Bias in data-driven artificial intelligence systems—An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Bias in data-driven artificial intelligence systems - An introductory survey

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

The Ethics of Doing Web Science Research

The panel continues the discussion of ethics within web science from previous years. Beyond fundamental notions such as privacy and intellectual property new challenges arise continuously for researchers as new technologies, platforms, contingencies and people become involved in building the web. Examples for ethical challenges include crowdsourcing as a method, social media data sharing, and, most recently, social media reliability. In the face of \u2018fake news\u2019, trolling, cyberbullying and automated content the question is whom to trust: are social media more or less reliable than traditional media? who should regulate their content? using which criteria? and what is the impact on research

Facebook Social Games

The popularity of Facebook social games is usually attributed to factors in the three areas of social interaction, play experience and the possibility for easy, casual play (Kinder-Kurlanda, 2012). It is, however, unclear, how these factors relate to the everyday lives of the largest user group, namely female players over the age of 35. An exploratory study of Australian players of Facebook social games was conducted to shed some light on gender-specific play behaviour in everyday circumstances (Willson, 2015a, 2015b). A similar study is planned for the German context

Death, Affect and the Ethical Challenges of Outing a Griefsquatter

The infamous Facebook emotion contagion experiment is one of the most prominent and best-known online experiments based on the concept of what we here call "living labs". In these kinds of experiments, real-world applications such as social web platforms trigger experimental switches inside their system to present experimental changes to their users - most of the time without the users being aware of their role as virtual guinea pigs. In the Facebook example the researches changed the way users' personal timeline was compiled to test the influence on the users' moods and feelings. The reactions to these experiments showed the inherent ethical issues such living labs settings bring up, mainly the study's lack of informed consent procedures, as well as a more general critique of the flaws in the experimental design. In this chapter, we describe additional use cases: The so-called living labs that focus on experimentation with information systems such as search engines and wikis and especially on their real-world usage. The living labs paradigm allows researchers to conduct research in real-world environments or systems. In the field of information science and especially information retrieval - which is the scientific discipline that is concerned with the research of search engines, information systems, and search related algorithms and techniques - it is still common practice to perform in vitro or offline evaluations using static test collections. Living labs are widely unknown or unavailable to academic researchers in these fields. A main benefit of living labs is their potential to offer new ways and possibilities to experiment with information systems and especially their users, but on the other hand they introduce a whole set of ethical issues that we would like to address in this chapter.Comment: to appear in: Zimmer, M., & Kinder-Kurlanda, K. (Eds.): Internet Research Ethics for the Social Age: New Challenges, Cases, and Contexts. Peter Lang (2017

WebSci '19 : Proceedings

It is our great pleasure to welcome you to the 11th ACM Conference on Web Science (Websci'19), June 30 -- July 3, 2019, Boston, MA, USA. This year, the conference theme is "Synergies for the Good: The Web and Society". We welcomed interdisciplinary contributions, especially those that had a broad perspective on the web, including those that combined analyses of web data with other types of data (e.g., from surveys or interviews) to better understand user behavior (online and offline); carried out longitudinal studies; presented successful cases of interdisciplinary and cross-disciplinary web research; used mixed-method approaches; critically reflected on the methods used; discussed responsible forms of Web Science (e.g. regarding standards, methods, generalizability of results); and/or those that reflected on the societal impact of web research, how the web is perceived in the media and in society, and whether this clashes with our self-image of Web Science. Thus, research on the interaction of society and the web was invited, and we received submissions highlighting web implications, synergies derived, and how the web as a socio-technical system will evolve in future. WebSci'19 was a unique conference where a multitude of disciplines converged in a creative and critical dialogue with the aim of understanding the web and its impacts. WebSci'19 welcomed participation from diverse fields including (but not limited to) art, anthropology, computer and information sciences, communication, economics, humanities, informatics, law, linguistics, philosophy, political science, psychology, and sociology. Following the tradition of earlier conferences, contributions to WebSci'19 aimed to cross traditional disciplinary boundaries. The community engaged with novel and thought-provoking ideas and discussed original research, work in progress, analysis, and practice in the field of Web Science, its current theoretical, methodological, and epistemological challenges as well as Web practices of individuals, collectives, institutions, and platforms. This year we were very pleased to accept 41 submissions for the regular research track chosen out of 130 submissions. We are grateful for the support of the Program Committee which consisted of 16 senior members and 66 regular members who selected an interesting, varied, exciting program comprising 31 long and 10 short papers. In addition to the posters selected from the Call for Posters, eight contributions were invited to be presented as a poster because they sparked fruitful discussions among the reviewers and were deemed to be of great interest and relevance to the WebSci'19 community. This year WebSci'19 encouraged authors to particularly prepare and publish reproducibility information of conducted research and resources, such as source code and datasets. Authors were asked to add (if possible) a link (e.g. DOI or URL) to data or any other information relevant to their submission. With this measure WebSci'19 aimed at raising awareness of the reproducibility issue and demonstrated that, as a community-driven conference, it subscribes to and actively promotes Open Science principles in resear

