CS Events

PhD Defense Promoting Fairness and Accuracy in Dynamic and Multimodal Algorithms
Export Event Save iCal Send to Google Calendar Send to Yahoo Calendar Send to Outlook Live Send to Microsoft Outlook Save iCal Preserve formatting in description (only supported in some calendar applications) Close Thursday, December 21, 2023, 10:30am - 12:00pm

Speaker: Abdulaziz A. Almuzaini

Location : CoRE 301

Committee: 

Vivek K. Singh 

David M. Pennock

Amélie Marian 

Pradeep K. Atrey (University at Albany)

Event Type: PhD Defense

Abstract: A multitude of decision-making tasks, such as content moderation, medical diagnosis, misinformation detection, and recidivism prediction, can now be easily automated due to the recent developments in machine learning (ML) capabilities. ML models excel in large scale data processing and complex pattern recognition. However, their effectiveness may diminish in specific situations when the assumption of stationarity is violated, i.e., the independent and identically distributed (iid) assumption. Specifically, the nature of the aforementioned tasks is that they are not static; they evolve over time. In this study, we explore these challenges and propose strategies to alleviate their adverse effects in multimodal settings including visual, textual and social data. We first introduce an “Anticipatory Bias Correction” method designed to address algorithmic fairness and accuracy jointly in temporally-shifting settings, ensuring the proactivity and adaptability objectives. Subsequently, we investigate the ML performance of a dermatological image processing task for skin-cancer detection, where datasets are collected from diverse locations and propose a fair and accurate methodological framework. Lastly, we summarize observed issues and provide recommendations for potential solutions.

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Contact  Professor Vivek K. Singh

Link to Zoom: https://rutgers.zoom.us/j/94732455955?pwd=a0hTcTV3ZThDand6SVBuZnk5NlVnUT09#success