Crying Wolf? On the Price Discrimination of Online Airline Tickets

PDF Posté par : Nozha Boujemaa

Price discrimination refers to the practice of dynamically varying the prices of goods based on a customer’s purchasing power and willingness to pay. In this paper, motivated by several anecdotal ac- counts, we report on a three-week experiment, conducted in search of price discrimination in airline tickets. Despite presenting the companies with multiple opportunities for discriminating us, and contrary to our expectations, we do not find any evidence for systematic price discrimi- nation. At the same time, we witness the highly volatile prices of certain airlines which make it hard to establish cause and effect. Finally, we provide alternative explanations for the observed price differences.

Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems

PDF Posté par : Daniel Le Métayer

Algorithmic systems that employ machine learning play an increasing role in making substantive decisions in modern society, ranging from online personalization to insurance and credit decisions to predictive policing. But their decision-making processes are often opaque—it is difficult to explain why a certain decision was made. We develop a formal foundation to improve the transparency of such decision-making systems. Specifically, we introduce a family of Quantitative Input Influence (QII) measures that capture the degree of influence of inputs on outputs of systems. These measures provide a foundation for the design of transparency reports that accompany system decisions (e.g., explaining a specific credit decision) and for testing tools useful for internal and external oversight (e.g., to detect algorithmic discrimination). Distinctively, our causal QII measures carefully account for correlated inputs while measuring influence. They support a general class of transparency queries and can, in particular, explain decisions about individuals (e.g., a loan decision) and groups (e.g., disparate impact based on gender). Finally, since single inputs may not always have high influence, the QII measures also quantify the joint influence of a set of inputs (e.g., age and income) on outcomes (e.g. loan decisions) and the marginal influence of individual inputs within such a set (e.g., income). Since a single input may be part of multiple influential sets, the average marginal influence of the input is computed using principled aggregation measures, such as the Shapley value, previously applied to measure influence in voting. Further, since transparency reports could compromise privacy, we explore the transparency-privacy tradeoff and prove that a number of useful transparency reports can be made differentially private with very little addition of noise. Our empirical validation with standard machine learning algo- rithms demonstrates that QII measures are a useful transparency mechanism when black box access to the learning system is available. In particular, they provide better explanations than standard associative measures for a host of scenarios that we consider. Further, we show that in the situations we consider, QII is efficiently approximable and can be made differentially private while preserving accuracy.

Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence

PDF Posté par : Nozha Boujemaa

We present Sunlight, a system that detects the causes of target- ing phenomena on the web – such as personalized advertisements, recommendations, or content – at large scale and with solid statisti- cal confidence. Today’s web is growing increasingly complex and impenetrable as myriad of services collect, analyze, use, and ex- change users’ personal information. No one can tell who has what data, for what purposes they are using it, and how those uses affect the users. The few studies that exist reveal problematic effects – such as discriminatory pricing and advertising – but they are either too small-scale to generalize or lack formal assessments of confi- dence in the results, making them difficult to trust or interpret. Sunlight brings a principled and scalable methodology to per- sonal data measurements by adapting well-established methods from statistics for the specific problem of targeting detection. Our method- ology formally separates different operations into four key phases: scalable hypothesis generation, interpretable hypothesis formation, statistical significance testing, and multiple testing correction. Each phase bears instantiations from multiple mechanisms from statis- tics, each making different assumptions and tradeoffs. Sunlight of- fers a modular design that allows exploration of this vast design space. We explore a portion of this space, thoroughly evaluating the tradeoffs both analytically and experimentally. Our exploration reveals subtle tensions between scalability and confidence. Sun- light’s default functioning strikes a balance to provide the first sys- tem that can diagnose targeting at fine granularity, at scale, and with solid statistical justification of its results. We showcase our system by running two measurement studies of targeting on the web, both the largest of their kind. Our studies – about ad targeting in Gmail and on the web – reveal statistically jus- tifiable evidence that contradicts two Google statements regarding the lack of targeting on sensitive and prohibited topics.

