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An Intelligence In Our Image: The Risks Of Bias And Errors In Artificial Intelligence

Right now, artificial intelligence (AI) and countless algorithms are integrated into our daily life. Because of the efficiency they bring into the table, the use of AI is only expected to widen. With humanity becoming more and more reliant on this technology, it is only natural to think about the implications. In contrast to the common impression that AI and algorithms are impartial and infallible, these technologies can fail miserably. William Welser IV and Osonde Osoba’s An Intelligence in Our Image: The Risks of Bias and Errors in Artificial Intelligence evaluates algorithms and AI — which they group together under the…
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Google I/O 2019 | Machine Learning Fairness: Lessons Learned

Google I/O 2019 | Machine Learning Fairness: Lessons Learned ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and improvements to models, including open source datasets and tools such as TensorFlow Model Analysis. This session will enable developers to proactively think about fairness in product development.   Speakers: Tulsee Doshi and Jacqueline Pan Session ID: T8ACB1
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