{"meta":{"query_hash":"f9e69a154431","filters":{"venue":"2022 ACM Conference on Fairness, Accountability, and Transparency"},"cohort_total":6,"direct_labels_cover":0,"predictions_cover":6,"exported":6,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/f9e69a154431","api":"https://metacan.xera.ac/api/v1/cohort?venue=2022+ACM+Conference+on+Fairness%2C+Accountability%2C+and+Transparency"},"results":[{"id":"W4229442586","doi":"10.1145/3531146.3533179","title":"The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations","year":2022,"lang":"en","type":"article","venue":"2022 ACM Conference on Fairness, Accountability, and Transparency","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Government of Canada; Canadian Institute for Advanced Research; Vector Institute; Microsoft Research","keywords":"Computer science; Fidelity; Audit; Quality (philosophy); Machine learning; Artificial intelligence; High fidelity; Data science","score_opus":0.0904029058685791,"score_gpt":0.2902133418712273,"score_spread":0.19981043600264822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229442586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5573982,0.002973649,0.42039025,0.009493044,0.00022194858,0.00026657333,0.0010691556,0.0008224061,0.0073648477],"genre_scores_gemma":[0.977473,0.00013587688,0.02134538,0.00031799226,0.000074622796,0.00006751696,0.00025750988,0.000095632975,0.00023251348],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9383346,0.043367065,0.003413344,0.0055583552,0.00781709,0.0015095588],"domain_scores_gemma":[0.43794808,0.46210435,0.035559874,0.049793735,0.011419921,0.0031741248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.066597834,0.00086834125,0.0014617787,0.0028961417,0.0026744048,0.0051700114,0.0017688399,0.0036338316,0.0027430686],"category_scores_gemma":[0.42195013,0.00061112765,0.0013018673,0.0020899265,0.0076146424,0.011203913,0.0067558605,0.005094803,0.0003506087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030808675,0.0005194025,0.404851,0.0012385625,0.0017438936,0.00049527624,0.01439601,0.12208297,0.0039157257,0.21533468,0.007292385,0.22504923],"study_design_scores_gemma":[0.00020005846,0.00052562513,0.073452465,0.00064103754,0.00038755688,0.000444818,0.0028213423,0.2841185,0.0076441765,0.6210931,0.0084100505,0.00026134704],"about_ca_topic_score_codex":0.0046943957,"about_ca_topic_score_gemma":0.0025368698,"teacher_disagreement_score":0.066597834,"about_ca_system_score_codex":0.002973036,"about_ca_system_score_gemma":0.002857045,"threshold_uncertainty_score":0.35220724},"labels":[],"label_agreement":null},{"id":"W4281252189","doi":"10.1145/3531146.3533222","title":"Stop the Spread: A Contextual Integrity Perspective on the Appropriateness of COVID-19 Vaccination Certificates","year":2022,"lang":"en","type":"article","venue":"2022 ACM Conference on Fairness, Accountability, and Transparency","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Science Foundation","keywords":"Vignette; Certificate; Salient; Perspective (graphical); Sample (material); Normative; Population; Internet privacy; Computer science; Coronavirus disease 2019 (COVID-19); Psychology; Social psychology; Medicine; Artificial intelligence; Political science","score_opus":0.11394853293193134,"score_gpt":0.33534910156612846,"score_spread":0.2214005686341971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281252189","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.83593136,0.0006146597,0.046677113,0.013900249,0.00007120511,0.00018133008,0.00012385879,0.00008178067,0.102418385],"genre_scores_gemma":[0.9978362,0.000056813187,0.0015138384,0.00024732054,0.000010257958,0.000017551012,0.000009574205,0.000009150612,0.00029942833],"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.95387745,0.03781483,0.001413107,0.0016314404,0.0032886618,0.0019745175],"domain_scores_gemma":[0.85838205,0.10943377,0.014988975,0.008603263,0.0065763635,0.0020156284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024415966,0.00025814518,0.0002876417,0.0009910724,0.0038742812,0.006274979,0.0009951399,0.0021485782,0.0040827035],"category_scores_gemma":[0.11473771,0.00036876433,0.00035666025,0.00074453646,0.0092839515,0.0077125067,0.0038006126,0.002935002,0.0003258273],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070782145,0.0005935634,0.18824671,0.00044829247,0.00010293179,0.0014137453,0.37247553,0.004384838,0.0054676803,0.34183437,0.0026394462,0.08168509],"study_design_scores_gemma":[0.00017710008,0.0015620125,0.16414328,0.0017431459,0.00028325937,0.0022652722,0.42510596,0.023205236,0.012970755,0.25100514,0.11719476,0.00034411647],"about_ca_topic_score_codex":0.0054719104,"about_ca_topic_score_gemma":0.004180751,"teacher_disagreement_score":0.024415966,"about_ca_system_score_codex":0.0029741332,"about_ca_system_score_gemma":0.0028134142,"threshold_uncertainty_score":0.12912554},"labels":[],"label_agreement":null},{"id":"W4283155630","doi":"10.1145/3531146.3533231","title":"Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI","year":2022,"lang":"en","type":"article","venue":"2022 