Cross-Device Tracking: Measurement and Disclosures

PDF Posté par : Daniel Le Métayer

Internet advertising and analytics technology companies are increasingly trying to find ways to link behavior across the various devices consumers own. This cross-device tracking can provide a more complete view into a consumer’s behavior and can be valuable for a range of purposes, including ad targeting, research, and conversion attribution. However, consumers may not be aware of how and how often their behavior is tracked across different devices. We designed this study to try to assess what information about cross-device tracking (including data flows and policy disclosures) is observ- able from the perspective of the end user. Our paper demonstrates how data that is routinely collected and shared online could be used by online third parties to track consumers across devices.

Mobilitics, saison 2 : Les smartphones et leurs apps sous le microscope de la CNIL et d'Inria

PDF Posté par : Nozha Boujemaa

La CNIL et Inria travaillent depuis maintenant 3 ans sur un projet de recherche et d’innovation ambitieux nommé Mobilitics. Son objectif : mieux connaître les smartphones, ces objets utilisés quotidiennement par des dizaines de millions de français et qui restent de véritables boîtes noires pour les utilisateurs, les chercheurs et les autorités de régulation. Pourtant, ces « amis qui nous veulent du bien » sont d’extraordinaires producteurs et consommateurs de données personnelles. Du point de vue de la recherche, ils incarnent idéalement les enjeux au cœur de l’activité de l’équipe Privatics d’Inria : comprendre les mécanismes techniques autour des données personnelles et concevoir des solutions techniques préservant la vie privée. Un outil capable de détecter les accès à des données personnelles dans les appareils (localisation, photos, carnet d'adresses) a donc été développé, mis au point et expérimenté. Après une première vague de tests en 2013, une « deuxième saison » de Mobilitics a eu lieu pendant l’été 2014. Les premiers résultats présentés dans cette lettre illustrent bien l'intérêt du partenariat entre Inria et la CNIL : des outils imaginés et conçus ensemble sont utilisés par les deux institutions, chacune dans son rôle. Pour la CNIL, il s’agit de mieux comprendre ce qui se passe réellement lors de l’usage de ces appareils, pour définir des priorités d’action et émettre des recommandations. Pour Inria, il s’agit aussi de pousser plus loin les investigations et analyses techniques et de développer des solutions permettant de mieux protéger les utilisateurs. Ces travaux sont donc l’occasion pour les deux institutions de partager leurs analyses et interrogations. En effet, si ces technologies offrent des services extraordinaires aux individus et sont bénéfiques pour la société, elles ne peuvent se développer que dans le respect de la vie privée et des libertés individuelles. Rendre la technologie plus transparente et plus compréhensible aux citoyens est un défi commun pour la recherche et pour l’autorité de régulation.

Online Tracking: A 1-million-site Measurement and Analysis

PDF Posté par : Daniel Le Métayer

We present the largest and most detailed measurement of online tracking conducted to date, based on a crawl of the top 1 million websites. We make 15 types of measurements on each site, including stateful (cookie-based) and stateless (fingerprinting-based) tracking, the effect of browser privacy tools, and the exchange of tracking data between different sites (“cookie syncing”). Our findings include multiple so- phisticated fingerprinting techniques never before measured in the wild. This measurement is made possible by our open-source web privacy measurement tool, OpenWPM, which uses an automated version of a full-fledged consumer browser. It supports parallelism for speed and scale, automatic recovery from failures of the underlying browser, and comprehensive browser instrumentation. We demonstrate our platform’s strength in enabling researchers to rapidly detect, quantify, and characterize emerging online tracking behaviors.

Automated Experiments on Ad Privacy Settings

PDF Posté par : Nozha Boujemaa

To partly address people’s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google’s ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user’s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.

WifiLeaks: Underestimated Privacy Implications of the ACCESS_WIFI_STATE Android Permission

PDF Posté par : Daniel Le Métayer

On Android, installing an application implies accepting the permissions it requests, and these permissions are then en- forced at runtime. In this work, we focus on the privacy im- plications of the ACCESS_WIFI_STATE permission. For this purpose, we analyzed permissions of the 2700 most popular applications on Google Play and found that the ACCESS- _WIFI_STATE permission is used by 41% of them. We then performed a static analysis of 998 applications requesting this permission and based on the results, chose 88 appli- cations for dynamic analysis. Our analyses reveal that this permission is already used by some companies to collect user Personally Identifiable Information (PII). We also conducted an online survey to study users’ perception of the privacy risks associated with this permission. This survey shows that users largely underestimate the privacy implications of this permission. As this permission is very common, most users are therefore potentially at risk.

Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights

PDF Posté par : Nozha Boujemaa

Big data and associated technologies have enormous potential for positive impact in the United States, from augmenting the sophistication of online interactions to enhancing understanding of climate change to making advances in healthcare. These efforts, as well as the technological trends of always-on networked devices, ubiquitous data collection, cheap storage, sensors, and computing power, will spur broader use of big data. Our challenge is to support growth in the beneficial use of big data while ensuring that it does not create unintended discriminatory consequences.

Statement on Algorithmic Transparency and Accountability

PDF Posté par : Nozha Boujemaa

Computer algorithms are widely employed throughout our economy and society to make decisions that have far-reaching impacts, including their applications for education, access to credit, healthcare, and employment. The ubiquity of algorithms in our everyday lives is an important reason to focus on addressing challenges associated with the design and technical aspects of algorithms and preventing bias from the onset.

Learning to trust artificial intelligence systems

PDF Posté par : Nozha Boujemaa

For more than 100 years, we at IBM have been in the business of building machines designed to help improve the effectiveness and efficiency of people. And we’ve made measurable improvements to many of the systems that facilitate life on this planet. But we’ve never known a technology that can have a greater benefit to all of society than artificial intelligence. At IBM, we are guided by the term “augmented intelligence” rather than “artificial intelligence.” This vision of “AI” is the critical difference between systems that enhance, improve and scale human expertise, and those that attempt to replicate human intelligence. The ability of AI systems to transform vast amounts of complex, ambiguous information into insight has the potential to reveal long-held secrets and help solve some of the world’s most enduring problems. AI systems can potentially be used to help discover insights to treat disease, predict the weather, and manage the global economy. It is an undeniably powerful tool. And like all powerful tools, great care must be taken in its development and deployment. To reap the societal benefits of AI systems, we will first need to trust it. The right level of trust will be earned through repeated experience, in the same way we learn to trust that an ATM will register a deposit, or that an automobile will stop when the brake is applied. Put simply, we trust things that behave as we expect them to. But trust will also require a system of best practices that can help guide the safe and ethical management of AI systems including alignment with social norms and values; algorithmic responsibility; compliance with existing legislation and policy; assurance of the integrity of the data, algorithms and systems; and protection of privacy and personal information. We consider this paper to be part of the global conversation on the need for safe, ethical and socially beneficial management of AI systems. To facilitate this dialogue, we are in the process of building an active community of thoughtful, informed thinkers that can evolve the ideas herein. Because there is too much to gain from AI systems to let myth and misunderstanding steer us off our course. And while we don’t have all the answers yet, we’re confident that together we can address the concerns of the few to the benefit of many.

