{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002485492,0.00008171507,0.0001496702,0.0002211431,0.001559998,0.0003211731,0.0006617706,0.00009991751,0.000003839321],"category_scores_gemma":[0.001943802,0.00004481984,0.00004211004,0.0005067919,0.0007316094,0.001649973,0.0001800362,0.0003917125,0.000001341196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007871318,"about_ca_system_score_gemma":0.0001495692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001051763,"about_ca_topic_score_gemma":0.003109564,"domain_scores_codex":[0.9990553,0.00009706624,0.0002775789,0.0000598398,0.0002993461,0.0002108979],"domain_scores_gemma":[0.9982942,0.0005936531,0.0004666352,0.0002706322,0.0003206157,0.00005420099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001263256,0.00006955759,0.01478927,0.00005735925,0.0003370134,0.000004962876,0.06325142,0.0002989707,0.00002263643,0.7237398,0.07513949,0.1221632],"study_design_scores_gemma":[0.002328759,0.00123185,0.09615859,0.0002191889,0.0004445628,0.00002554182,0.5342119,0.02960137,0.00009914344,0.1324317,0.2027539,0.0004935567],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1641201,0.0001195722,0.02934991,0.8040856,0.0004236948,0.0008908701,0.0001425507,0.000126998,0.0007406379],"genre_scores_gemma":[0.9971777,0.000626152,0.001443121,0.0005619415,0.00008445112,0.00001234565,0.00002730217,0.000005515243,0.00006143782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8330576,"threshold_uncertainty_score":0.9997398,"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."}}