{"id":"W4396218845","doi":"10.1145/3637396","title":"Seamful XAI: Operationalizing Seamful Design in Explainable AI","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"Universitas Brawijaya","keywords":"Sociotechnical system; Computer science; Operationalization; Leverage (statistics); Process (computing); Context (archaeology); Knowledge management; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.001675546,0.0001403561,0.000170279,0.0002101986,0.0006078403,0.000912881,0.0008821712,0.0001470728,0.00007954631],"category_scores_gemma":[0.0008363209,0.0001159411,0.0001178071,0.0004153148,0.0001194116,0.001796943,0.0002381611,0.0005948241,0.00002755526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003893739,"about_ca_system_score_gemma":0.0001331672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009828688,"about_ca_topic_score_gemma":0.0001536404,"domain_scores_codex":[0.9985251,0.00006675954,0.0003017825,0.0002963275,0.0005233673,0.0002865923],"domain_scores_gemma":[0.9989073,0.0003497837,0.0001099213,0.0001527427,0.0004185842,0.0000617077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001038392,0.0003535498,0.00120328,0.0003080568,0.0001295027,0.000007171627,0.1349429,0.001438022,0.01744372,0.7381784,0.09386081,0.01203073],"study_design_scores_gemma":[0.001057431,0.0009860663,0.004495663,0.005084827,0.00009985655,0.00001743803,0.02436818,0.02258672,0.05430216,0.7495764,0.1361742,0.001251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8755443,0.0001208432,0.0009086715,0.07819414,0.003446623,0.001031623,0.000003633451,0.0002794812,0.04047067],"genre_scores_gemma":[0.9949448,0.00003458721,0.001492662,0.001227771,0.0009394855,0.00002433388,0.000001244615,0.00002045606,0.001314689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1194005,"threshold_uncertainty_score":0.8802933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1375082652121015,"score_gpt":0.432029780617969,"score_spread":0.2945215154058675,"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."}}