Modalités de régulation des algorithmes de traitement des contenus

PDF Posté par : Nozha Boujemaa

La secrétaire d’Etat chargée du numérique a confié au Conseil général de l’économie une mission d’analyse et de propositions relative aux modalités de régulation des algorithmes de traitement des contenus. Il s’agit des algorithmes utilisés sur le web pour filtrer des contenus, ordonner des réponses à une recherche, sélectionner les informations pertinentes, faire des recommandations, calculer un score, prédire un évènement ou un risque. Ces algorithmes peuvent être très complexes, composés de multiples briques logicielles : Google indique que les algorithmes de son moteur de recherche prennnent en compte 200 critères, Netflix décompose son moteur de recommandation en 12 algorithmes différents. Parmi ces briques logicielles, l’apprentissage machine connaît depuis quelques années un développement fulgurant, lié à l’augmentation de la puissance de calcul (utilisation de cartes graphiques pour réaliser un très grand nombre d’opérations en parallèle) et à la disponibilité de grandes quantités de données permettant l’apprentissage : on fait croître de tels algorithmes, plus qu’on ne les écrit. Par la nature de leur architecture et de leur fonctionnement, il est très difficile de comprendre le processus de raisonnement interne des algorithmes d'apprentissage machine, et donc d’expliquer un résultat particulier. Ces algorithmes posent donc des problèmes de transparence et de contrôle originaux. Les algorithmes sont inséparables des données qu’ils traitent et des plateformes qui les utilisent pour proposer un service. L’originalité du point de vue de ce rapport est cependant de s’intéresser aux algorithmes eux-mêmes et de faire des propositions pour de meilleures pratiques concernant leur développement, leur utilisation et leur contrôle, tout en préservant l’innovation. Trois scénarios de développement de « l’économie des algorithmes » sont en cours de déploiement en parallèle et poseront des questions de régulation différentes. Selon le 1er scénario, les algorithmes se banalisent au sein de l’économie du logiciel, en large partie en open source. Cette fragmentation pose des problèmes de contrôle du traitement des données et de la performance des algorithmes, auxquels il faudra apporter des réponses par la certification des composants logiciels. Selon le 2nd scénario, des leaders de l’intelligence artificielle conservent une longueur d’avance et agrègent des écosystèmes dédiés autour d’algorithmes propriétaires aux performances inégalées. Ces grands systèmes posent des problèmes de concurrence, de position dominante sur certains marchés et d’opacité pour les acteurs extérieurs, à commencer par les régulateurs eux-mêmes. Selon le 3ème scénario, des producteurs de données et des spécialistes du traitement algorithmique s’organisent en silos indépendants pour acquérir une taille critique, tant en termes de masse de données que de compétences en traitement. Cette organisation en silo pose des problèmes de conditions d’accès pour les nouveaux entrants, ainsi que de propriété et de portabilité des données.

The Ethics of Algorithms: 
 from radical content to self-driving cars

PDF Posté par : Nozha Boujemaa

A new kind of object, intermediator, gate-keeper and more has risen: the algorithm, or the code that operates increasingly ubiquitous computational objects and governs digital environments. Computer chips and other forms of computation are not new; however, the increasing integration of digital connectivity in everyday life; the rise of massive amounts of datasets with personal, Jinancial and other kinds of information, and the rise in objects that have embedded chips have combined to create a new environment. This environment has been shaped by three developments: Advances especially in machine learning which allow artiJicial intelligence, with the help of big data, to perform tasks that were outside its reach just a few years ago; the rise of powerful platforms online such a Google, Amazon or Facebook that mediate social, political, personal and commercial interactions for billions of people and act as powerful gatekeepers; and the incorporation of algorithmic capabilities to other areas of decision-making ranging from hiring, Jiring and employment to healthcare, advertising, to Jinance and many others. In sum, algorithms are increasingly used to make decisions for us, about us, or with us. They are progressively capable and pervasive. They are now either main or auxiliary tools, or even sole decision-makers, in areas of life that either did not exist more than a decade ago (what updates and news should you be shown from your social network, as in Facebook’s Newsfeed) to traditional areas where decisions used to be made primarily via human judgment, such as health-care and employment. SigniJicantly, algorithms are rapidly encroaching into “subjective” decision-making where there is no right or wrong answer, or even a good deJinition of what a “right” answer would look like without much transparency, accountability or even a mapping out of the issues. The speed of technological developments, corporate and government incentives have overtaken and overshadowed the urgently needed discussion of ethics and accountability of this new decision-making infrastructure. The concerns that often bring us to thinking about algorithms are both historic and mundane: fairness, discrimination and power. Algorithms, and all complex computational systems, however, operate in ways that are a new category of objects compared with other institutions, persons or objects that have not been probed for such concerns. In this report, we provide some of the key areas that require further probing, research and discussion, and should be taken up by policy-makers, civic actors, citizens and everyone concerned about the ethical, legal and policy frameworks in the 21st century which can nolonger be discussed without incorporating questions of computation. We will begin by deJining algorithms, in particular those that demand ethical scrutiny. We will proceed by illustrating three characteristics of algorithms with cases from a wide variety of Jields. In the Jinal section, we will address three regulatory responses that have discussed in response to the challenges posed by algorithmic decision-making. This background paper is the result of a two-day conference on “The Ethics of Algorithms”, held in Berlin on March 9 and 10, 2015. The event was jointly organised by the Centre for Internet and Human Rights and the Technical University Berlin, with the support of the Dutch Ministry of Foreign Affairs. The results presented in this paper will feed into the discussions at the Global Conference on Cyberspace, which will take place in the Hague on 16 and 17 April 2015.

