Fair, Transparent, and Accountable Algorithmic Decision-making Processes

By Bruno Lepri, Nuria Oliver, Emmanuel Letouzé, Alex Pentland, Patrick Vinck. 2017

622 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety Research, AI Ethics, Fairness & Society

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Citations in the corpus

Fairwashing: the risk of rationalization (2019, methodology): “The explainability requirement can be achieved by two common approaches, namely transparent-box design and black-box explanation (also called post-hoc explanation) [25, 22, 27, 16].”
Explaining Explanations in AI (2018, background): “parent by explanations at a minimum of three levels: “at the level of the entire model, at the level of individual components (e.g., parameters), and at the level of a particular training algorithm” (Lepri et al., 2017). A model, its component parts, or its training/learning algorithm can thus be sa”
Algorithms, Platforms, and Ethnic Bias: An Integrative Essay Forthcoming in Phylon: The Clark Atlanta University Review of Race and Culture (2018, neutral): “However, firms will resist because it is often in their best interest to protect their information and algorithmic decision-making processes, both legitimately to prevent gaming their processes and to prevent scrutiny (Citron & Pasquale 2016; Lepri et al., 2017).”
Transparency and explainability of AI systems: From ethical guidelines to requirements (2023, background): “The use of artificial intelligence (AI) is changing the world we live in [23].”
Bias in word embeddings (2020, background): “• What definitions of fairness and discrimination are appropriate and under what conditions? [15, 62] • At which part of an algorithm does bias emerge and in what form? [42, 85, 93] • What are the actual consequences of biased algorithms and who is accountable for them? [6, 68, 76] • How can researc”
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning (2019, background): “…design relevant human studies, both machine learning experts and human-computer interaction (HCI) researchers actively explore this area ( 10,44–46 ), and propose several metrics for human evaluation on general explanations from IML, such as mental mode ( 15, 47–50 ), human-machine performance…”
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence (2021, background): “…concretely, the development of explainable AI systems is motivated by several social and ethical reasons (see, for example, [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]): increasing societal acceptance of prediction-based decisions, establishing trust in the results of these decisions, making…”