Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Quantifying Harm
Authors: Sander Beckers, Hana Chockler, Joseph Y. Halpern
IJCAI 2023 | Venue PDF | 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. |