A snapshot of the frontiers of fairness in machine learning

By Alexandra Chouldechova, Aaron Roth. Communications of the ACM, 2020

381 citations recorded by the source corpus.

A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/48336cd6e6aa2e94446e2be6bbbb7ce2c3815c5c

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Citations in the corpus

Ethical Machine Learning in Healthcare (2020, background): “quot; (based on some sensitive attribute) by requiring that a classier performance metric be balanced across 12 Chen, I.Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., Ghassemi, M. those groups. (116, 117). For instance, a model may be partially assessed by calculating the true positive rate sep”
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models (2022, background): “Classical fairness metrics [25, 29, 75] usually require specifying a protected attribute, such as sexual orientation or race.”
Limits and Possibilities for “Ethical AI” in Open Source: A Study of Deepfakes (2022, methodology): “We define Implementation harms as those arising through code, algorithm or data problems that can be fixed without changing the intent, or use, of the software, for example through the use of “de-biasing” techniques to reduce bias in algorithms or training data [9, 18, 78].”
Data collection and quality challenges in deep learning: a data-centric AI perspective (2021, methodology): “surveys [18,131,38,87] used for predicting crime, hiring, giving loans, and more.”
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics (2021, background): “Surveys of Mehrabi et al. (2019), Hutchinson and Mitchell (2019) and Chouldechova and Roth (2020) cover a broad scope of literature on algorithmic fairness.”
Copyright in generative deep learning (2021, neutral): “…debates have, fortunately, found a place in the conversation (for an interesting summary of machine learning researches related to fairness, see Chouldechova and Roth (2020)) because of biases and discrimination they may cause (as happened with AI Portrait Ars [O ’ Leary, 2019], leading to…”