WebSci '17 : Proceedings

Welcome to Web Science! The ACM Web Science Conference 2017 is the 9th conference of the series (the 7th being sponsored by ACM-SIGWEB), and it is a unique venue where different disciplines are brought together in a creative and critical dialogue with the aim of understanding the Web. It is a very inclusive, interdisciplinary congress that welcomes participation from all fields including art, anthropology, computer and information sciences, communication, economics, humanities, informatics, law, linguistics, philosophy, political science, psychology, and sociology. In particular, this conference is a unicum in that it has a tradition for contributions that seek to cross traditional disciplinary boundaries. This year we were very pleased to receive 85 long paper submissions, 42 short paper submissions, and 32 extended abstract/poster submissions, totaling 159 candidate contributions. Given the high quality of submission, it has been a hard job to decide which of the contributions to select for the conference. We are grateful for the support of the Program Committee which consisted of 12 senior members and 66 regular members, plus 50 subreviewers for the research track and 23 regular members plus 3 subreviewers for the poster track. All PC members worked hard, producing 478 reviews and 127 meta-reviews; the process generated intense discussions, with more than 220 comments, based on which we could select an interesting, varied, exciting program comprising 30 long,15 short papers and 23 extended abstracts/posters

Fairness in agreement with European values: An interdisciplinary perspective on ai regulation

With increasing digitalization, Artificial Intelligence (AI) is becoming ubiquitous. AI-based systems to identify, optimize, automate, and scale solutions to complex economic and societal problems are being proposed and implemented. This has motivated regulation efforts, including the Proposal of an EU AI Act. This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them, focusing on (but not limited to) the Proposal. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. Then, we map these perspectives along three axes of interests: (i) Standardization vs. Localization, (ii) Utilitarianism vs. Egalitarianism, and (iii) Consequential vs. Deontological ethics which leads us to identify a pattern of common arguments and tensions between these axes. Positioning the discussion within the axes of interest and with a focus on reconciling the key tensions, we identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns. </p

Fairness in agreement with European values: An interdisciplinary perspective on ai regulation

With increasing digitalization, Artificial Intelligence (AI) is becoming ubiquitous. AI-based systems to identify, optimize, automate, and scale solutions to complex economic and societal problems are being proposed and implemented. This has motivated regulation efforts, including the Proposal of an EU AI Act. This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them, focusing on (but not limited to) the Proposal. We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives. Then, we map these perspectives along three axes of interests: (i) Standardization vs. Localization, (ii) Utilitarianism vs. Egalitarianism, and (iii) Consequential vs. Deontological ethics which leads us to identify a pattern of common arguments and tensions between these axes. Positioning the discussion within the axes of interest and with a focus on reconciling the key tensions, we identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information System