{"665044":{"#nid":"665044","#data":{"type":"event","title":"PhD Proposal by De\u2019Aira Bryant","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E Development of a Bias-Aware Algorithm for the Analysis and Perception of Children\u0026rsquo;s Facial Expressions by Autonomous Agents\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Tuesday, January 31, 2023\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 8:00am \u0026ndash; 10:00am\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/93648508890?pwd=SGRMeHdxVUxNRHV6N3pTNjh1cXdqdz09\u0026amp;from=addon\u0022\u003EZoom Link\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMeeting ID: 936 4850 8890\u003C\/p\u003E\r\n\r\n\u003Cp\u003EPasscode: 576950\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDe\u0026rsquo;Aira Bryant\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EComputer Science Ph.D. Student\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESchool of Interactive Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAyanna Howard (Advisor) \u0026ndash; School of Interactive Computing, Georgia Institute of Technology \/ College of Engineering, Ohio State University\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECharles Isbell \u0026ndash; School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESonia Chernova \u0026ndash; \u0026nbsp;School of Interactive Computing, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003EJason Borenstein \u0026ndash; \u0026nbsp;School of Public Policy, Georgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETom Williams \u0026ndash; \u0026nbsp;Department of Computer Science, Colorado School of Mines\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe field of human-robot interaction (HRI) has made great strides toward designing autonomous agents that operate in real-world environments with humans. Potential applications span the sectors of education, healthcare, hospitality, and more. These agents are often complex intelligent systems that utilize various perception algorithms to interact with their environment. Prior work in artificial intelligence (AI) has shown that human-centered perception algorithms are susceptible to producing biased output, thus leading to ethical concerns around fairness, privacy, and safety. The measurement and mitigation of bias in AI systems have since risen to be among the most important challenges in recent computer science history. Yet, very little work has examined bias with respect to autonomous agents or how the perpetuation of bias via autonomous agents affects HRI.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis thesis examines the effects of human, data, and algorithmic bias on HRI through the lens of facial expression recognition (FER) and presents bias-aware techniques that will facilitate more effective HRI. First, we analyze human bias through an examination of normative expectations for expressive autonomous agents when considering factors like robot race, gender, and embodiment. These expectations help inform the design processes needed to develop effective agents. Next, we investigate data and algorithmic bias in FER systems for populations with scarce data. Subsequently, we develop improved techniques for modeling facial expression perception and benchmarking FER algorithms. We then propose the application of a semi-supervised machine learning technique, self-learning, as a bias-aware strategy for FER development and present preliminary results from a pilot experiment. We further propose a validation experiment to assess human perceptions of fairness during an interaction with an autonomous agent using a standard or bias-aware FER perception algorithm, thus improving our understanding of the implications of bias for HRI.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Development of a Bias-Aware Algorithm for the Analysis and Perception of Children\u2019s Facial Expressions by Autonomous Agents"}],"uid":"27707","created_gmt":"2023-01-24 15:55:10","changed_gmt":"2023-01-24 15:55:10","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-01-31T08:00:00-05:00","event_time_end":"2023-01-31T10:00:00-05:00","event_time_end_last":"2023-01-31T10:00:00-05:00","gmt_time_start":"2023-01-31 13:00:00","gmt_time_end":"2023-01-31 15:00:00","gmt_time_end_last":"2023-01-31 15:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}