The Black Box Society: The Secret Algorithms That Control Money and Information

By Frank A. Pasquale. 2015

2,164 citations recorded by the source corpus.

Subfields: Sociology and Political Science, AI Ethics, Fairness & Society

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Citations in the corpus

A Survey of Methods for Explaining Black Box Models (2018, background): “ can be deeply concealed within the trained classier, we risk to consider, maybe unconsciously, such practices and prejudices as general rules. We are warned about a growing \black box society" [74], governed by \secret algorithms protected by industrial secrecy, legal protections, obfuscation,”
Price of Transparency in Strategic Machine Learning (2016, background): “One approach recently proposed to limit such occurrences of invidious ML is to enforce some kind of ”transparency”, whereby the goal and inner-workings of the algorithm would be made public [2], [3].”
European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" (2016, background): “[27]) to caution against the rise of a “black box” society and demand increased transparency in algorithmic decision-making.”
How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016, background): “Pasquale (2015) argues for the use of auditors who have access to the code and can assure that classiﬁca-tions are non-discriminatory.”
Problem Formulation and Fairness (2019, methodology): “measurement, clean and process data, construct working models, and interpret quantified results [4,5,19,32,34–36].”
The ethics of algorithms: Mapping the debate (2016, neutral): “One possible path to explainability is algorithmic auditing carried out by data processors (Zarsky, 2016), external regulators (Pasquale, 2015; Tutt, 2016; Zarsky, 2016), or empirical researchers (Kitchin, 2016; Neyland, 2016), using ex post audit studies (Adler et al., 2016; Diakopoulos, 2015;…”
Explaining Explanations in AI (2018, background): “they have become even more important (Bodo et al., 2017; Kroll et al., 2016; Nissenbaum, 1996; Olhede and Wolfe, 2018; Pasquale, 2015; Selbst and Barocas, 2018; Veale and Edwards, 2018).”
Meaningful Explanations of Black Box AI Decision Systems (2019, background): “On the contrary, the last decade has witnessed the rise of a black box society (Pasquale 2015).”
Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability? (2017, background): “…the possible recipients (or beneficiaries) of disclosure are to be distinguished: intermediate bodies that have some oversight role, affected individuals, or the public in general — which obviously includes the former two categories (Zarsky 2013: 1532 – 1533; cf. also Pasquale 2015: Table 5.1).”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “As important responsibilities and processes are increasingly delegated to algorithmic decision-making systems [13, 56], more attention is being paid to algorithmic transparency [12, 43].”
Algorithms and their others: Algorithmic culture in context (2016, background): “For those for whom algorithmic practice potentially embodies an end-run around traditional forms of legislative accountability, this opacity is a severe problem, and some, such as Pasquale (2015), have argued that algorithms need to be available to audit.”
A systematic review of algorithm aversion in augmented decision making (2019, background): “More recently, however, fervent calls against opaque algocracy have occupied the limelight (e.g., Danaher, 2016; O'Neil, 2016; Pasquale, 2015).”
When the algorithm itself is a racist: Diagnosing ethical harm in the basic Components of Software (2016, background): “Research and writing is now blossoming among academics, journalists, and nonprofit organizations that criticizes (Gangadharan, 2014; Pasquale, 2015) and even reverse-engineers (Angwin, n.”
Value Alignment or Misalignment - What Will Keep Systems Accountable? (2017, background): “Demanding accessible and responsive ethical reasoning from a system is not merely a matter of transparency, of seeing how a system came to regard an action as the best one in a particular circumstance (Pasquale 2015).”
Communicating Algorithmic Process in Online Behavioral Advertising (2018, background): “Most algorithmic decision-making systems do not communicate their inner workings to users [39], resulting in information asymmetry—a disparity in what is visible to different parties to a system [34].”
Designing a Moral Compass for the Future of Computer Vision Using Speculative Analysis (2017, neutral): “…discusses in The Black Box Society, Occupy Wall Street already suffered from some of these conditions as Wall St. banks, unknown to activists, gave security camera footage to the FBI, allowing certain protesters to be identi-ﬁed and targeted [46], likely with the aid of face recognition software.”