{"id":"W4378176706","doi":"10.1177/02683962231181148","title":"Not seeing the (moral) forest for the trees? How task complexity and employees’ expertise affect moral disengagement with discriminatory data analytics recommendations","year":2023,"lang":"en","type":"article","venue":"Journal of Information Technology","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Dehumanization; Disengagement theory; Analytics; Task (project management); Psychology; Affect (linguistics); Moral disengagement; Knowledge management; Computer science; Data science; Social psychology; Sociology; Economics; Medicine; Management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008456091,0.0002779886,0.0002868453,0.0004360249,0.001861173,0.005699405,0.0005438435,0.0015044,0.005601743],"category_scores_gemma":[0.06435692,0.0002953263,0.0003427561,0.0002369561,0.003058992,0.003128158,0.001993655,0.002253704,0.0005474141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282973,"about_ca_system_score_gemma":0.001333051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002091046,"about_ca_topic_score_gemma":0.002839653,"domain_scores_codex":[0.9917602,0.005576587,0.0002475883,0.0006596406,0.001074312,0.0006816725],"domain_scores_gemma":[0.9097533,0.06379282,0.01441061,0.003851487,0.003782197,0.004409494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00329533,0.003554075,0.5171559,0.0008817115,0.0004803015,0.001248233,0.2591117,0.004035434,0.03861199,0.02991615,0.005997574,0.1357115],"study_design_scores_gemma":[0.0003302508,0.001860571,0.7192291,0.0004380548,0.0003786748,0.0004079803,0.176244,0.01756261,0.008967991,0.05544084,0.01875463,0.0003853757],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988468,0.00008282225,0.002514742,0.001207441,0.00002756297,0.00002177028,0.00001443006,0.00002020797,0.00764298],"genre_scores_gemma":[0.9986953,0.00003213828,0.0006037979,0.0002035905,0.0000069671,0.00001451199,0.000007654541,0.00001024841,0.000425708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008456091,"threshold_uncertainty_score":0.04472065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.206026314601658,"score_gpt":0.3900405069580577,"score_spread":0.1840141923563997,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}