Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI
Published in NeurIPS workshop Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI (EvalEval), 2024
Impact assessments have emerged as a common way to identify the negative and positive implications of AI deployment, with the goal of avoiding the downsides of its use. It is undeniable that impact assessments are important - especially in the case of rapidly proliferating technologies such as generative AI. But it is also essential to critically interrogate the current literature and practice on impact assessment, to identify its shortcomings, and to develop new approaches that are responsive to these limitations. In this provocation, we do just that by first critiquing the current impact assessment literature and then proposing a novel approach that addresses our concerns: Scenario-Based Sociotechnical Envisioning.
Recommended citation: Kieslich, K., Diakopoulos, N., & Helberger, N. (2024). Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI. NeurIPS workshop Evaluating Evaluations: Examining Best Practices for Measuring Broader Impacts of Generative AI (EvalEval). https://doi.org/10.48550/ARXIV.2410.23704
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