Work2018

Inherent Trade-Offs in Algorithmic Fairness

125 citations0/0 files storedACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Ethics and Social Impacts of AI Game Theory and Voting Systems Explainable Artificial Intelligence (XAI)

Abstract

Recent discussion in both the academic literature and the public sphere about classification by algorithms has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and discuss recent research establishing inherent trade-offs between these conditions. We also consider a variety of methods for promoting fairness and related notions for classification and selection problems that involve sets rather than just individuals.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 125 citations exist upstream.

References

2012Fairness through awareness 2014Big Data's Disparate Impact 2016Equality of Opportunity in Supervised Learning 2007The difference: how the power of diversity creates better groups, firms, schools, and societies 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2004Groups of diverse problem solvers can outperform groups of high-ability problem solvers 2015Certifying and Removing Disparate Impact 2017Algorithmic Decision Making and the Cost of Fairness

Cited by

2019Human Decision Making with Machine Assistance 2022In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits 2022Algorithmic fairness in business analytics: Directions for research and practice 2022The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.