Real-world harms caused by the use of AI technologies are widespread. Tracking and analyzing them improves our understanding of the variety of harms and the circumstances that lead to their occurrence once AI systems are deployed. This report presents a standardized conceptual framework for defining, tracking, classifying, and understanding harms caused by AI. It lays out the key elements required for the identification of AI harm, their basic relational structure, and definitions without imposing a single interpretation of AI harm. The brief concludes with an example of how to apply and customize the framework while keeping its modular structure.

As policymakers decide how best to regulate AI, they first need to grasp the different types of harm that various AI applications might cause at the individual, national, and even societal levels. To better understand AI harm, the blog presents some key components and characteristics.

Process frameworks provide a blueprint for organizations implementing responsible artificial intelligence (AI), but the sheer number of frameworks, along with their loosely specified audiences, can make it difficult for organizations to select ones that meet their needs. This report presents a matrix that organizes approximately 40 public process frameworks according to their areas of focus and the teams that can use them. Ultimately, the matrix helps organizations select the right resources for implementing responsible AI.

CSET's AI Assessment team provides a template that helps organizations create profiles to guide the management and deployment of AI systems in line with NIST's AI Risk Management Framework.

With the rapid integration of AI into our daily lives, we must all learn when and whether to trust the technology, understand its capabilities and limitations, and adapt as these systems — and our functional relationships with them — evolve.