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Found 11 papers, 3 papers with code
The Intersectionality Problem for Algorithmic Fairness
A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such fairness has been attained.
STAR: SocioTechnical Approach to Red Teaming Language Models
This research introduces STAR, a sociotechnical framework that improves on current best practices for red teaming safety of large language models.
The Impossibility of Fair LLMs
The need for fair AI is increasingly clear in the era of general-purpose systems such as ChatGPT, Gemini, and other large language models (LLMs).
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation
Standard benchmarks of bias and fairness in large language models (LLMs) measure the association between social attributes implied in user prompts and short LLM responses.
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina
In this paper, we quantify access to polling locations, developing a methodology for the calibrated measurement of racial disparities in polling location "load" and distance to polling locations.
De-biasing "bias" measurement
When a model's performance differs across socially or culturally relevant groups--like race, gender, or the intersections of many such groups--it is often called "biased."
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
Estimating the number of SARS-CoV-2 infections and the impact of social distancing in the United States
Understanding the number of individuals who have been infected with the novel coronavirus SARS-CoV-2, and the extent to which social distancing policies have been effective at limiting its spread, are critical for effective policy going forward.
Applications Populations and Evolution
A statistical framework for fair predictive algorithms
We propose a method to remove bias from predictive models by removing all information regarding protected variables from the permitted training data.
