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Research

11.19.2021

The meaning and measurement of bias: lessons from natural language processing – Abigail Z. Jacobs, Su Lin Blodgett, Solon Barocas, Hal Daumé, and Hanna Wallach

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
https://dl.acm.org/doi/abs/10.1145/3351095.3375671

Insights

The recent interest in identifying and mitigating bias in computational systems has introduced a wide range of different—and occasionally incomparable—proposals for what constitutes bias in such systems. This tutorial introduces the language of measurement modeling from the quantitative social sciences as a framework for examining how social, organizational, and political values enter computational systems and unpacking the varied normative concerns operationalized in different techniques for measuring “bias.” We show that this framework helps to clarify the way unobservable theoretical constructs—such as “creditworthiness,” “risk to society,” or “tweet toxicity”—are turned into measurable quantities and how this process may introduce fairness-related harms. In particular, we demonstrate how to systematically assess the construct validity and reliability of these measurements to detect and characterize specific types of harms, which arise from mismatches between constructs and their operationalizations. We then take a critical look at existing approaches to examining “bias” in NLP models, ranging from work on embedding spaces to machine translation and hate speech detection.

07.29.2021

Towards Understanding and Mitigating Social Biases in Language Models – Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov

Proceedings of the 38th International Conference on Machine Learning
https://arxiv.org/pdf/2106.13219.pdf

Insights

As machine learning methods are deployed in real world settings such as healthcare, legal systems, and social science, it is crucial to recognize how they shape social biases and stereotypes in these sensitive decision-making processes. Among such real-world deployments are large-scale pretrained language models (LMs) that can be potentially dangerous in manifesting undesirable representational biases- harmful biases resulting from stereotyping that propagate negative generalizations involving gender, race, religion, and other social constructs.

04.21.2020

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle – Harini Suresh and John Guttag

EAAMO’21, October 5–9, 2021,–, NY, USA
https://arxiv.org/pdf/1901.10002.pdf

Insights

As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable downstream consequences, it is critical that we understand when and how harm might be introduced throughout the ML life cycle. In this paper, we provide a framework that identifies seven distinct potential sources of downstream harm in machine learning, spanning data collection, development, and deployment. In doing so, we aim to facilitate more productive and precise communication around these issues, as well as more direct, application-grounded ways to mitigate them.