2026-10-13T11:21:24Z https://api.figshare.com/v2/oai

oai:figshare.com:article/31742617 2026-09-21T09:47:36Z category_29236 category_28864 category_29053 item_type_3 month_year_09_2026

Code, raw results, and statistical tables for: Translating Günther Anders’ Critical Theory into Algorithmic Fairness: A Simulation-Based Proof of Concept for Fairness-Enhanced Decision Support with Simulated Human Review Ying Wang (23471791) Wenjun Tian (8923925) Other information and computing sciences not elsewhere classified Artificial intelligence not elsewhere classified Fairness, accountability, transparency, trust and ethics of computer systems algorithmic fairness human-in-the-loop simulation study post-processing disparate impact German Credit Günther Anders reproducibility This item contains the source code, the simulated-data generator, the raw per-seed results, and the regenerated statistical tables underlying the article "Translating Günther Anders’ Critical Theory into Algorithmic Fairness: A Simulation-Based Proof of Concept for Fairness-Enhanced Decision Support with Simulated Human Review" (Tian W, Wang Y; submitted to PLOS One). It is a snapshot of the GitHub repository https://github.com/YingWang08/anders-fairness-hitl, the version cited in the article. About the studyThe study is a simulation-based proof of concept. All "human" decisions are generated by fixed rule-based proxies; no human participants were involved. A fairness-enhanced, HITL-inspired post-processing framework (FE-HITL) is evaluated on a simulated agricultural resource-allocation task (30 independently generated datasets of 10,000 farmers) and on the UCI German Credit benchmark (50 resampled splits and 5-fold cross-validation). Contents- experiments/run_sensitivity_standalone.py: the eight agricultural analysis suites. The simulated data are generated in memory by generate_agricultural_data_biased(), generate_agricultural_data_biased_v2(), and generate_full_dataset(); no data file is needed.- experiments/run_credit_clean.py: the German Credit experiments (data loaded from OpenML, data ID 31; a copy of the UCI data file is in data/).- experiments/run_r3_additions.py and src/fe_hitl_r3.py: read-only analyses added in the third revision (Algorithm 1 as specified, single-candidate controls, feedback-and-update diagnostics).- experiments/revision_results/ and experiments/credit_revision_results/: raw per-seed results, i.e., the data points behind all reported means, standard deviations, and tests.- regen_stats.py, inference_fixed.py, fairness_cost_joint.py: regenerate every statistical table of the article and its S1 Appendix from the raw results into r3_tables/. r3_tables/MANIFEST.json records input hashes, library versions, and statistical conventions.- requirements.txt and requirements-lock.txt: the software environment (results generated with Python 3.14.3). Reproducing the statisticspip install -r requirements.txtpython regen_stats.pypython inference_fixed.pypython fairness_cost_joint.pyThe regenerated files in r3_tables/ match the deposited ones to numerical precision. See README.md for the full run order and seed sets. Notes- The fixed-ratio post-processing baseline called FRP in the article is labelled "Debiased-HITL" in the code and data files; it is an author-designed heuristic.- Files listed as superseded in README.md, as well as data/generate_agricultural_data.py and appendix/S1_Appendix.md from the first submission, are not used by any reported result.- German Credit data: Hofmann H. Statlog (German Credit Data). UCI Machine Learning Repository; This version replaces earlier versions, which corresponded to earlier revisions of the manuscript. The code is released under the MIT License. 2026-03-15T12:50:10Z Dataset Dataset 10.6084/m9.figshare.31742617.v5 https://figshare.com/articles/dataset/_b_From_G_nther_Anders_to_Engineerable_Fairness_Code_and_Data_for_FE-HITL_Framework_b_/31742617 MIT