Generative AI systems have various applications, including writing books and creating graphic designs. However, evaluating their ethical and social risks is crucial. This paper proposes a three-layered framework for evaluating these risks, focusing on AI system capability, human interaction, and systemic impacts. There are three main gaps in safety evaluations: context, specific risks, and multimodality. By repurposing existing evaluation methods and implementing a comprehensive approach, such as in the case study on misinformation, we can better assess the risks and ensure responsible development and deployment of AI systems.
Evaluating Social and Ethical Risks from Generative AI
Generative AI systems are already being used in various industries, such as writing books, creating graphic designs, and assisting medical practitioners. However, it is crucial to evaluate the potential ethical and social risks associated with these systems. In our paper, we propose a three-layered framework for evaluating these risks.
Three-Layered Framework for Evaluation
Our framework includes evaluations of AI system capability, human interaction, and systemic impacts. By assessing these aspects, we can ensure responsible development and deployment of AI systems.
Addressing Current Gaps
We have identified three main gaps in the current state of safety evaluations: context, specific risks, and multimodality. To close these gaps, we suggest repurposing existing evaluation methods for generative AI and implementing a comprehensive approach to evaluation.
Comprehensive Approach to Evaluation
Our approach integrates findings on AI system capability, user behavior, and contextual factors. By considering these multiple layers of evaluation, we can draw conclusions beyond model capability and determine whether harm, such as misinformation, actually occurs and spreads.
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