Algorithmic Accountability

By Nicholas A. Diakopoulos. 2015

535 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Safety Research

View Paper: https://www.semanticscholar.org/paper/89e2834958529f739262d15031bbb33c1160bbf0

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Citations in the corpus

Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction (2019, background): “In addition, more recently, the US government has introduced a new bill called the ”Algorithmic Accountability Act” [1] which would require companies to assess their machine learning systems for bias and discrimination and take corrective measures.”
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, methodology): “…discussion and re-evaluation of the ethical implications of using a specific dataset for an ML development project, and the audit methodologies of (Diakopoulos 2015) ensure that external voices, who may have an opposing view as to whether or not an ML-system 1 3 From What to How: An Initial…”
Fair, Transparent, and Accountable Algorithmic Decision-making Processes (2017, background): “Categorization can be considered as a form of direct discrimination, whereby algorithms are used for disparate treatment [19].”
Artificial intelligence and algorithmic bias: implications for health systems (2019, background): “For many, the concern is not only that “algorithms are for the most part reflecting back the bias in our world” [ 5 ], but that they are doing so at potentially massive scale and without due oversight.”
The ethics of algorithms: Mapping the debate (2016, unknown): “…2015; Tutt, 2016; Zarsky, 2016), or empirical researchers (Kitchin, 2016; Neyland, 2016), using ex post audit studies (Adler et al., 2016; Diakopoulos, 2015; Kitchin, 2016; Romei and Ruggieri, 2014; Sandvig et al., 2014), reﬂexive ethnographic studies in development and testing (Neyland,…”
Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development (2020, methodology): “For example, if the input data of an algorithm include implicit or explicit human judgments, stereotypes, or biases, an accurate algorithmic output will inevitably entail these human judgments, stereotypes, and prejudices (Diakopoulos 2015; Suresh and Guttag 2019; Barfield and Pagallo 2018).”
This Thing Called Fairness (2019, background): “plish a deﬁned outcome”) but considers them in the broad sense (saying “algorithms can arguably make mistakes and operate with biases”, which does not make sense for the narrower technical deﬁnition) [35]. 9TheuseofCOMPASinothercontexts,suchasatsentencing,hasalsobeensubjecttomuchofthesametechnicalcr”
ORES (2019, background): “ORES implements several of the dominant recommendations for algorithmic system builders around transparency and community consent [19, 22, 89].”
Algorithmic Accountability and Public Reason (2017, background): “In this way, decisions based on machine learning algorithms might end up reinforcing underlying social inequalities (Kamiran & Calders, 2012; Bozdag, 2013; Diakopoulos, 2015; Sandvig, Hamilton, Karahalios, & Langbort, 2014; Barocas & Selbst, 2016).”
A Qualitative Exploration of Perceptions of Algorithmic Fairness (2018, background): “A wide range of techniques have been proposed to scrutinize algorithms, such as model interpretability, audits, expert analysis, and reverse engineering [22,42,76,77].”