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Methodol."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>\n            Fairness is one of the socio-technical concerns that must be addressed in software systems. Considering the popularity of mobile software applications (apps) among a wide range of individuals worldwide, mobile apps with unfair behaviors and outcomes can affect a significant proportion of the global population, potentially more than any other type of software system. Users express a wide range of socio-technical concerns in mobile app reviews. This research aims to investigate fairness concerns raised in mobile app reviews. Our research focuses on AI-based mobile app reviews as the chance of unfair behaviors and outcomes in AI-based mobile apps may be higher than in non-AI-based apps. To this end, we first manually constructed a ground-truth dataset, including 1,132 fairness and 1,473 non-fairness reviews. Leveraging the ground-truth dataset, we developed and evaluated a set of machine learning and deep learning models that distinguish fairness reviews from non-fairness reviews. Our experiments show that our best-performing model can detect fairness reviews with a precision of 94%. We then applied the best-performing model on approximately 9.5M reviews collected from 108 AI-based apps and identified around 92K fairness reviews. Next, applying the K-means clustering technique to the 92K fairness reviews, followed by manual analysis, led to the identification of six distinct types of fairness concerns (e.g.,\n            <jats:italic>\u201creceiving different quality of features and services in different platforms and devices\u201d<\/jats:italic>\n            and\n            <jats:italic>\u201clack of transparency and fairness in dealing with user-generated content\u201d<\/jats:italic>\n            ). Finally, the manual analysis of 2,248 app owners\u2019 responses to the fairness reviews identified six root causes (e.g., \u201ccopyright issues\u201d) that app owners report to justify fairness concerns.\n          <\/jats:p>","DOI":"10.1145\/3690633","type":"journal-article","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T15:10:38Z","timestamp":1724944238000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Fairness Concerns in App Reviews: A Study on AI-Based Mobile Apps"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0750-0902","authenticated-orcid":false,"given":"Ali","family":"Rezaei Nasab","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4516-1409","authenticated-orcid":false,"given":"Maedeh","family":"Dashti","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9081-1354","authenticated-orcid":false,"given":"Mojtaba","family":"Shahin","sequence":"additional","affiliation":[{"name":"RMIT University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6276-9956","authenticated-orcid":false,"given":"Mansooreh","family":"Zahedi","sequence":"additional","affiliation":[{"name":"University of Melbourne, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9958-0102","authenticated-orcid":false,"given":"Hourieh","family":"Khalajzadeh","sequence":"additional","affiliation":[{"name":"Deakin University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1466-7386","authenticated-orcid":false,"given":"Chetan","family":"Arora","sequence":"additional","affiliation":[{"name":"Monash University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2056-5346","authenticated-orcid":false,"given":"Peng","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Microsoft. 2024. 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