Algorithmic Accountability and Public Reason

By Reuben Binns. 2017

275 citations recorded by the source corpus.

The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system’s outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of ‘public reason’. I argue that situating demands for algorithmic accountability within this justificatory framework enables us to better articulate their purpose and assess the adequacy of efforts toward them.

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Citations in the corpus

Explaining Explanations in AI (2018, background): “Justification has recently received increasing attention by scholars discussing algorithmic accountability (e.g. Binns (2017); Biran and McKeown (2014); Hildebrandt and Koops (2010); Selbst and Barocas (2018); Weller (2017)).”
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, background): “In this sense, the tools and methodologies represent a pragmatic version of Habermas’s discourse ethics 22 (Mingers and Walsham 2010).”
WeBuildAI (2019, background): “One way to avoid this is to limit participation so that democratic control of algorithms is subject to the constraints of public reason [10, 65].”
Accountability in artificial intelligence: what it is and how it works (2023, unknown): “: because the agent may have to provide pieces of evidence, explanations and justifications to objections on both technical and social sources of conduct (Binns 2018).”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, unknown): “27 academic articles [8, 9, 17, 20, 22, 28, 35, 48, 52, 58, 61, 65, 86, 90, 93, 98, 106, 110, 111, 120, 124, 135, 139–141, 143, 144] which were found to be relevant to the topic prior to the start of the systematic review were assessed for their keywords.”
Race After Technology: Abolitionist Tools for the New Jim Code (2020, background): “Some of the recent work in critical data studies is aligned with this line of thinking and proposes that moving toward more desirable futures entails revising the current focus on individual account-abilities by, e.g., considering ways to enact public reason (Binns 2018).”
Beyond the individual: governing AI's societal harm (2021, unknown): “This risks diminishing the accountability of public decision-makers for the legality of such decisions, and thereby undermining the broader societal interest of the rule of law (Binns, 2018; Zalnieriute et al., 2019; Buchholtz, 2020).”
The Illusion of Control: Placebo Effects of Control Settings (2018, background): “Relatedly, explorations of the ethics of misrepresentative control settings might fit well within emerging conversations about the ethics of algorithmic systems [5, 11, 13, 44].”
Trust in AI: progress, challenges, and future directions (2024, unknown): “Within this project, the so-called “ right to explanation ” has been central (Ahn et al., 2021; Alam and Mueller, 2021; Ausloos et al., 2020; Binns, 2018; Bovens et al., 2014; Buçinca et al., 2021; Doshi-Velez et al., 2016; Feldman et al., 2019; Smith-Renner et al., 2020; Sperrle et al., 2020;…”
Confronting Power and Corporate Capture at the FAccT Conference (2022, background): “Philosophers of technology and legal scholars have also appealed for standards of accountability and that access to recourse be provided to persons affected by algorithmic decisions based on unfavorable results [10, 63].”
From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, background): “A way forward Social scientists (Matzner, 2014) and political philosophers (from Rousseau and Kant, to Rawls and Habermas) (Binns, 2018a), are used to dealing with the kind of plurality outlined in section 4, and to thinking about the interaction between individual level and group level ‘ethics.”
The ethics of algorithms: key problems and solutions (2020, methodology): “The second approach proposes a collaborative knowledge-based method which would focus on community-driven data resources containing practical experiences of ML and modelling (Veale and Binns 2017; Katell et al. 2020).”