{"id":44625,"date":"2023-02-06T13:37:59","date_gmt":"2023-02-06T18:37:59","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ams\/?post_type=news&#038;p=44625"},"modified":"2023-05-31T14:34:24","modified_gmt":"2023-05-31T18:34:24","slug":"stephens-19-uses-data-science-skills-to-determine-bias-in-ai","status":"publish","type":"news","link":"https:\/\/engineering.jhu.edu\/ams\/news\/stephens-19-uses-data-science-skills-to-determine-bias-in-ai\/","title":{"rendered":"Stephens \u201919 uses data science skills to determine bias in AI\u00a0"},"content":{"rendered":"<p><span data-contrast=\"auto\">From apps and face recognition to Google searches and GPS, artificial intelligence (AI) has become an integral part of many people\u2019s daily lives. Despite its usefulness, there are growing concerns about the effect of biases embedded in the algorithms powering our AI systems, resulting in skewed and unfair results and predictions.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">A data scientist at the <a href=\"https:\/\/www.jhuapl.edu\">Johns Hopkins University Applied Physics Laboratory,<\/a> Alexandra Stephens MS \u201919 is developing a tool that not only identifies when bias happens, but also evaluates whether or not it is the result of chance.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">\u201cWe define \u2018bias\u2019 as a difference in performance between two or more subpopulations, which could include protected groups. We use statistical hypothesis testing to determine whether a difference in performance is due to random chance or indicates that an algorithm is biased. This tells us if the difference is \u2018meaningful\u2019,\u201d said Stephens, a <\/span><span data-contrast=\"auto\">graduate of the <a href=\"https:\/\/engineering.jhu.edu\/ams\/\">Department of Applied Mathematics and Statistics<\/a>\u2019 dual <a href=\"https:\/\/engineering.jhu.edu\/ams\/academics\/graduate-studies\/bachelors-masters-program\/\">bachelor\u2019s\/master\u2019s degree program<\/a>.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Once bias is statistically determined, S<\/span><span data-contrast=\"none\">tephens works on developing software tools to help her colleagues retrain the algorithms in question using cognitive bias mitigation strategies.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">\u201cIdentifying bias is critical in sensitive domains where you may be impacting a group of people. Consider an algorithm that predicts the likelihood of someone committing a crime again, but the algorithm used to generate this result is ultimately biased. When people put their trust in a blackbox algorithm rather than a trained human, it can have a negative impact on the lives of others,\u201d s<\/span><span data-contrast=\"auto\">aid Stephens.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Shortly after beginning her career at APL, Stephens was called to be part of a <\/span><span data-contrast=\"auto\">COVID-19 response team, analyzing emergency room and case data and developing metrics to characterize the state of the pandemic across US geographical regions. This helped inform federal decision-making about how best to allocate scarce supplies.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">This role later evolved into generating analytic reports on COVID-19 cases and deaths using an automated Python pipeline, which is a growing open-source programming language for engineers<\/span><i><span data-contrast=\"auto\">. <\/span><\/i><span data-contrast=\"auto\">In that role, Stephens used software engineering skills learned in a <a href=\"https:\/\/engineering.jhu.edu\">Whiting School of Engineering<\/a> data mining course taught by <\/span><a href=\"https:\/\/engineering.jhu.edu\/ams\/faculty\/tamas-budavari\/\"><span data-contrast=\"auto\">Tam\u00e1s Budav\u00e1ri,<\/span><\/a><span data-contrast=\"auto\"> an associate professor of applied mathematics and statistics, to create reports and graphs used by\u00a0 senior U.S. government leaders and other relevant stakeholders.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">\u201cProfessor Budav\u00e1ri introduced us to the use of Python to complete data science tasks. I furthered this skill at an internship, specifically with data analysis and plotting Python packages. This was very applicable to my time on the COVID-19 response team where we had to rapidly analyze and visualize COVID-19 data to quickly make sense of the pandemic and inform resource allocation and other decisions,\u201d said Stephens.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Other courses that she took at Johns Hopkins that she considers particularly noteworthy in terms of equipping her with skills she uses in her role at APL include Financial Computing, taught by <\/span><a href=\"https:\/\/engineering.jhu.edu\/ams\/faculty\/daniel-naiman\/\"><span data-contrast=\"none\">Daniel Naiman<\/span><\/a><span data-contrast=\"none\">, a professor of applied mathematics and statistics, and Optimization I and II, taught by <\/span><a href=\"https:\/\/engineering.jhu.edu\/ams\/faculty\/donniell-fishkind\/\"><span data-contrast=\"none\">Donniell Fishkind<\/span><\/a><span data-contrast=\"none\">, an associate research professor of applied mathematics and statistics.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">\u201cIn those classes, I used MATLAB to solve optimization problems for the first time and I also was able to do research with Dr. Fishkind building baseball schedules with optimization which strengthened my software skills,\u201d Stephens said.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">She says that the integration of software with mathematical theory and the use of code to solve math problems in these courses were so enjoyable and challenging that they sparked her interest in learning more.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">She looks forward not only to continuing to hone her coding skills and software techniques at APL, but also to assisting others in creating code bases that are accessible and simple to use.\u00a0<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">\u201cMath will always be my first love, but math alone can\u2019t solve these big, real-world problems. I&#8217;m always grateful that I studied applied mathematics at Hopkins because it provided me with a solid theoretical foundation as well as the practical ability to implement a solution,\u201d said Stephens. <\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">\u00a0 <\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","protected":false},"template":"","class_list":["post-44625","news","type-news","status-publish","hentry","news_categories-alumni-spotlight","news_categories-research"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Stephens \u201919 uses data science skills to determine bias in AI\u00a0 | Department of Applied Mathematics and Statistics<\/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:\/\/engineering.jhu.edu\/ams\/news\/stephens-19-uses-data-science-skills-to-determine-bias-in-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Stephens \u201919 uses data science skills to determine bias in AI\u00a0 | Department of Applied Mathematics and Statistics\" \/>\n<meta property=\"og:description\" content=\"From apps and face recognition to Google searches and GPS, artificial intelligence (AI) has become an integral part of many people\u2019s daily lives. 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