digital Identities and Weblining

Filtering journalistic content vs. consumer profiles could lead to serious social

inequality. Marcia Stepanek coined weblining to describe this phenomenon:

Call it weblining, an information-age version of that nasty practice of red lining,


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where lenders and other businesses mark neighborhoods off limits. Cyber space

doesnt have geography but thats no impediment to weblining [] weblining may

permanently close doors to you or your business."(Sterpanek, 2000).

New York University sociologist Marshall Blonsky adds to the meaning of

weblining: If I am weblined and judged to be of minimum value, I will never

have the product and services channeled to me or the economic opportunities that

flow to others over the net." (ibid).

Digital identity is at the core of weblining. Though the emphasis of Stepanek and

Blonsky is on economic aspects of commercial organizations, the described

phenomenon is also true in spreading journalistic content based on profiling. The

economic forcesadvertisers and the journalism organizationscannot be

expected to show altruism and create mechanisms to protect our right to equal

accessibility to content. More seriously, no one can protect us from the effects of

the need to target content per consumer profiles on the quality of journalistic

content.

7. Digital Identities and the Practice ofJournalism

From the point of view of journalism practice, the emergence of digital identities

suggests that publishers and journalists will be able to simulate and measure what

their news stories will do for audiences and the other stakeholders in their

storytelling, while they are developing the story. They would be able to test run

stories before publication, much as advertisers now do with new product tests. This

will introduce interesting opportunities and challenges for journalism.

Simple on-textual advertising need not threaten journalistic principles of separation

between content production and selling audience attention to advertisers (who may

have stakes in the stories). But as contextual advertising starts to understand

content, context and audience better, ads will be placed precisely. Present pay-perclick

business models will create an incentive for publishers to focus on stories that match ads. If

so, it will threaten journalistic freedom. The classic separation of

Church and State, the metaphor used by publishers to distinguish between news


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stories and paid advertising, will blur.

For example, consider a situation where readers use their digital identities,

combined with a series of filters, to select news stories they want to be brought to

their attention. Lets say the quality of filters and digital identities is good enough

to estimate both the chance that a story will catch the reader attention and the

chance it will lead to action by the reader. Now consider a set of contextual

advertisers (these can also be digital identities) that will pay for attention and

interaction with readers. Consider a journalist with access to these digital identities

and filters, as well as access to the contextual advertisers, when writing a story.

The journalist can test the story on digital identities representing both audience and

advertisers as the story is written. The journalist can adjust the writing to receive

the best results, a combination of what the journalist, the audience wants and the

advertisers want.

Consider, finally, that the journalists own digital identity will be included in the

interaction, The journalists digital identity is combined with a set of filters for

selecting themes that the journalist wishes to cover, connected to readers and

advertisers digital identities, and exposed to a news ticker type flow of events,

e.g. all the twitter feeds, the blogosphere and all the other news feeds on the

Internet. It can be data flow from stock markets, sensors measuring weather or

earthquakes etc. The journalist can then be tipped off about events that will

produce suitable matching between his/her own interests, and the interests of the

audience and advertisers.

Thus producing a successful story is equal to solving a dynamic equation involving

the journalist, the audience and the business model, e.g. the advertiser. Producing a

journalistic story while guided by the interaction between the digital identities and

the filters can be seen as an iterative, heuristic solution of the equation, identifying

overlapping interests and optimizing the combined actions into a result maximizing

value for each party. In each interaction, real-life users behind the digital identities

give feedback, reinforcing or modifying digital identities and filters actions, to

improve the outcome in the next round.


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8. Principles of Journalism and Digital

Identities

The interaction between digital identities, as discussed above, may improve the

outcome for all parties involved. But it is a hazardous scenario. It needs to be

discussed among the actors who care about journalism and its role in society.

Looking at existing journalistic principles, at least the following can be strongly

affected by the above scenario:

Journalisms first loyalty is to the citizens: Journalists can be pressured toshow loyalty to citizens digital identities rather than to the citizens themselves.

If each story is coupled directly to the business model, and if the businessmodel builds on selling audience attention/interaction to advertisers, this can

be a problem. It will be difficult to maintain a loyalty to the audience of

citizens if the journalist will earn more money by adapting to the [digital

identities of the] advertisers.

Its practitioners must maintain independence from those they co ver:It

may be possible to involve behavioral models of those covered in the stories in

the equation. This will improve the journalists chances to plan a series of

stories, knowing how the outcome of one story opens for the next. It will give

journalists a tool for projecting the effects the story will have on stakeholders.

Those covered in the story may also be advertisers or have strong, shared

interests with advertisers. This makes the web of co-dependencies more visible

to the journalist. In some cases this can help a journalist to be independent but

in many other cases it will make it difficult to maintain independence.

Its practitioners must be allowed to practice their personalconscience: If

the business model and the system of digital identities and filters permits

projecting how much profit a story can produce as it is written, or if it will

offer predictions of how the story will influence stakeholders in the journalism

organization, probability increases that the journalists personal consciences

may conflict with businesses or other stakeholders interests. In short: if I

write the story the way I want, my publisher will know that I chose to earn less

money. Or: If I write the story the way I want, my publisher will know that I

chose to increase the risk of us getting in conflict with the advertisers.


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These are only quick, simple examples of types of issues that need to be considered

while developing systems of digital identities and filters for journalism.

