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  1. A Theory of Justice: Revised Edition.John Rawls - 1999 - Harvard University Press.
    Previous edition, 1st, published in 1971.
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  • Thinking, Fast and Slow.Daniel Kahneman - 2011 - New York: New York: Farrar, Straus and Giroux.
    In the international bestseller, Thinking, Fast and Slow, Daniel Kahneman, the renowned psychologist and winner of the Nobel Prize in Economics, takes us on a groundbreaking tour of the mind and explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. The impact of overconfidence on corporate strategies, the difficulties of predicting what will make us happy in the future, the profound effect of cognitive (...)
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  • Theorie des Kommunikativen Handelns.Jürgen Habermas - 1981
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  • AI4People—an ethical framework for a good AI society: opportunities, risks, principles, and recommendations.Luciano Floridi, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, Burkhard Schafer, Peggy Valcke & Effy Vayena - 2018 - Minds and Machines 28 (4):689-707.
    This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...)
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  • Explaining Explanations in AI.Brent Mittelstadt - forthcoming - FAT* 2019 Proceedings 1.
    Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...)
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  • The Emotional Dog and Its Rational Tail: A Social Intuitionist Approach to Moral Judgment.Jonathan Haidt - 2001 - Psychological Review 108 (4):814-834.
    Research on moral judgment has been dominated by rationalist models, in which moral judgment is thought to be caused by moral reasoning. The author gives 4 reasons for considering the hypothesis that moral reasoning does not cause moral judgment; rather, moral reasoning is usually a post hoc construction, generated after a judgment has been reached. The social intuitionist model is presented as an alternative to rationalist models. The model is a social model in that it deemphasizes the private reasoning done (...)
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  • Big Data: A Revolution That Will Transform How We Live, Work, and Think.V. Mayer-Schoenberger & K. Cukier - unknown
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  • Trends in Memory Development Research.Lawrence Kohlberg, Charles G. Levine & Alexandra Hewer - 1983 - S Karger.
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  • Toward an Ethics of AI Assistants: an Initial Framework.John Danaher - 2018 - Philosophy and Technology 31 (4):629-653.
    Personal AI assistants are now nearly ubiquitous. Every leading smartphone operating system comes with a personal AI assistant that promises to help you with basic cognitive tasks: searching, planning, messaging, scheduling and so on. Usage of such devices is effectively a form of algorithmic outsourcing: getting a smart algorithm to do something on your behalf. Many have expressed concerns about this algorithmic outsourcing. They claim that it is dehumanising, leads to cognitive degeneration, and robs us of our freedom and autonomy. (...)
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  • (1 other version)Social space and symbolic power.Pierre Bourdieu - 1989 - Sociological Theory 7 (1):14-25.
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  • Du musst dein Leben ändern: über Anthropotechnik.Peter Sloterdijk - 2009 - Frankfurt am Main: Suhrkamp.
    Der Mensch als Übender, als sich durch Übungen selbst erzeugendes Wesen - Rainer Maria Rilke hat den Antrieb zu solchen Exerzitien zu Beginn des 20. Jahrhunderts in die Form gefaßt: "Du mußt dein Leben ändern." In seinem Plädoyer für die Ausweitung der Übungszone des einzelnen wie der Gesellschaft entwirft Peter Sloterdijk eine grundlegende und grundlegend neue Anthropologie. Den Kern seiner Wissenschaft vom Menschen bildet die Einsicht von der Selbstbildung alles Humanen. Seine Aktivitäten wirken unablässig auf ihn zurück: die Arbeit auf (...)
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  • Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data.Reuben Binns & Michael Veale - 2017 - Big Data and Society 4 (2):205395171774353.
    Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...)
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  • Algorithmic governance: Developing a research agenda through the power of collective intelligence.Kalpana Shankar, Burkhard Schafer, Niall O'Brolchain, Maria Helen Murphy, John Morison, Su-Ming Khoo, Muki Haklay, Heike Felzmann, Aisling De Paor, Anthony Behan, Rónán Kennedy, Chris Noone, Michael J. Hogan & John Danaher - 2017 - Big Data and Society 4 (2).
    We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic (...)
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  • Data Derivatives.Louise Amoore - 2011 - Theory, Culture and Society 28 (6):24-43.
