Quantifying Harm

In earlier work we defined a qualitative notion of harm: either harm is caused, or it is not. For practical applications, we often need to quantify harm; for example, we may want to choose the least harmful of a set of possible interventions. We first present a quantitative definition of harm in a deterministic context involving a single individual, then we consider the issues involved in dealing with uncertainty regarding the context and going from a notion of harm for a single individual to a notion of "societal harm", which involves aggregating the harm to individuals. We show that the "obvious" way of doing this (just taking the expected harm for an individual and then summing the expected harm over all individuals) can lead to counterintuitive or inappropriate answers, and discuss alternatives, drawing on work from the decision-theory literature.

Authors

• Sander Beckers
• Hana Chockler
• Joseph Y. Halpern

Keywords

• AI Ethics, Trust, Fairness: ETF: Ethical, legal and societal issues
• Uncertainty in AI: UAI: Causality, structural causal models and causal inference
• Uncertainty in AI: UAI: Decision and utility theory