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  1. In AI we trust? Perceptions about automated decision-making by artificial intelligence.Theo Araujo, Natali Helberger, Sanne Kruikemeier & Claes H. de Vreese - 2020 - AI and Society 35 (3):611-623.
    Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial (...)
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  • Algorithmic Decision-Making and the Control Problem.John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan - 2019 - Minds and Machines 29 (4):555-578.
    The danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up (...)
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  • The ethics of algorithms: mapping the debate.Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter & Luciano Floridi - 2016 - Big Data and Society 3 (2):2053951716679679.
    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 (...)
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  • Foundations of Social Theory.James Samuel Coleman - 1990 - Belknap Press.
    Combining principles of individual rational choice with a sociological conception of collective action, James Coleman recasts social theory in a bold new way. The result is a landmark in sociological theory, capable of describing both stability and change in social systems. This book provides for the first time a sound theoretical foundation for linking the behavior of individuals to organizational behavior and then to society as a whole. The power of the theory is especially apparent when Coleman analyzes corporate actors, (...)
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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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  • Challenging algorithmic profiling: The limits of data protection and anti-discrimination in responding to emergent discrimination.Tobias Matzner & Monique Mann - 2019 - Big Data and Society 6 (2).
    The potential for biases being built into algorithms has been known for some time, yet literature has only recently demonstrated the ways algorithmic profiling can result in social sorting and harm marginalised groups. We contend that with increased algorithmic complexity, biases will become more sophisticated and difficult to identify, control for, or contest. Our argument has four steps: first, we show how harnessing algorithms means that data gathered at a particular place and time relating to specific persons, can be used (...)
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