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Fairness in Large Language Models: A Taxonomic Survey
Additionally, the concept of fairness in LLMs is discussed categorically, summarizing metrics for evaluating bias in LLMs and existing algorithms for promoting fairness.
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and protection that impact professionals across various careers.
History, Development, and Principles of Large Language Models-An Introductory Survey
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.
Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern.
Preventing Discriminatory Decision-making in Evolving Data Streams
However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting.
Individual Fairness under Uncertainty
Algorithmic fairness, the research field of making machine learning (ML) algorithms fair, is an established area in ML.