8.1 Principles for Using Digital Identities for

Journalism

We suggest the need for principles for using digital identities in journalism. Some

such may be:

1.Peoples needs are more important than the needs of digitalidentities

Digital Identities can never be identical to a persons whole being. Some

measure of error should always be considered. People are more important

than digital identities. Digital identities should adapt to people, not viceversa;

2 Using digital identities in journalism should not compromisejournalisms loyalty to the audience o r its independence fromsources;

3 Using digital identities in journalism should not compromise thejournalists freedom to practice his/her personal conscience

8.2 Need for Further Discussion Between

Stakeholders in Society

A group of computer scientists, AI researchers and roboticists met in Asilomar

Conference Grounds on Monterey Bay in California to debate whether there

should be limits on research that might lead to the loss of human control over

computer-based systems that carry a growing share of societys workloadtheir

concern is that further advances could create profound social disruptions and even

have dangerous consequencesand force humans to learn to live with machines

that increasingly copy human behaviors"(Markoff, 2009).

The scientists were concerned about job loss or criminals accessing these tools. No

reference was made to the possible devastating effects that using AI tools may have

on journalistic content. The conference was organized by the Association for the


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Advancement of Artificial Intelligence (AAAI). Dr Horvitz of Microsoft, who

organized the meeting, said he believed computer scientists must respond to the

notions of superintelligent machines and artificial intelligence run amok the

panel was seeking ways to guide research so that technology improved society

rather than move it toward technological catastrophe" (ibid).

It is time to organize a similar conference with computer scientists, AI experts,

academic researchers in the area of multimedia information retrieval, journalism

professionals and experts, social communication experts and economists who

specialize in media business models, to explore the potential effects of AI

algorithms on the journalism profession and its role in a democratic society. Some

of the questions to be explored:

1. Will people control or be controlled by their digital identities?

2. How will the definition of journalism be influenced by digital identities?

3. With the Internet, journalism is no longer only broadcasting but also

interacting with readerships and facilitating public discussions. What is the

role of journalism in society?

4. How will journalistic principles be affected by interaction between digital

identities?

5. Which business models are enabled by digital identities? To what extent

will journalists be attention workers, paid by brokering the readership

attention to advertisers; to what extent will they be knowledge workers,

paid by brokering knowledge?

6. What are suitable principles for journalism, in a situation where interaction

with and between digital identities guides the production of journalism, the

ways it generates value for people, and the ways it creates profits for the

journalism industry?

7. What is the match between journalism and journalistic business models?

8. How will journalistic principles and matching business models be updated?

9. How are journalistic principles, and the process for updating them, be

implemented in an environment of digital identities?


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About the authors

Noam Lemelshtrich Latar is the Founding Dean of the Sammy Ofer School ofCommunications at IDC Herzliya (the first private academic institution in Israel),and serves since 2009 as the Chairperson of the Israel Communications

Association, which groups all media researchers in the Israeli Universities and

Colleges. Lemelshtrich Latar received a Ph.D. in communications from MIT in

1974 and MSc. in engineering systems at Stanford in 1971. He was among the

founders of the Community Dialog Project at MIT, experimenting with interactive

TV programs involving communities through electronic means. From 1975 to 2005

Lemelshtrich Latar pioneered the teaching and research of new media at the

Hebrew and Tel Aviv Universities. From 1999 to 2005 he was involved in the

Israeli high-tech industry as a venture-capital chairman, helping to establish several

communications start ups in cognitive enhancement, data mining of consumer

choices and home networking. In 2005 he joined IDC Herzliya Israel as founding

Dean of a new school of communications, emphasizing new media. His current

research interest is in digital identities and the effect of AI on journalism.

David Nordfors is co-founding Executive Director of the Center for Innovation andCommunication at Stanford University. He coined

Innovation Journalism and Attention Work and started the first innovation

journalism initiatives, in Sweden and at Stanford. He is a member of the World

Economic Forum Global Agenda Council on the Future of Journalism. Nordfors is

adjunct professor at IDC Herzliya and visiting professor at the Monterrey Institute

of Technology and Higher Education (Tech Monterrey). Dr. Nordfors has a Ph.D.

in molecular quantum physics from the Uppsala University, and did his postdoctoral

research in theoretical chemistry at the University of Heidelberg. He was

the initial Director of Research Funding of the Knowledge Foundation in Sweden

(KK-stiftelsen). He was the first Science Editor of Datateknik, a Swedish IT

magazine, from where he initiated and headed the first hearing about the Internet to

be held by the Swedish Parliament.

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Institute, Menlo Park, CA. Related concepts: 1) IA or Intelligence Amplification by Ashby, W.R.

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popular technology-related news of the day, which is continuously updated. The stories selected are

all chosen by an automated process. http://en.wikipedia.org/wiki/Techmeme (11 Jan 2010)

"""Nordfors, D. (2008). Separating Journalism and the Media, EJC Magazine, 4 Dec 2008, EuropeanJournalism Centre http://www.ejc.net/magazine/article/separating_journalism_and_the_media/

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#""Compact Oxford English Dictionary, published on-line by AskOxford.com. Retrieved Sep 6 2009.#"""PEJ and CCJ Principles of Journalism, published 1997. Available athttp://www.journalism.org/resources/principles Retrieved 6 Sep 2009.

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ai andthe future of free journalism

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