Discrimination préjudiciable/illicite, discrimination souhaitée/légale

Neutralité du Net : le débat est-il bien posé ?

Lien Posté par : Bruno Tuffin

Le 3 Avril 2014, le Parlement européen a apporté une définition « claire et sans ambiguïté » de la neutralité du Net ; il semble sous-entendu que la neutralité est préservée par ce texte de loi.

Data, Responsibly

PDF Posté par : Nozha Boujemaa

Big data technology promises to improve people’s lives, accelerate scientific discovery and innov- ation, and bring about positive societal change. Yet, if not used responsibly, large-scale data analysis and data-driven algorithmic decision-making can increase economic inequality, affirm systemic bias, and even destabilize global markets. While the potential benefits of data analysis techniques are well accepted, the importance of using them responsibly – that is, in accordance with ethical and moral norms, and with legal and policy considerations – is not yet part of the mainstream research agenda in computer science. Dagstuhl Seminar “Data, Responsibly” brought together academic and industry researchers from several areas of computer science, including a broad representation of data management, but also data mining, security/privacy, and computer networks, as well as social sciences researchers, data journalists, and those active in government think-tanks and policy initiatives. The goals of the seminar were to assess the state of data analysis in terms of fairness, transparency and diversity, identify new research challenges, and derive an agenda for computer science research and education efforts in responsible data analysis and use. While the topic of the seminar is transdisciplinary in nature, an important goal of the seminar was to identify opportunities for high-impact contributions to this important emergent area specifically from the data management community.

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.

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.

Monitoring the Errors of Discriminative Models with Probabilistic Programming

PDF Posté par : Nozha Boujemaa

Machine learning algorithms produce predictive models whose patterns of error can be difficult to summarize and predict. Specific types of error may be non-uniformly distributed over the space of input features, and this space itself may be only sparsely and non-uniformly represented in the training data. This abstract shows how to use BayesDB, a probabilistic programming platform for probabilistic data analysis, to simultaneously (i) learn a discriminative model for a specific prediction of interest, and (ii) build a non-parametric Bayesian generative “monitoring model” that jointly models the input features and the probable errors of the discriminative model.

The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making

PDF Posté par : Nozha Boujemaa

Machine learning methods are increasingly being used to inform, or sometimes even directly to make, important decisions about humans. A number of recent works have focussed on the fairness of the outcomes of such decisions, particularly on avoiding decisions that affect users of different sensitive groups (e.g., race, gender) disparately. In this paper, we propose to consider the fairness of the process of decision making. Process fairness can be measured by estimating the degree to which people consider various features to be fair to use when making an important legal decision. We examine the task of predicting whether or not a prisoner is likely to commit a crime again once released by analyzing the dataset considered by ProPublica relating to the COMPAS system. We introduce new measures of people’s discomfort with using various features, show how these measures can be estimated, and consider the effect of removing the uncomfortable features on prediction accuracy and on outcome fairness. Our empirical analysis suggests that process fairness may be achieved with little cost to outcome fairness, but that some loss of accuracy is unavoidable.

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.

Big data : A Tool for Inclusion or Exclusion?

PDF Posté par : Nozha Boujemaa

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.

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.

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.

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.

Does Google leverage market power through tying and bundling?

PDF Posté par : Dominique Cardon

I examine Google’s pattern and practice of tying to leverage its dominance into new sectors. In particular, I show how Google used these tactics to enter numer- ous markets, to compel usage of its services, and often to dominate competing offerings. I explore the technical and commercial implementations of these prac- tices and identify their effects on competition. I conclude that Google’s tying tactics are suspect under antitrust law.

The Trouble Algorithmic Decisions: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making

PDF Posté par : Dominique Cardon

We are currently witnessing a sharp rise in the use of algorithmic decision- making tools. In these instances, a new wave of policy concerns is set forth. This article strives to map out these issues, separating the wheat from the chaff. It aims to provide policy makers and scholars with a comprehensive framework for approaching these thorny issues in their various capacities. To achieve this objective, this article focuses its attention on a general analytical framework, which will be applied to a specific subset of the overall discussion. The analytical framework will reduce the discussion to two dimensions, every one of which addressing two central elements. These four factors call for a distinct discussion, which is at times absent in the existing literature. The two dimensions are the specific and novel problems the process assumedly generates and the specific attributes which exacerbate them. While the problems are articulated in a variety of ways, they most likely could be reduced to two broad categories: efficiency and fairness-based concerns. In the context of this discussion, such prob- lems are usually linked to two salient attributes the algorithmic processes feature—its opaque and automated nature.

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

Neutralité du Net : une approche plus large est nécessaire

Lien Posté par : Bruno Tuffin

Le 3 Avril 2014, le Parlement européen a apporté une définition « claire et sans ambiguïté » de la neutralité du Net. Cependant, en restant neutres vis-à-vis du débat sous-jacent, nous pensons que l’écosystème Internet est suffisamment complexe pour que la réponse proposée par la commission ne soit que partielle, et que le problème subsiste sous d’autres formes.

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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