A Causal Analysis of Harm
Authors: Sander Beckers, Hana Chockler, Joseph Halpern
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality [13]. The key features of our definition are that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems. |
| Researcher Affiliation | Academia | Sander Beckers Cluster of Excellence in Machine Learning University of Tübingen and Munich Center for Mathematical Philosophy, LMU srekcebrednas@gmail.com Hana Chockler causa Lens and Department of Informatics King s College London hana.chockler@kcl.ac.uk Joseph Y. Halpern Computer Science Department Cornell University halpern@cs.cornell.edu |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use datasets in the context of training or evaluation, therefore no public dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits, so no training/test/validation splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or system-level training settings. |