215 citations · 888 across the 37 of their papers we have counts for
3 papers · 1 filter
LLMs for XAI: Future Directions for Explaining Explanations
Alexandra Zytek, Sara Pidò, Kalyan Veeramachaneni
In response to the demand for Explainable Artificial Intelligence (XAI), we investigate the use of Large Language Models (LLMs) to transform ML explanations into natural, human-rea…
Machine learning 2.0 : Engineering Data Driven AI Products
James Max Kanter, Benjamin Schreck, Kalyan Veeramachaneni
ML 2.0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models - which requires months-long discovery, exploration and "feasibilit…
Solving the "false positives" problem in fraud prediction
Roy Wedge, James Max Kanter, Santiago Moral Rubio +2
In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction. False positives plague the fraud prediction in…