FICO Announces Winners of Inaugural xML Challenge
Highlights:
- First xML Challenge showcases the importance of explainability in artificial intelligence (AI).
- Winners from
IBM ,Duke andNew York University demonstrate that complex, machine learning algorithms can also be explainable.
FICO, the leading provider of analytics and decision management technology, together with
Participants were challenged to create machine learning models with both high accuracy and explainability using a real-world dataset provided by FICO.
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
Receiving Honorable Mention and overall second place was
The team representing
"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
About FICO
FICO (NYSE: FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956 and based in
Learn more here. Join the conversation.
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SOURCE FICO
Greg Jawski for FICO, Email: greg.jawski@porternovelli.com, Phone: +1 212-601-8248