Decision Management & Optimization
SAN JOSE, Calif. — January 31, 2019
- First xML Challenge showcases the importance of explainability in artificial intelligence (AI).
- Winners from IBM, Duke and New York University demonstrate that complex, machine learning algorithms can also be explainable
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. 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 prize. 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.
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."
About the xML Challenge
The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams are challenged to create machine learning models with both high accuracy and explainability; using financial datasets provided by FICO. Teams are expected to tell the story of their models which are then qualitatively evaluated by data scientists at FICO.
FICO (NYSE: FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956 and based in Silicon Valley, the company is a pioneer in the use of predictive analytics and data science to improve operational decisions. FICO holds more than 190 US and foreign patents on technologies that increase profitability, customer satisfaction and growth for businesses in financial services, manufacturing, telecommunications, health care, retail and many other industries. Using FICO solutions, businesses in more than 100 countries do everything from protecting 2.6 billion payment cards from fraud, to helping people get credit, to ensuring that millions of airplanes and rental cars are in the right place at the right time.
FICO is a registered trademark of Fair Isaac Corporation in the US and other countries.
Greg Jawski for FICO
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