ACM Conference on Fairness, Accountability, and Transparency","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":183,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada)","funders":"","keywords":"Documentation; Computer science; Information retrieval; Data science; Artificial intelligence; Programming language","score_opus":0.2238368987664745,"score_gpt":0.4491079445249052,"score_spread":0.2252710457584307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283155630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006988659,0.00047987592,0.92722857,0.016535696,0.00079712126,0.0020050406,0.005491059,0.025613697,0.014860315],"genre_scores_gemma":[0.050296195,0.0006136718,0.9142381,0.0027852678,0.0003773496,0.0027403673,0.012759421,0.008386319,0.0078034126],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.8159092,0.10512956,0.03818383,0.00974595,0.028180694,0.0028508715],"domain_scores_gemma":[0.34893143,0.1853017,0.027387623,0.37078124,0.058836635,0.008761339],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.23975576,0.0018891897,0.001978709,0.010630994,0.005772913,0.028364373,0.009599047,0.006562269,0.014613892],"category_scores_gemma":[0.42762032,0.0040677395,0.0022187468,0.0092426315,0.007746827,0.048424277,0.030321194,0.013809995,0.010478085],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009795137,0.0006561178,0.008341782,0.0017579794,0.00022857069,0.0010408936,0.027877005,0.009287776,0.0061884685,0.38288432,0.1748615,0.385896],"study_design_scores_gemma":[0.00026934335,0.00024625353,0.0027177252,0.0034202484,0.00008685398,0.00056816026,0.0043337583,0.026312312,0.008394038,0.20747307,0.7457438,0.00043444426],"about_ca_topic_score_codex":0.0050141225,"about_ca_topic_score_gemma":0.005202419,"teacher_disagreement_score":0.99040097,"about_ca_system_score_codex":0.006408177,"about_ca_system_score_gemma":0.023807002,"threshold_uncertainty_score":0.9375165},"labels":[],"label_agreement":null},{"id":"W4283156114","doi":"10.1145/3531146.3533209","title":"On the Power of Randomization in Fair Classification and Representation","year":2022,"lang":"en","type":"article","venue":"2022 ACM Conference on Fairness, Accountability, and Transparency","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Classifier (UML); Artificial intelligence; Machine learning; Computer science; Mathematics; Representation (politics); Algorithm; Linear classifier; Randomization; Mathematical optimization; Pattern recognition (psychology)","score_opus":0.10741622087148499,"score_gpt":0.37011059986207107,"score_spread":0.26269437899058606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283156114","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026275843,0.0006104095,0.96225035,0.0029363602,0.00014643234,0.000120559416,0.000116254974,0.00025330286,0.007290521],"genre_scores_gemma":[0.80220306,0.0007355214,0.18891406,0.0015513973,0.0005005517,0.00062127126,0.00023171681,0.0002880453,0.0049544466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97816545,0.012465085,0.00080661324,0.0034818968,0.0035897272,0.0014912288],"domain_scores_gemma":[0.8957075,0.082424246,0.004336561,0.012598637,0.003497429,0.0014355885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.024469629,0.00143935,0.0026778115,0.0015537025,0.0027464267,0.005130389,0.0028472603,0.0028857135,0.00458839],"category_scores_gemma":[0.108576685,0.00080759014,0.0017238789,0.0019328329,0.010741358,0.013104142,0.005808804,0.0068034483,0.0007182676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003018762,0.0001171452,0.0017510755,0.00008438876,0.00006770372,0.00010213728,0.00030957657,0.15397722,0.0009961769,0.7961383,0.002426672,0.043727748],"study_design_scores_gemma":[0.000047664696,0.000057940782,0.00023122532,0.00003824783,0.000019009278,0.000050361577,0.000039530085,0.3016797,0.0007831389,0.69550174,0.0015247111,0.000026724598],"about_ca_topic_score_codex":0.0022751896,"about_ca_topic_score_gemma":0.0018273453,"teacher_disagreement_score":0.024469629,"about_ca_system_score_codex":0.0044323676,"about_ca_system_score_gemma":0.004004323,"threshold_uncertainty_score":0.12940931},"labels":[],"label_agreement":null},{"id":"W4283167130","doi":"10.1145/3531146.3534637","title":"Data