{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:40:21Z","timestamp":1723016421546},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Growing awareness towards ethical use of machine learning (ML) models has created a surge for the development of fair models. Existing work in this regard assumes the presence of sensitive attributes in the data and hence can build classifiers whose decisions remain agnostic to such attributes. However, in the real world settings, the end-user of the ML model is unaware of the training data; besides, building custom models is not always feasible. Moreover, utilizing a pre-trained model with high accuracy on certain dataset can not be assumed to be fair. Unknown biases in the training data are the true culprit for unfair models (i.e., disparate performance for groups in the dataset). In this preliminary research, we propose a different lens for building fair models by enabling the user with tools to discover blind spots and biases in a pre-trained model and augment them with corrective measures.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/924","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"6470-6471","source":"Crossref","is-referenced-by-count":2,"title":["A Compliance Checking Framework for DNN Models"],"prefix":"10.24963","author":[{"given":"Sunny","family":"Verma","sequence":"first","affiliation":[{"name":"Advanced Analytics Institute, School of Computer Science, University of Technology Sydney, Australia"},{"name":"Commonwealth Scientific and Industrial Research Organisation, CSIRO, Data61, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, CSIRO, Data61, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Zhu","sequence":"additional","affiliation":[{"name":"Commonwealth Scientific and Industrial Research Organisation, CSIRO, Data61, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Advanced Analytics Institute, School of Computer Science, University of Technology Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2019","name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","start":{"date-parts":[[2019,8,10]]},"theme":"Artificial Intelligence","location":"Macao, China","end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:52:49Z","timestamp":1564300369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/924"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/924","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}