{"id":46112,"date":"2023-01-25T10:15:06","date_gmt":"2023-01-25T09:15:06","guid":{"rendered":"https:\/\/www.h-its.org\/de\/event\/kolloquium-shimei-pan\/"},"modified":"2023-02-13T17:25:37","modified_gmt":"2023-02-13T16:25:37","slug":"kolloquium-shimei-pan","status":"publish","type":"tribe_events","link":"https:\/\/www.h-its.org\/de\/event\/kolloquium-shimei-pan\/","title":{"rendered":"Kolloquium Shimei Pan: Bias and Fairness in AI"},"content":{"rendered":"

By Shimei Pan<\/a>, Information Systems Department of UMBC, Baltimore, USA<\/p>\n

The emergence of advanced AI technologies, such as Deep Learning and ChatGPT has the potential to revolutionize the way we work, live, and interact with each other, potentially bringing a profound transformation of our society. Thus, it is imperative that we ensure the responsible development and equitable use of these technologies. Unfortunately, it has been found recently\u00a0that numerous\u00a0AI systems exhibit prejudice against certain groups of people, including women and people of color.\u00a0 There is an increasing concern that vulnerable groups in our society could be harmed by biased AI systems.<\/p>\n

In this talk, I will present some of our recent work on bias and fairness in AI such as fairness definitions\/assessment and bias mitigation. \u00a0As AI fairness is not a purely technical construct, having social implications, I will also present our work on human-fair AI interaction to demonstrate that an algorithmic solution itself is often insufficient to achieve its intended societal goals.<\/p>\n

 <\/p>\n

Short CV:<\/strong><\/p>\n

Dr. Shimei Pan is an Associate Professor in the Information Systems\u00a0Department and the director of the text mining and social media analytics lab at University of Maryland, Baltimore County. Previously, she\u00a0was a research scientist at IBM Watson Research Center in New\u00a0York. Her research focuses on Natural Language Processing (NLP),\u00a0fair AI, and Human-AI Interaction. \u00a0Her current Fulbright Award in\u00a0Germany is on cross-cultural analysis of social biases with large pre-trained language models. Dr. Pan received a Ph.D. in Computer Science\u00a0from Columbia University.<\/p>\n

 <\/p>\n

REGISTRATION:<\/strong><\/p>\n

The talk will be hybrid.<\/article>\n
If you would like to participate in person, please register in advance at benedicta.frech@h-its.org<\/a>.<\/article>\n
If you would like to participate online, please register in advance here: https:\/\/kta-email.zoom.us\/meeting\/register\/tJEldOCrqDssE9UtTNjzWOSpZh6dEb3EZelh<\/a><\/article>\n
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After registering, you will receive a confirmation email containing information about joining the meeting.<\/article>\n
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In case you are not able to attend, you can watch the talk afterwards on the HITS YouTube channel:\u00a0https:\/\/www.youtube.com\/user\/TheHITSters<\/a>.<\/article>\n","protected":false},"excerpt":{"rendered":"

By Shimei Pan, Information Systems Department of UMBC, Baltimore, USA The emergence of advanced AI technologies, such as Deep Learning and …<\/p>\n","protected":false},"author":42,"featured_media":46115,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"hits-research-group":[1302],"tribe_events_cat":[1345,1408],"class_list":["post-46112","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","hits-research-group-nlp-de","tribe_events_cat-oeffentliche-veranstaltungen","tribe_events_cat-kolloquien","cat_oeffentliche-veranstaltungen","cat_kolloquien"],"acf":[],"yoast_head":"\nKolloquium Shimei Pan: Bias and Fairness in AI - HITS gGmbH<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.h-its.org\/de\/event\/kolloquium-shimei-pan\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Kolloquium Shimei Pan: Bias and Fairness in AI - 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