Juan E. Gilbert
ABSTRACT
Fairness in machine learning (ML) and artificial intelligence (AI) has gained much attention in recent years, which has led to the creation of multiple fairness metrics and frameworks. However, it is still challenging for practitioners to decide which fairness metrics to incorporate in their work and how to effectively incorporate fairness into ML and AI models and systems. ML and AI can benefit from computer-human interaction (CHI) expertise from our backgrounds in empirically grounded research. ML and AI black-box models that are creating biased results are in need of CHI expertise to add the human aspect to the evaluation and creation of AI systems. This will result in what I call Equitable AI systems. In my talk, I will describe an equitable AI system I created to create holistic diversity in higher education admissions, scholarships, hiring, etc. I will also discuss research from a CHI perspective in addressing bias in ML and AI.
- Juan E. Gilbert and Andrea E. Johnson. 2013. A Study of Admissions Software for Achieving Diversity, Psychnology Journal, 11, 1, pp. 67-90.Google Scholar
- Juan E. Gilbert and Chance Lewis. 2008. An Investigation of Computational Holistic Evaluation of Admissions Applications for a Minority Focused STEM Research Program. Journal of STEM Education: Innovations and Research, 9,1, pp. 1-8.Google Scholar
- Juan E. Gilbert. 2008. Applications Quest: A Case Study on Holistic Admissions. Journal of College Admission, Spring 2008, pp. 12 – 18.Google Scholar
- Juan E. Gilbert. 2006. Applications Quest: Computing Diversity. Communications of the ACM, 49,3, ACM, pp. 99 – 104.Google ScholarDigital Library
Index Terms
- Equitable AI
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