Results 11 to 20 of about 4,233,926 (393)
Large Language Models are not Fair Evaluators [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models.
Peiyi Wang +8 more
semanticscholar +1 more source
Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation [PDF]
ACM Conference on Recommender Systems, 2023 The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm — Recommendation via LLM (RecLLM).
Jizhi Zhang +5 more
semanticscholar +1 more source
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness [PDF]
arXiv.org, 2023 Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications. However, since they are highly data-driven, relying on billion-sized datasets randomly scraped from the ...
Felix Friedrich +6 more
semanticscholar +1 more source
Foundation Models and Fair Use [PDF]
Journal of machine learning research, 2023 Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation.
Peter Henderson +5 more
semanticscholar +1 more source
Learning Fair Node Representations with Graph Counterfactual Fairness [PDF]
Web Search and Data Mining, 2022 Fair machine learning aims to mitigate the biases of model predictions against certain subpopulations regarding sensitive attributes such as race and gender.
Jing Ma +5 more
semanticscholar +1 more source
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments [PDF]
Big Data, 2016 Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. Although such instruments are gaining increasing popularity across the country, their
Alexandra Chouldechova
semanticscholar +1 more source
Fair Allocation of Scarce Medical Resources in the Time of Covid-19.
New England Journal of Medicine, 2020 Allocating Scarce Medical Resources for Covid-19 The Covid-19 pandemic has already stressed health care systems throughout the world, requiring rationing of medical equipment and care.
E. Emanuel +9 more
semanticscholar +1 more source
Federated Learning with Fair Averaging [PDF]
International Joint Conference on Artificial Intelligence, 2021 Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the magnitudes. To address this issue, we propose the federated fair averaging
Z. Wang +5 more
semanticscholar +1 more source
Operationalizing the CARE and FAIR Principles for Indigenous data futures
Scientific Data, 2021 As big data, open data, and open science advance to increase access to complex and large datasets for innovation, discovery, and decision-making, Indigenous Peoples’ rights to control and access their data within these data environments remain limited ...
S. Carroll +4 more
semanticscholar +1 more source
Learning Fair Representations for Recommendation: A Graph-based Perspective [PDF]
The Web Conference, 2021 As a key application of artificial intelligence, recommender systems are among the most pervasive computer aided systems to help users find potential items of interests.
Le Wu +5 more
semanticscholar +1 more source