The rise of foundation models that power the growth of generative AI and other AI use cases offers exciting possibilities—yet it also raises new questions and concerns about their ethical design, development, deployment, and use.

The IBM AI Ethics Board publication Foundation models: Opportunities, risks and mitigations addresses those concerns and explores the technology’s benefits, risks, guardrails, and mitigations.

The paper lays out the potential risks associated with foundation models through the lenses of ethics, laws, and regulations in three different categories:

1. Traditional. Known risks from prior or earlier forms of AI systems.
2. Amplified. Known risks now intensified because of intrinsic characteristics of foundation models, most notably their inherent generative capabilities.
3. New. Emerging risks intrinsic to foundation models and their inherent generative capabilities.

These risks are structured in relation to whether they are associated with content provided to the foundation model — the input — or the content generated by it — the output — or if they are related to additional challenges. They are presented in a table, which highlights why these risks are a concern and why it is important to consider these risks during the development, release, and use of foundation models.

In addition, this paper highlights some of the mitigation strategies and tools available such as the watsonx enterprise data and AI platform and open-source trustworthy AI tools. These strategies focus on balancing safety with innovation and allowing users to experience the power of AI and foundation models.

Foundation models: Opportunities, Risks and Mitigations will take you on a journey towards realizing the potential of foundation models, understanding the importance of the risks they could cause, and learning about strategies to mitigate their potential effects.