{"675076":{"#nid":"675076","#data":{"type":"news","title":"Breaking Barriers with Amaya McNealey: Navigating the Complexities of Algorithmic Fairness ","body":[{"value":"\u003Cdiv\u003E\u003Cp\u003EIn her presentation at the \u003Ca href=\u0022https:\/\/emerging-researchers.org\/\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003E2024 Emerging Researchers National (ERN) Conference in STEM\u003C\/a\u003E held in Washington, D.C., ISyE PhD student \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/amaya-mcnealey\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003EAmaya McNealey\u003C\/a\u003E seized 1st place in the Graduate Oral Presentation in the Data Science, Physiology, and Health category.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003ECurrently in her second year as a PhD student in the \u003Ca href=\u0022http:\/\/www.isye.gatech.edu\/\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003EH. Milton Stewart School of Industrial and Systems Engineering (ISyE)\u003C\/a\u003E program, Amaya McNealey has directed her research and focus in healthcare and social systems. Co-advised by ISyE professors, \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/lauren-steimle\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003ELauren Steimle\u003C\/a\u003E, and \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/gian-gabriel-garcia\u0022 rel=\u0022noreferrer noopener\u0022 target=\u0022_blank\u0022\u003EGian-Gabriel Garcia\u003C\/a\u003E, McNealey has worked with them to develop models that aim to understand and mitigate biases in healthcare algorithms.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EAt the ERN Conference in STEM, McNealey\u2019s research analyzed the potential for algorithmic bias in clinical decision support, particularly within the realm of maternal health disparities.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003EThrough her work, McNealey sheds light on the critical need for comprehensive solutions to mitigate biases perpetuated by machine learning (ML) algorithms.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003ENationwide and particularly in Georgia, there\u2019s been a sharp increase in maternal adverse outcomes for Black and Hispanic women, in which her research investigates the racial disparities. Her work focused on the impact of removing race as a predictor in an ML model used to help pregnant people decide whether to deliver via C-section or vaginally.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003E\u201cRemoving race isn\u2019t simply always going to be the solution to reducing disparities in these models \u2013 additional research to understand what is race actually trying to capture in these models and incorporate these underlying factors in a way that maintains model accuracy while accounting for fairness.\u201d\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003EBy unveiling how simply removing race from ML algorithms can still lead to disproportionate impact on historically marginalized populations, her work advocates for interdisciplinary collaboration and practical implementation to ensure equitable outcomes for all.\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003EWith that type of data materializing, McNealey plans to create a pipeline for fair machine learning development with metrics that can be used in an effective way.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003E\u201cModel fairness is essentially looking at how either prediction or outcomes of a machine learning model affects the population. When we create our ML models, typically the first thing we look at is accuracy, but we do not regularly investigate the downstream effects or decisions that are made as a result of the model.\u201d\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003EIn collaboration with Emory University, McNealey has gained insight into how these models are used in practice which has allowed her to consider how this impacts her work and additionally the community at hand.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003ELeaning on recent advances in algorithmic fairness, Amaya challenges conventional approaches by highlighting the limitations of merely removing race indicators from ML models.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003EBy advocating for holistic solutions, she is already paving the way for meaningful progress towards impartial practices in ML research.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003E\u201cWith this type of work, we don\u2019t see it just in healthcare, we see it in loan applications, criminal recidivism\u2026 I think this can be very impactful for a lot of different communities and applications.\u201d\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003ELooking ahead, McNealey envisions a future where accessible tools and metrics empower stakeholders to detect, justify, and mitigate biases effectively.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003EHer award-winning presentation exemplifies inclusive excellence and leads the next generation to the new era of shaping diverse perspectives on comprehensive equity in ML research. As we celebrate her achievements, let us embrace the opportunity to pursue technologies that uplift and empower every individual irrespective of race, gender or background.\u0026nbsp;\u0026nbsp;\u003C\/p\u003E\u003C\/div\u003E\u003Cdiv\u003E\u003Cp\u003E___\u003C\/p\u003E\u003Cp\u003EBy: Camille Carpenter, Communications Manager\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EHer award-winning presentation examined critical issues surrounding algorithmic biases, particularly in maternal health disparities, showcasing her commitment to advancing equitable practices in machine learning research.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Amaya McNealey, ISyE PhD student, was awarded 1st place in the Graduate Oral Presentation at the 2024 Emerging Researchers National Conference in STEM"}],"uid":"36284","created_gmt":"2024-06-10 21:00:24","changed_gmt":"2024-06-10 21:13:39","author":"chenriquez8","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2024-06-07T00:00:00-04:00","iso_date":"2024-06-07T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"674162":{"id":"674162","type":"image","title":"Amaya McNealey","body":null,"created":"1718053234","gmt_created":"2024-06-10 21:00:34","changed":"1718053234","gmt_changed":"2024-06-10 21:00:34","alt":"Amaya McNealey","file":{"fid":"257641","name":"Amaya McNealey.png","image_path":"\/sites\/default\/files\/2024\/06\/10\/Amaya%20McNealey.png","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/06\/10\/Amaya%20McNealey.png","mime":"image\/png","size":1649312,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/06\/10\/Amaya%20McNealey.png?itok=lFTi7ub3"}}},"media_ids":["674162"],"groups":[{"id":"660354","name":"Center for Academics, Success, and Equity"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[{"id":"135","name":"Research"},{"id":"193158","name":"Student Competition Winners (academic, innovation, and research)"},{"id":"193157","name":"Student Honors and Achievements"},{"id":"8862","name":"Student Research"}],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}