{"doi":"10.1109/dac56929.2023.10247765","title":"Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models","abstract":"Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause a traffic accident if corner cases (e.g., extreme weather) cannot be fairly regarded; or it will incur healthcare disparities if the AI model misdiagnoses a certain group of people (e.g., brown and black skin). In recent years, there are emerging research works on addressing unfairness, and they mainly focus on a single unfair attribute, like skin tone; however, real-world data commonly have multiple attributes, among which unfairness can exist in more than one attribute, called \"multi-dimensional fairness\". In this paper, we first reveal a strong correlation between the different unfair attributes, i.e., optimizing fairness on one attribute will lead to the collapse of others. Then, we propose a novel Multi-Dimension Fairness framework, namely Muffin, which includes an automatic tool to unite off-the-shelf models to improve the fairness on multiple attributes simultaneously. Case studies on dermatology datasets with two unfair attributes show that the existing approach can achieve 21.05% fairness improvement on the first attribute while it makes the second attribute unfair by 1.85%. On the other hand, the proposed Muffin can unite multiple models to achieve simultaneously 26.32% and 20.37% fairness improvement on both attributes; meanwhile, it obtains 5.58% accuracy gain.","journal":"PubMed","year":2023,"id":395080,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9581,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1169890,"name":"Junhuan Yang","orcid":"0000-0002-4848-0248","position":1,"is_corresponding":false},{"id":1169891,"name":"Lei Yang","orcid":"0000-0002-0646-440X","position":2,"is_corresponding":false},{"id":1165037,"name":"Yiyu Shi","orcid":"0000-0002-6788-9823","position":3,"is_corresponding":false},{"id":1165038,"name":"Jingtong Hu","orcid":"0000-0003-4029-4034","position":4,"is_corresponding":false},{"id":807633,"name":"Weiwen Jiang","orcid":"0000-0002-9004-487X","position":5,"is_corresponding":false},{"id":1169889,"name":"Yi Sheng","orcid":"0000-0003-0809-6994","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T01:19:18.686170Z","pmid":"38567296","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":[]}