Responsibility and Blame: A Structural-Model Approach
Abstract
Causality is typically treated an all-or-nothing concept; either A is a cause of B or it is not. We extend the definition of causality introduced by Halpern and Pearl [2004a] to take into account the degree of responsibility of A for B. For example, if someone wins an election 11-0, then each person who votes for him is less responsible for the victory than if he had won 6-5. We then define a notion of degree of blame, which takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent.
Cite
Text
Chockler and Halpern. "Responsibility and Blame: A Structural-Model Approach." International Joint Conference on Artificial Intelligence, 2003. doi:10.1613/JAIR.1391Markdown
[Chockler and Halpern. "Responsibility and Blame: A Structural-Model Approach." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/chockler2003ijcai-responsibility/) doi:10.1613/JAIR.1391BibTeX
@inproceedings{chockler2003ijcai-responsibility,
title = {{Responsibility and Blame: A Structural-Model Approach}},
author = {Chockler, Hana and Halpern, Joseph Y.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2003},
pages = {147-153},
doi = {10.1613/JAIR.1391},
url = {https://mlanthology.org/ijcai/2003/chockler2003ijcai-responsibility/}
}