The National artificial intelligence research and development strategic plan

PDF Posté par : Nozha Boujemaa

Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016, the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federally- funded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide andevaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

Designing AI Systems that Obey Our Laws and Values

PDF Posté par : Nozha Boujemaa

Operational AI Systems (for example, self-driving cars) need to obey both the law of the land and our values. We propose AI oversight systems (“AI Guard- ians”) as an approach to addressing this challenge, and to respond to the potential risks associated with in- creasingly autonomous AI systems.a These AI oversight systems serve to verify that operational systems did not stray unduly from the guidelines of their programmers and to bring them back in compliance if they do stray. The introduction of such sec- ond-order, oversight systems is not meant to suggest strict, powerful, or rigid (from here on ‘strong’) controls. Operations systems need a great de- gree of latitude in order to follow the lessons of their learning from addi- tional data mining and experience and to be able to render at least semi- autonomous decisions (more about this later). However, all operational systems need some boundaries, both in order to not violate the law and to adhere to ethical norms. Developing such oversight systems, AI Guard- ians, is a major new mission for the AI community.

A Survey of Collaborative Filtering Techniques

PDF Posté par : Dominique Cardon

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model- based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Algorithmic harms beyond Facebook and Google: emergent challenges of Computational Agency

PDF Posté par : Dominique Cardon

In June of 2014, experiments performed on Facebook users without their knowledge—a previously little-known topic—became big news, garnering enormous coverage nationally and internation- ally. The furor was sparked by a study published in the prestigious Proceedings of the National Academy of the Sciences (PNAS). Facebook’s researchers had, according to their abstract, confirmed that they had shown “experimental evidence for massive-scale contagion via social networks” by reducing the number of positive or negative posts shown to Facebook users by experimentally manipulating us- ers’ algorithmically curated “News Feed.”1 Facebook employee Adam D.I. Kramer was the first author; the other authors were academics—Jeffrey T. Hancock, a professor at Cornell, and Jamie Guillory, who was Hancock’s graduate student at the time the research was under- taken. Facebook’s algorithmically curated News Feed decides which of the “status updates” a Facebook user sees from his or her friends. The researchers positively showed that news and updates on Face- book influence the tenor of the viewing Facebook-user’s subsequent posts—and that Facebook itself was able to tweak and control this influence by tweaking the algorithm.2 While this was not a surprise to anyone who studied Facebook or other online social platforms, the confirmation of the effect, and the mode of the confirmation— through experimental manipulation of the algorithm—sparked a large conversation.

Algorithmic Ideology

Lien Posté par : Dominique Cardon

This article investigates how the new spirit of capitalism gets inscribed in the fabric of search algorithms by way of social practices. Drawing on the tradition of the social construction of technology (SCOT) and 17 qualitative expert interviews it discusses how search engines and their revenue models are negotiated and stabilized in a network of actors and interests, website providers and users first and foremost. It further shows how corporate search engines and their capitalist ideology are solidified in a socio-political context characterized by a techno-euphoric climate of innovation and a politics of privatization. This analysis provides a valuable contribution to contemporary search engine critique mainly focusing on search engines' business models and societal implications. It shows that a shift of perspective is needed from impacts search engines have on society towards social practices and power relations involved in the construction of search engines to renegotiate search engines and their algorithmic ideology in the future.

Can an Algorithm be Unethical?

PDF Posté par : Dominique Cardon

In Information and Communication Technologies (ICTs), computer algorithms now control the display of content across a wide range of industries and applications, from search results to social media (Gillespie, 2013). Abuses of power by Internet platforms have led to calls for “algorithm transparency” and regulation (Pasquale, 2014). This paper responds by asking what an analyst needs to know ​about algorithms i​n order to determine if an ICT is acting improperly, unethically or illegally. It further asks whether “the algorithm” is a useful object for ethical investigation. The paper briefly reviews the technical history of the term, then performs an ethical analysis of a hypothetical surveillance system to investigate the question of whether it is useful to consider an algorithm “unethical” in itself. It finds that law and policy researchers can in fact employ technical expertise about algorithms and that such expertise might be crucial to make judgments about future ICTs.

Can an Algorithm be Disturbed?

