The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
Martin Jr., V
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Recent research on algorithmic fairness has highlighted that the problem formulation phase of ML system development can be a key source of bias that has significant downstream impacts on ML system fairness outcomes. However, very little attention has been paid to methods for improving the fairness efficacy of this critical phase of ML system development. Current practice neither accounts for the dynamic complexity of high-stakes domains nor incorporates the perspectives of vulnerable stakeholders. In this paper we introduce community based system dynamics (CBSD) as an approach to enable the participation of typically excluded stakeholders in the problem formulation phase of the ML system development process and facilitate the deep problem understanding required to mitigate bias during this crucial stage.
representative citing papers
A genetic algorithm exploring billions of policy combinations, scored by LLM-evaluated harm mitigation, expert cost, and participatory ratings, identifies viable AI policy options under different weighting schemes.
citing papers explorer
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Ethical and social risks of harm from Language Models
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
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Informing AI Policy Assessment using Large-Scale Simulation of Interventions
A genetic algorithm exploring billions of policy combinations, scored by LLM-evaluated harm mitigation, expert cost, and participatory ratings, identifies viable AI policy options under different weighting schemes.