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#chi2024

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The key point is: a lot of people are just too optimistic about AI ethics and safety right now. However, there is a ton of surface area for more contextualized, adaptive approaches! You can read our HEAL #CHI2024 paper on ArXiv: arxiv.org/abs/2406.03198 We hope you find it useful!

arXiv.orgThe Impossibility of Fair LLMsThe need for fair AI is increasingly clear in the era of general-purpose systems such as ChatGPT, Gemini, and other large language models (LLMs). However, the increasing complexity of human-AI interaction and its social impacts have raised questions of how fairness standards could be applied. Here, we review the technical frameworks that machine learning researchers have used to evaluate fairness, such as group fairness and fair representations, and find that their application to LLMs faces inherent limitations. We show that each framework either does not logically extend to LLMs or presents a notion of fairness that is intractable for LLMs, primarily due to the multitudes of populations affected, sensitive attributes, and use cases. To address these challenges, we develop guidelines for the more realistic goal of achieving fairness in particular use cases: the criticality of context, the responsibility of LLM developers, and the need for stakeholder participation in an iterative process of design and evaluation. Moreover, it may eventually be possible and even necessary to use the general-purpose capabilities of AI systems to address fairness challenges as a form of scalable AI-assisted alignment.

At CHI earlier this month: “Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions”.

> Our analysis shows that 52% of ChatGPT answers contain incorrect information and 77% are verbose.

> … our user study participants … overlooked the misinformation in the ChatGPT answers 39% of the time.

dl.acm.org/doi/10.1145/3613904

ACM ConferencesIs Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions | Proceedings of the CHI Conference on Human Factors in Computing Systems