PDF Posté par : Dominique Cardon

Within literary and cultural studies there has been a new focus on the “surface” as opposed to the “depth” of a work as the proper object of study. We have seen this interest manifested through what appears to be the return of prior approaches including formalist reading practices, attention to the aesthetic dimensions of a text, and new methodologies that come from the social sciences and are interested in modes of description and observation. In arguing for the adoption of these methodologies, critics have advocated for an end to what Paul Ricoeur has termed “the hermeneutics of suspicion” and various forms of ideological critique that have been the mainstay of criticism for the past few decades.2 While these “new” interpretations might begin with what was once repressed through prior selection criteria, they all shift our attention away from an understanding of a “repressed” or otherwise hidden object by understanding textual features less as signifier, an arrow to follow to some hidden depths, than an interesting object in its own right. Computer aided approaches to literary criticism or “digital readings,” to be sure, not an unproblematic term, have been put forward as one way of making a break from the deeply habituated reading practices of the past, but their advocates risk overstating the case and, in giving up on critique, they remain blind to untheorized dimensions of these computational methods. While digital methods enable one to examine radically larger archives than those assembled in the past, a transformation that Matthew Jockers characterizes as a shift from micro to “macroanalysis”, the fundamental assumptions about texts and meaning implicit in these tools and in the criticism resulting from use of these tools belong to a much earlier period of literary analysis.

Digital Discrimination: The Case of Airbnb.com

PDF Posté par : Dominique Cardon

Online marketplaces often contain information not only about products, but also about the people selling the products. In an effort to facilitate trust, many platforms encourage sellers to provide personal profiles and even to post pictures of themselves. However, these features may also facilitate discrimination based on sellers’ race, gender, age, or other aspects of appearance. In this paper, we test for racial discrimination against landlords in the online rental marketplace Airbnb.com. Using a new data set combining pictures of all New York City landlords on Airbnb with their rental prices and information about quality of the rentals, we show that non-black hosts charge approximately 12% more than black hosts for the equivalent rental. These effects are robust when controlling for all information visible in the Airbnb marketplace. These findings highlight the prevalence of discrimination in online marketplaces, suggesting an important unintended consequence of a seemingly-routine mechanism for building trust.

EU regulations on algorithmic decision-making and a “right to explanation”

PDF Posté par : Dominique Cardon

We summarize the potential impact that the Euro- pean Union’s new General Data Protection Reg- ulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user-level predic- tors) which “significantly affect” users. The law will also create a “right to explanation,” whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large chal- lenges for industry, it highlights opportunities for machine learning researchers to take the lead in designing algorithms and evaluation frameworks which avoid discrimination.

Algorithms, Governance, and Governmentality: On Governing Academic Writing

PDF Posté par : Dominique Cardon

Algorithms, or rather algorithmic actions, are seen as problematic because they are inscrutable, automatic, and subsumed in the flow of daily practices. Yet, they are also seen to be playing an important role in organizing opportunities, enacting certain categories, and doing what David Lyon calls ‘‘social sorting.’’ Thus, there is a general concern that this increasingly prevalent mode of ordering and organizing should be governed more explicitly. Some have argued for more transparency and openness, others have argued for more democratic or value-centered design of such actors. In this article, we argue that governing practices—of, and through algo- rithmic actors—are best understood in terms of what Foucault calls governmentality. Governmentality allows us to consider the performative nature of these governing practices. They allow us to show how practice becomes problematized, how calculative practices are enacted as tech- nologies of governance, how such calculative practices produce domains of knowledge and expertise, and finally, how such domains of knowledge become internalized in order to enact self-governing subjects. In other words, it allows us to show the mutually constitutive nature of problems, domains of knowledge, and subjectivities enacted through governing prac- tices. In order to demonstrate this, we present attempts to govern aca- demic writing with a specific focus on the algorithmic action of Turnitin.

