{"683785":{"#nid":"683785","#data":{"type":"news","title":"Juba Ziani Secures NSF Grant to Improve AI With Smarter Data Incentives","body":[{"value":"\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/users\/juba-ziani\u0022\u003EJuba Ziani\u003C\/a\u003E is on a mission to change how the world thinks about data in artificial intelligence. An assistant professor in Georgia Tech\u2019s \u003Ca href=\u0022https:\/\/www.isye.gatech.edu\/\u0022\u003EH. Milton Stewart School of Industrial and Systems Engineering (ISyE)\u003C\/a\u003E, Ziani has secured a $425,000 National Science Foundation (NSF) grant to explore how smart incentives can lead to higher-quality, more widely shared datasets. His work forms part of a $1 million NSF collaboration with Columbia University computer science professor Augustin Chaintreau and senior personnel Daniel Bj\u00f6rkegren, aiming to challenge outdated ideas and shape a more reliable future for AI.\u003C\/p\u003E\u003Cp\u003EArtificial intelligence (AI) increasingly shapes critical decisions in everyday life, from who sees a job posting or qualifies for a loan, to who is granted bail in the criminal justice system. These systems rely on historical data to learn patterns and make predictions. For example, an applicant might be approved for a loan because an AI system recognizes that previous borrowers with similar credit histories successfully repaid. But when the underlying training data is incomplete or low-quality, the consequences can be serious, disproportionately affecting those from groups historically excluded from such opportunities.\u003C\/p\u003E\u003Cp\u003EZiani\u0027s research will explore how the economic value of data, combined with the effects of data markets and network dynamics, can lead to incentives that naturally improve dataset robustness. By identifying the conditions under which the supposed efficiency trade-off disappears, Ziani and his collaborators hope to open the door to more reliable and equitable AI systems.\u003C\/p\u003E\u003Cp\u003ETraditionally, researchers have assumed that making AI-assisted decision-making more robust and representative comes at the expense of efficiency. This assumption treats training data as fixed and unchangeable, which can place limits on the potential of AI systems. But as large-scale data platforms grow and the exchange of data becomes more accessible, the conventional trade-off between robustness and efficiency may no longer apply.\u003C\/p\u003E\u003Cp\u003E\u201cOur project demonstrates how carefully designing incentives\u2014both for data producers and data buyers\u2014can enhance the quality and robustness of datasets\u0026nbsp;\u003Cem\u003Ewithout\u003C\/em\u003E\u0026nbsp;compromising performance,\u201d said Ziani. \u201cThis has the potential to fundamentally reshape the way AI systems are trained and how data is collected, shared, and valued.\u201d\u003C\/p\u003E\u003Cp\u003EWith this work, Ziani aims to advance both the theory and practice of AI and data economics, ensuring that as AI continues to transform society, and does so in a way that is fair, accurate, and trustworthy.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech\u2019s Juba Ziani has been awarded a $425,000 National Science Foundation (NSF) grant to explore how economic incentives can improve the quality and robustness of data used in AI systems. In collaboration with Columbia University\u2019s Augustin Chaintreau, the project aims to challenge the traditional trade-off between data quality and efficiency, offering new insights into how better data sharing practices can lead to more equitable and reliable AI outcomes.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"The researcher aims to transform how data is valued and shared, paving the way for stronger, more equitable AI technologies."}],"uid":"36736","created_gmt":"2025-08-13 13:45:00","changed_gmt":"2025-08-13 19:42:54","author":"ebrown386","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2025-08-13T00:00:00-04:00","iso_date":"2025-08-13T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"677681":{"id":"677681","type":"image","title":"Juba Ziani.jpg","body":null,"created":"1755095827","gmt_created":"2025-08-13 14:37:07","changed":"1755095827","gmt_changed":"2025-08-13 14:37:07","alt":"Juba Ziani","file":{"fid":"261612","name":"Juba-Ziani.jpg","image_path":"\/sites\/default\/files\/2025\/08\/13\/Juba-Ziani.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/08\/13\/Juba-Ziani.jpg","mime":"image\/jpeg","size":82023,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/08\/13\/Juba-Ziani.jpg?itok=-QcJNkcL"}}},"media_ids":["677681"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[{"id":"135","name":"Research"}],"keywords":[{"id":"192863","name":"go-ai"}],"core_research_areas":[{"id":"193655","name":"Artificial Intelligence at Georgia Tech"},{"id":"39541","name":"Systems"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EErin Whitlock Brown, Communications Manager II\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}