Fairness Violations and Mitigation under Covariate Shift

Vishwali Mhasawade · Rumi Chunara

49 citations0/0 files storedACM Conference on Fairness, Accountability, and Transparency (FAccT)

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 Domain Adaptation and Few-Shot Learning Explainable Artificial Intelligence (XAI)

Abstract

We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set. Stability against changes in data distribution is an important mandate for responsible deployment of models. The domain adaptation literature addresses this concern, albeit with the notion of stability limited to that of prediction accuracy. We identify sufficient conditions under which stable models, both in terms of prediction accuracy and fairness, can be learned. Using the causal graph describing the data and the anticipated shifts, we specify an approach based on feature selection that exploits conditional independencies in the data to estimate accuracy and fairness metrics for the test set. We show that for specific fairness definitions, the resulting model satisfies a form of worst-case optimality. In context of a healthcare task, we illustrate the advantages of the approach in making more equitable decisions.

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 — 49 citations exist upstream.

References

2015Is Racism a Fundamental Cause of Inequalities in Health?2017Algorithmic Decision Making and the Cost of Fairness 2016To Predict and Serve?2019A comparative study of fairness-enhancing interventions in machine learning 2003Unequal Treatment 2019Path-Specific Counterfactual Fairness 2019Delayed Impact of Fair Machine Learning 2019Fairness Constraints: A Flexible Approach for Fair Classification

Cited by

2023Algorithmic fairness in artificial intelligence for medicine and healthcare 2022Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems 2023Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach 2021It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks 2024Operationalizing the Search for Less Discriminatory Alternatives in Fair Lending 2023What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

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.