Bearing Account-able Witness to the Ethical Algorithmic System

PDF Posté par : Dominique Cardon

This paper explores how accountability might make otherwise obscure and inaccessible algorithms available for governance. The potential import and difficulty of accountability is made clear in the compelling narrative reproduced across recent popular and academic reports. Through this narrative we are told that algorithms trap us and control our lives, undermine our privacy, have power and an independent agential impact, at the same time as being inaccessible, reducing our opportunities for critical engagement. The paper suggests that STS sensibilities can provide a basis for scrutinizing the terms of the compelling narrative, disturbing the notion that algorithms have a single, essential characteristic and a pre- dictable power or agency. In place of taking for granted the terms of the compelling narrative, ethnomethodological work on sense-making accounts is drawn together with more conventional approaches to accountability focused on openness and transparency. The paper uses empirical material from a study of the development of an ‘‘ethical,’’ ‘‘smart’’ algorithmic videosurveillance system. The paper introduces the ‘‘ethical’’ algorithmic surveillance system, the approach to accountability developed, and some of the challenges of attempting algorithmic accountability in action. The paper concludes with reflections on future questions of algorithms and accountability.

Why Map Issues? On Controversy Analysis as a Digital Method

PDF Posté par : Dominique Cardon

This article takes stock of recent efforts to implement controversy analysis as a digital method in the study of science, technology, and society (STS) and beyond and outlines a distinctive approach to address the problem of digital bias. Digital media technologies exert significant influence on the enactment of controversy in online settings, and this risks undermining the substantive focus of controversy analysis conducted by digital means. To address this problem, I propose a shift in thematic focus from controversy analysis to issue mapping. The article begins by distinguishing between three broad frameworks that currently guide the development of controversy analysis as a digital method, namely, demarcationist, discursive, and empiricist. Each has been adopted in STS, but only the last one offers a digital ‘‘move beyond impartiality.’’ I demonstrate this approach by analyzing issues of Internet governance with the aid of the social media platform Twitter.

Governing Algorithms: Myth, Mess, and Methods

PDF Posté par : Dominique Cardon

Algorithms have developed into somewhat of a modern myth. On the one hand, they have been depicted as powerful entities that rule, sort, govern, shape, or otherwise control our lives. On the other hand, their alleged obscurity and inscrutability make it difficult to understand what exactly is at stake. What sustains their image as powerful yet inscrutable entities? And how to think about the politics and governance of something that is so difficult to grasp? This editorial essay provides a critical backdrop for the special issue, treating algorithms not only as computational artifacts but also as sensitizing devices that can help us rethink some entrenched assumptions about agency, transparency, and normativity.

The Ethics of Algorithms: Mapping the Debate

PDF Posté par : Nozha Boujemaa

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.algorithms, automation, big data, data analytics, data mining, ethics, machine learning

Big Data - A Tool for Inclusion or Exclusion?

Lien Posté par : Nozha Boujemaa

Executive Summary We are in the era of big data. With a smartphone now in nearly every pocket, a computer in nearly every household, and an ever-increasing number of Internet-connected devices in the marketplace, the amount of consumer data flowing throughout the economy continues to increase rapidly. The analysis of this data is often valuable to companies and to consumers, as it can guide the development of new products and services, predict the preferences of individuals, help tailor services and opportunities, and guide individualized marketing. At the same time, advocates, academics, and others have raised concerns about whether certain uses of big data analytics may harm consumers, particularly low- income and underserved populations. To explore these issues, the Federal Trade Commission (“FTC” or “the Commission”) held a public workshop, Big Data: A Tool for Inclusion or Exclusion?, on September 15, 2014. The workshop brought together stakeholders to discuss both the potential of big data to create opportunities for consumers and to exclude them from such opportunities. The Commission has synthesized the information from the workshop, a prior FTC seminar on alternative scoring products, and recent research to create this report. Though “big data” encompasses a wide range of analytics, this report addresses only the commercial use of big data consisting of consumer information and focuses on the impact of big data on low-income and underserved populations. Of course, big data also raises a host of other important policy issues, such as notice, choice, and security, among others. Those, however, are not the primary focus of this report. As “little” data becomes “big” data, it goes through several phases. The life cycle of big data can be divided into four phases: (1) collection; (2) compilation and consolidation; (3) analysis; and (4) use. This report focuses on the fourth phase and discusses the benefits and risks created by the use of big data analytics; the consumer protection and equal opportunity laws that currently apply to big data; research in the field of big data; and lessons that companies should take from the research. Ultimately, this report is intended to educate businesses on important laws and research that are relevant to big data analytics and provide suggestions aimed at maximizing the benefits and minimizing its risks.

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