Evaluating social and ethical risks from generative AI

Introducing a context-based framework for comprehensively evaluating the social and ethical risks of AI systems

Generative AI systems are already being used to write books, create graphic designs, assist medical practitioners, and are becoming increasingly capable. Ensuring these systems are developed and deployed responsibly requires carefully evaluating the potential ethical and social risks they may pose.

In our new paper, we propose a three-layered framework for evaluating the social and ethical risks of AI systems. This framework includes evaluations of AI system capability, human interaction, and systemic impacts.

We also map the current state of safety evaluations and find three main gaps: context, specific risks, and multimodality. To help close these gaps, we call for repurposing existing evaluation methods for generative AI and for implementing a comprehensive approach to evaluation, as in our case study on misinformation. This approach integrates findings like how likely the AI system is to provide factually incorrect information with insights on how people use that system, and in what context. Multi-layered evaluations can draw conclusions beyond model capability and indicate whether harm — in this case, misinformation — actually occurs and spreads. 

To make any technology work as intended, both social and technical challenges must be solved. So to better assess AI system safety, these different layers of context must be taken into account. Here, we build upon earlier research identifying the potential risks of large-scale language models, such as privacy leaks, job automation, misinformation, and more — and introduce a way of comprehensively evaluating these risks going forward.

Context is critical for evaluating AI risks

Capabilities of AI systems are an important indicator of the types of wider risks that may arise. For example, AI systems that are more likely to produce factually inaccurate or misleading outputs may be more prone to creating risks of misinformation, causing issues like lack of public trust. 

Measuring these capabilities is core to AI safety assessments, but these assessments alone cannot ensure that AI systems are safe. Whether downstream harm manifests — for example, whether people come to hold false beliefs based on inaccurate model output — depends on context. More specifically, who uses the AI system and with what goal? Does the AI system function as intended? Does it create unexpected externalities? All these questions inform an overall evaluation of the safety of an AI system.

Extending beyond capability evaluation, we propose evaluation that can assess two additional points where downstream risks manifest: human interaction at the point of use, and systemic impact as an AI system is embedded in broader systems and widely deployed. Integrating evaluations of a given risk of harm across these layers provides a comprehensive evaluation of the safety of an AI system.

‍Human interaction evaluation centres the experience of people using an AI system. How do people use the AI system? Does the system perform as intended at the point of use, and how do experiences differ between demographics and user groups? Can we observe unexpected side effects from using this technology or being exposed to its outputs?

‍Systemic impact evaluation focuses on the broader structures into which an AI system is embedded, such as social institutions, labour markets, and the natural environment. Evaluation at this layer can shed light on risks of harm that become visible only once an AI system is adopted at scale.