    In a quiet London office, a software designer muses on the algorithms that will make possible the risk flags to be visualized on the screens of border guards from Heathrow to St Pancras International. There is, he says, ‘real time decision making’ – to detain, to deport, to secondarily question or search – but there is also the ‘offline team who run the analytics and work out the best set of rules’. Writing the code that will decide the association rules (...)
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  • Predictive privacy: towards an applied ethics of data analytics.Rainer Mühlhoff - 2021 - Ethics and Information Technology 23 (4):675-690.
    Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or (...)
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  • Privacy and Freedom.Alan F. Westin - 1970 - Science and Society 34 (3):360-363.
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  • Paternalism.Rolf Sartorius - 1983 - Ethics 95 (2):353-354.
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  • Why we should have seen that coming.M. J. Wolf, K. Miller & F. S. Grodzinsky - 2017 - Acm Sigcas Computers and Society 47 (3):54-64.
    In this paper we examine the case of Tay, the Microsoft AI chatbot that was launched in March, 2016. After less than 24 hours, Microsoft shut down the experiment because the chatbot was generating tweets that were judged to be inappropriate since they included racist, sexist, and anti-Semitic language. We contend that the case of Tay illustrates a problem with the very nature of learning software that interacts directly with the public, and the developer's role and responsibility associated with it. (...)
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  • Thinking critically about and researching algorithms.Rob Kitchin - 2017 - Information, Communication and Society 20 (1):14-29.
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  • Autonomous vehicle safety: An interdisciplinary challenge.P. Koopman & M. Wagner - 2017 - IEEE Intelligent Transportation Systems Magazine 9.
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  • I, Robot.Isaac Asimov - 1950 - Doubleday.
    A classic collection of interlocking tales chronicles the near-future development of the robot and features models that have the ability to read minds, experience human emotions, and take over the world--and, perhaps, render humankind itself obsolete. Reprint. 25,000 first printing.
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  • Luhmann, N. Social Systems. [REVIEW]N. Luhmann, John Bednarz & Dirk Baecker - 1998 - Human Studies 21 (2):227-234.
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  • From privacy to anti-discrimination in times of machine learning.Thilo Hagendorff - 2019 - Ethics and Information Technology 21 (4):331-343.
    Due to the technology of machine learning, new breakthroughs are currently being achieved with constant regularity. By using machine learning techniques, computer applications can be developed and used to solve tasks that have hitherto been assumed not to be solvable by computers. If these achievements consider applications that collect and process personal data, this is typically perceived as a threat to information privacy. This paper aims to discuss applications from both fields of personality and image analysis. These applications are often (...)
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  • Three fallacies of digital footprints.Kevin Lewis - 2015 - Big Data and Society 2 (2).
    “Digital footprints” is an attractive, useful, and increasingly popular metaphor for thinking about Big Data. In this essay, I elaborate on this metaphor to highlight three relatively basic fallacies in the way we tend to think about Big Data: first, that they contain information on complete populations, or “N = all”; second, that they contain recordings of naturalistic behavior; and third, that they can be understood devoid of context.
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  • Anmerkungen der Bände Vi–Ix.Immanuel Kant - 1977 - De Gruyter.
    Diese broschierte Textausgabe enthält den photomechanischen Nachdruck von Abt. 1 der Akademie Ausgabe Kants. Hier sind in der noch heute maßgeblichen kritischen Edition die Werke Kants mit sachlichen Erläuterungen und textkritischem Apparat zugänglich.
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  • Theorie des kommunikativen Handelns, 2 bd : bd 1. : Handlungsrationalität und gesellschaftliche Rationalisierung ; bd 2. : Zur Kritik der funktionalistischen Vernunft. [REVIEW]Jürgen Habermas - 1987 - Revue Philosophique de la France Et de l'Etranger 177 (4):557-558.
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  • The hidden faces of automation.Lilly Irani - 2016 - XRDS: Crossroads, The ACM Magazine for Students 23 (2):34-37.
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  • Maschinelles Lernen und Diskriminierung: Probleme und Lösungsansätze.T. Hagendorff - 2019 - Österreichische Zeitschrift Für Soziologie 44:53-66.
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