Quantifying Harm
Authors: Sander Beckers, Hana Chockler, Joseph Y. Halpern
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we extend our earlier definition so as to provide a quantitative notion of harm. The first step is relatively straightforward: we define a quantitative notion of harm in a deterministic setting. [...] We have given a formal definition of quantitative harm, based on our earlier definition of qualitative harm. [...] In fact, we prove that harm has the same complexity as causality in the full paper, that is, DP-complete [Beckers et al., 2022b]. |
| Researcher Affiliation | Academia | Sander Beckers1 , Hana Chockler2 and Joseph Y. Halpern3 1Institute for Logic, Language, and Computation, University of Amsterdam 2Department of Informatics, King s College London 3Computer Science Department, Cornell University |
| Pseudocode | No | The paper contains formal definitions and mathematical expressions but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training, validation, or testing. |
| Dataset Splits | No | The paper does not provide specific dataset split information as it does not conduct empirical experiments with datasets. |
| Hardware Specification | No | The paper does not report on experimental work, and therefore no specific hardware details are provided. |
| Software Dependencies | No | The paper focuses on theoretical definitions and conceptual discussions and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or training configurations. |