{"doi":"10.1007/978-3-031-19775-8_17","title":"HIVE: Evaluating the Human Interpretability of Visual Explanations","abstract":null,"journal":"Lecture Notes in Computer Science","year":2022,"id":592293,"datarank":0.7635920500529518,"base_score":4.110873864173311,"endowment":4.110873864173311,"self_citation_contribution":0.6166310796259968,"citation_network_contribution":0.14696097042695494,"self_endowment_contribution":0.6166310796259968,"citer_contribution":0.14696097042695494,"corpus_percentile":null,"corpus_rank":null,"citation_count":60,"citer_count":6,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1372985,"name":"Nicole Meister","orcid":"0000-0002-7154-6882","position":1,"is_corresponding":false},{"id":1515615,"name":"Vikram V. Ramaswamy","orcid":"0000-0002-0552-5338","position":2,"is_corresponding":false},{"id":1515616,"name":"Ruth Fong","orcid":"0000-0001-8831-6402","position":3,"is_corresponding":false},{"id":33808,"name":"Olga Russakovsky","orcid":"0000-0001-5272-3241","position":4,"is_corresponding":false},{"id":884947,"name":"Sunnie S. Y. Kim","orcid":"0000-0002-8901-7233","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"HIVE: Evaluating the Human Interpretability of Visual Explanations","abstract":"As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable. Despite the recent growth of interpretability work, there is a lack of systematic evaluation of proposed techniques. In this work, we introduce HIVE (Human Interpretability of Visual Explanations), a novel human evaluation framework that assesses the utility of explanations to human users in AI-assisted decision making scenarios, and enables falsifiable hypothesis testing, cross-method comparison, and human-centered evaluation of visual interpretability methods. To the best of our knowledge, this is the first work of its kind. Using HIVE, we conduct IRB-approved human studies with nearly 1000 participants and evaluate four methods that represent the diversity of computer vision interpretability works: GradCAM, BagNet, ProtoPNet, and ProtoTree. Our results suggest that explanations engender human trust, even for incorrect predictions, yet are not distinct enough for users to distinguish between correct and incorrect predictions. We open-source HIVE to enable future studies and encourage more human-centered approaches to interpretability research.","is_dataset_classified":null,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"20725694","pmcid":null,"openalex_id":"https://openalex.org/W4226258012","authors":[],"funders":[{"funder_name":"National Science Foundation","grant_id":"1763642","title":"RI: Medium: Recognizing, Mitigating and Governing Bias in AI"}],"total_grants":1,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2024,"count":6}],"oa_status":"green","license":"Springer TDM","oa_locations":[{"url":"https://arxiv.org/pdf/2112.03184","host_type":"repository"},{"url":"https://arxiv.org/pdf/2112.03184","host_type":"repository"},{"url":"https://link.springer.com/content/pdf/10.1007/978-3-031-19775-8_17","host_type":"publisher"},{"url":"http://arxiv.org/abs/2112.03184","host_type":"repository"},{"url":"https://doi.org/10.1007/978-3-031-19775-8_17","host_type":"book series"},{"url":"https://doi.org/10.48550/arxiv.2112.03184","host_type":"repository"},{"url":"https://dx.doi.org/10.48550/arxiv.2112.03184","host_type":""},{"url":"https://arxiv.org/abs/2112.03184","host_type":""}],"fields_of_study":["Explainable Artificial Intelligence (XAI)","Adversarial Robustness in Machine Learning","Anomaly Detection Techniques and Applications","03 medical and health sciences","0302 clinical medicine","0202 electrical engineering, electronic engineering, information engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Interpretability","Computer science","Falsifiability","Artificial intelligence","Machine learning","Data science","Epistemology","FOS: Computer and information sciences","Computer Vision and Pattern Recognition (cs.CV)","Computer Science - Computer Vision and Pattern Recognition"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-26T13:05:30.476651Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}