Governance in the Age of Large-Scale Data-Driven Language Technology","year":2022,"lang":"en","type":"article","venue":"2022 ACM Conference on Fairness, Accountability, and Transparency","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Corporate governance; Data governance; Work (physics); Knowledge management; Computer science; Data science; Data management; Scale (ratio); Public relations; Political science; Business; Database; Data quality; Engineering; Marketing","score_opus":0.11548224591861539,"score_gpt":0.39609528443750874,"score_spread":0.28061303851889335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283167130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06703626,0.004358048,0.7113709,0.16837242,0.0007669042,0.00042693358,0.00052135036,0.0014210397,0.045726117],"genre_scores_gemma":[0.7513699,0.0031816787,0.22006963,0.012476399,0.0015562093,0.0008047663,0.0007859556,0.00064413145,0.009111483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9527237,0.027650977,0.002708553,0.007306039,0.007847951,0.0017628807],"domain_scores_gemma":[0.8333529,0.092614144,0.0096553145,0.046678852,0.011675931,0.0060228514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09051332,0.00046172162,0.0010822888,0.0030071896,0.0043594316,0.018895535,0.0034915495,0.0045129876,0.0022068322],"category_scores_gemma":[0.104938895,0.00094993453,0.0007613037,0.0044740704,0.020836832,0.03673578,0.01772404,0.009119137,0.00088998507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004607753,0.000051341274,0.0029614358,0.00010749125,0.000035135938,0.00013223416,0.006114466,0.0037133375,0.0007511843,0.93996924,0.0058072107,0.040310793],"study_design_scores_gemma":[0.000026560505,0.000031412932,0.0007450936,0.0001460382,0.000012117973,0.000095572555,0.0020475807,0.0076786256,0.0006982579,0.9093406,0.07913738,0.000040668994],"about_ca_topic_score_codex":0.0038957775,"about_ca_topic_score_gemma":0.0024444226,"teacher_disagreement_score":0.09051332,"about_ca_system_score_codex":0.0055619515,"about_ca_system_score_gemma":0.00933232,"threshold_uncertainty_score":0.47868592},"labels":[],"label_agreement":null},{"id":"W4283170666","doi":"10.1145/3531146.3533088","title":"Taxonomy of Risks posed by Language Models","year":2022,"lang":"en","type":"article","venue":"2022 ACM Conference on Fairness, Accountability, and Transparency","topic":"Hate Speech and Cyberbullying Detection","field":"Computer Science","cited_by":604,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Misinformation; Taxonomy (biology); Harm; Risk analysis (engineering); Futures studies; Computer science; Risk management; Data science; Management science; Knowledge management; Engineering ethics; Psychology; Business; Artificial intelligence; Computer security; Social psychology; Engineering","score_opus":0.07387969715411008,"score_gpt":0.28462327550732947,"score_spread":0.21074357835321939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283170666","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1465897,0.0081715,0.63808537,0.05345459,0.0005182718,0.0010008927,0.0005506115,0.0012673405,0.15036166],"genre_scores_gemma":[0.8288844,0.005505418,0.15217103,0.002587718,0.00040074904,0.00088572805,0.00055626186,0.000238929,0.008769788],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92419946,0.031843413,0.00656043,0.0033816462,0.030202093,0.0038129769],"domain_scores_gemma":[0.8756186,0.07425313,0.013977911,0.016932413,0.016523054,0.0026949926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03159864,0.0018763024,0.0008698409,0.008971202,0.007098341,0.012589732,0.0032737558,0.0079946965,0.003166432],"category_scores_gemma":[0.08222337,0.0010322748,0.0021426345,0.0035229765,0.016332833,0.023881076,0.013232015,0.0077935415,0.00079194497],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007163408,0.00009699293,0.01173439,0.00038479763,0.000065738954,0.0009807671,0.016193341,0.009468224,0.0007324404,0.90893316,0.0029164169,0.048422035],"study_design_scores_gemma":[0.000020842303,0.00010813767,0.002261511,0.0011065885,0.00008880118,0.0024683243,0.011284185,0.025179248,0.001473445,0.8954215,0.06045306,0.00013430846],"about_ca_topic_score_codex":0.0048475047,"about_ca_topic_score_gemma":0.002538707,"teacher_disagreement_score":0.03159864,"about_ca_system_score_codex":0.0069292015,"about_ca_system_score_gemma":0.0066742403,"threshold_uncertainty_score":0.16711158},"labels":[],"label_agreement":null}]}