Accountable Algorithms

By J. Reidenberg, D. G. Robinson, Harlan YUt. 2016

Subfields: AI Ethics, Fairness & Society

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

Citations in the corpus

Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (2018, background): “Transparency of datasets and systems is essential for preserving accountability and building more just systems (Kroll et al., 2017).”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…Fallon and Blaha, 2018; Hayes and Shah, 2017; Wachter, Mittelstadt, and Russel, 2017), • Accountability (Adler, et al., 2018; Belotti and Edwards, 2001; Kroll et al, 2016), • Safety and privacy (Amodei, Olah, Steinhardt, Christiano, Schulman, and Mané, 2016, Hajian, et al., 2015; Lum and Isaac,…”
Blind Justice: Fairness with Encrypted Sensitive Attributes (2018, background): “Desirable fairness and accountability applications we enable include: (Kroll et al., 2016), we efﬁciently provide for an individual to challenge a received outcome, verifying that it matches the outcome from the previously certiﬁed model.”
Meaningful Explanations of Black Box AI Decision Systems (2019, background): “This limitation impacts not only information ethics, but also accountability, safety and industrial liability (Danks and London 2017; Kingston 2016; Kroll et al. 2017).”
"The human body is a black box": supporting clinical decision-making with deep learning (2019, background): “At the same time, big data and machine learning rightly seem to present unique challenges with consequences for fairness, transparency, and accountability that require new analyses and governance principles [5, 6, 7].”
Equity Beyond Bias in Language Technologies for Education (2019, background): “Prior work has proposed regulatory frameworks that may serve as guidance (Whittaker et al., 2018); legal frameworks for these questions are already being developed (Kroll et al., 2016); bills are being introduced into the US Senate (Farivar, 2019).”
This Thing Called Fairness (2019, background): “The scientific interest in fairness, transparency, accountability, explainability, and other values in computer science has advanced the state of theory as well as application, engaging cutting edge research in cryptography [84], formal languages [1], and systems [63, 118].”
Measuring Fairness in Ranked Outputs (2016, background): “First, having insight into the properties of a ranked output, or more generally of an output of an algorithmic process, helps make the process transparent, interpretable and accountable [6, 8].”
Explanations in Autonomous Driving: A Survey (2021, background): “Refer to [32], [33], [34], [35] for further details on regulations and ethical guidelines.”
The growing ubiquity of algorithms in society: implications, impacts and innovations (2018, background): “The importance of accountability is highlighted in several recent reports [43,73,92].”
Trustworthy Artificial Intelligence: A Review (2022, background): “Kroll et al. [116] proposed a legal framework to ensure that the deployed model works within the specified boundaries.”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, background): “…machine learning, how can we make sure that the system does not explicitly, but more importantly, implicitly consider such protected classes [56, 91], for instance by using proxies which correspond to such protected classes (e.g. zip-codes as a stand-in for race and ethnicity, particularly in…”