IBM Research Receives First Place in the Explainable Machine Learning Challenge
FICO, the leading provider of analytics and decision management technology, together with Google and academics at UC Berkeley, Oxford, Imperial, UC Irvine and MIT, have announced the winners of the first xML Challenge at the 2018 NeurIPS workshop on Challenges and Opportunities for AI in Financial Services.
Participants were challenged to create machine learning models with both high accuracy and explainability using a real-world dataset provided by FICO. Sanjeeb Dash, Oktay Günlük and Dennis Wei, representing IBM Research, were this year’s challenge winners.
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The winning team received the highest score in an empirical evaluation method that considered how useful explanations are for a data scientist with the domain knowledge in the absence of model prediction, as well as how long it takes for such a data scientist to go through the explanations. For their achievements, the IBM team earned a $5,000 p****.
Receiving Honorable Mention and overall second place was New York University’s team, comprised of Steffen Holter, Oscar Gomez, and Enrico Bertini. The NYU team took home $2,000.
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The team representing Duke University, which included Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang and Tong Wang, received the FICO Recognition Award acknowledging their submission for going above and beyond expectations with a fully transparent global model and a user-friendly dashboard to allow users to explore the global model and its explanations. The Duke team took home $3,000.
“We congratulate all of the participants and award recipients on a job well done,” said Jari Koister, vice president of product management at FICO. “The importance of explainability in AI is growing each year. While data scientists must be able to understand and execute complex models that make business decisions, customers are demanding explanations for the predictions of deployed models. The winning teams in this challenge demonstrated that complex machine learning algorithms can also be explainable.”
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