Getting a Read on Responsible AI

There is great promise and potential in artificial intelligence (AI), but if such technologies are built and trained by humans, are they capable of bias? Absolutely, says William Wang, the Duncan and Suzanne Mellichamp Chair in Artificial Intelligence and Designs at UC Santa Barbara, who will give the virtual talk "What is Responsible AI," at 4 p.m. Tuesday, Jan. 25, as part of the UCSB Library's Pacific Views speaker series (register here). "The key challenge for building AI and machine learning systems is that when such a system is trained on datasets with limited samples from history, they may gain knowledge from the protected variables (e.g., gender, race, income, etc.), and they are prone to produce biased outputs," said Wang, also director of UC Santa Barbara's Center for Responsible Machine Learning. "Sometimes these biases could lead to the'rich getting richer' phenomenon after the AI systems are deployed," he added. "That's why in addition to accuracy, it is important to conduct research in fair and responsible AI systems, including the definition of fairness, measurement, detection and mitigation of biases in AI systems."