TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI
This work addresses the need for a structured understanding of AI risks for researchers and policymakers, but it is incremental as it builds on existing risk identification efforts.
The paper tackles the problem of categorizing societal-scale and extinction-level risks from AI by proposing an exhaustive taxonomy based on accountability, including risks from unanticipated interactions and deliberate misuse, and provides illustrative stories to explore these risks.
While several recent works have identified societal-scale and extinction-level risks to humanity arising from artificial intelligence, few have attempted an {\em exhaustive taxonomy} of such risks. Many exhaustive taxonomies are possible, and some are useful -- particularly if they reveal new risks or practical approaches to safety. This paper explores a taxonomy based on accountability: whose actions lead to the risk, are the actors unified, and are they deliberate? We also provide stories to illustrate how the various risk types could each play out, including risks arising from unanticipated interactions of many AI systems, as well as risks from deliberate misuse, for which combined technical and policy solutions are indicated.