CRII: III: Towards Efficient Interpretation for Explainable Learning: A Computational Pers — NSF Award to Wake Forest University (
Artificial intelligence (AI) systems, especially advanced machine learning models, increasingly support critical decisions in areas such as healthcare. However, many of these AI systems operate as "black boxes", providing outcomes without clear explanations of how decisions were made. The lack of transparency can hinde
| Award title | CRII: III: Towards Efficient Interpretation for Explainable Learning: A Computational Pers |
|---|---|
| Award ID | 2451480 |
| Awardee | Wake Forest University |
| City | WINSTON SALEM |
| State | NC |
| Amount obligated | $175,000 |
| Principal investigator | Fan Yang |
| Program | Info Integration & Informatics |
| Start date | 06/15/2025 |
| Abstract | Artificial intelligence (AI) systems, especially advanced machine learning models, increasingly support critical decisions in areas such as healthcare. However, many of these AI systems operate as "black boxes", providing outcomes without clear explanations of how decisions were made. The lack of transparency can hinder trust and accountability, particularly when AI decisions significantly affect human lives. This project seeks to address a critical limitation of existing explainable AI techniqu |
| Source | NSF Awards |
$799/mo
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