# CRII: III: Towards Efficient Interpretation for Explainable Learning: A Computational Perspective on Attribution and Recourse

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · Wake Forest University (NC) · $175,000

## 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 techniques: their inefficiency in producing explanations quickly and reliably. By improving the efficiency of these methods, this research aims to broaden the practical use of explainable AI systems in real-world scenarios, such as medical diagnosis and personalized treatments. This advancement in AI interpretability will significantly enhance the national health, prosperity, and welfare by enabling safer and more reliable deployment of AI in critical application scenarios. 

This project addresses the computational inefficiencies in current explainable AI methods through three interconnected research objectives. First, it will accelerate computationally demanding interpretation algorithms, specifically focusing on two widely used but computationally intensive explanation scenarios:  Use a solution approach for distributing gains or costs fairly . This acceleration will be achieved by novel randomized approximation techniques, substantially lowering computational complexity. Second, the proj

## Key facts

- **NSF award ID:** 2451480
- **Awardee organization:** Wake Forest University (NC)
- **SAM.gov UEI:** MBU6HCLNZ431
- **PI:** Fan Yang
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** INFO INTEGRATION & INFORMATICS, CISE Resrch Initiatn Initiatve
- **Estimated total:** $175,000
- **Funds obligated:** $175,000
- **Transaction type:** Standard Grant
- **Period:** 06/15/2025 → 05/31/2027

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2451480

## Citation

> US National Science Foundation, Award 2451480, CRII: III: Towards Efficient Interpretation for Explainable Learning: A Computational Perspective on Attribution and Recourse. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2451480. Licensed CC0.

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