Exploring a New Framework for Assessing Social and Ethical Risks in AI Systems
Introducing a context-based framework for comprehensively evaluating the social and ethical risks of AI systems
The world of AI is evolving rapidly, with generative systems now capable of tasks like writing books, designing graphics, and even assisting medical professionals. However, with great power comes great responsibility. It’s crucial to evaluate the ethical and social risks these AI systems may pose.
In our groundbreaking new paper, we propose a three-layered framework for assessing the social and ethical risks of AI systems. This framework delves into AI system capability, human interaction, and systemic impacts.
Identifying gaps in current safety evaluations, we emphasize the importance of context, specific risks, and multimodality. By repurposing existing methods and taking a multi-layered approach, we aim to provide a more comprehensive evaluation of potential risks such as misinformation.
Context is Key in AI Risk Assessment
Understanding the capabilities of AI systems is essential in predicting potential risks. Evaluating how these systems interact with humans and their impact on broader systems adds another layer of complexity to safety assessments.
Beyond assessing capabilities, we highlight the importance of human interaction and systemic impact evaluations. By looking at risks across these layers, we gain a holistic view of an AI system’s safety.
Building upon previous research, we stress the need to comprehensively evaluate risks associated with AI technologies, such as privacy concerns, misinformation, and job automation.
Shared Responsibility for Safety
Ensuring the safety of AI systems is a collaborative effort. AI developers, application developers, public authorities, and broader stakeholders all play a role in assessing and mitigating risks.
Identifying Gaps in AI Safety Evaluations
Through our research, we identified three key gaps in current safety evaluations of generative multimodal AI systems.
- Context: Existing evaluations often focus solely on AI capabilities, neglecting human interaction and systemic impact risks.
- Risk-specific evaluations: Evaluation frameworks lack coverage in various risk areas and tend to define harm narrowly.
- Multimodality: Safety evaluations predominantly focus on text output, leaving gaps in assessing risks related to other modalities like images, audio, and video.
We invite contributions to our repository of safety evaluations of generative AI systems, aiming to address these gaps in assessments.
Implementing Comprehensive Evaluations
As AI systems continue to advance, it’s imperative to conduct rigorous safety evaluations that consider all aspects of usage and impact. By repurposing existing evaluations and exploring new approaches, we can create a robust evaluation ecosystem for safe AI systems.
The responsibility for ensuring AI system safety rests on all stakeholders, emphasizing the need for collaboration and innovation in evaluation practices.