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How Biased AI Holds Data Science Back (+ Ways to Fix It) by Sr. Data Scientist Sophie Searcy
By Emily Wilson • March 06, 2018
This week, Metis Sr. Data Scientist Sophie Searcy's article, How Biased AI is Holding Us Back, and Two Things We Can Do About It, was published in InformationWeek. In it, she notes that many working in tech think that "because our products and services are based on 0’s and 1’s, everything we put out into the world is fair and logical." Not true, she writes, urging everyone to "take a closer look at the biases that inhabit so much of our work, as well as some of the ways we can work toward a culture of inclusive AI."
She expounds upon ideas of increasing access for diverse groups of young people, career-changers, and professionals interested in the field, as well as ensuring the diversification of senior leadership. We invite you to read (and enjoy) the entire article for much more insight and information on the ever-important topic.
Learn about the Metis commitment to promote and pursue diversity in data science here.
In this post, learn how you can gain access to three types of YouTube data: the videos themselves for use in computer vision tasks, the video transcripts for natural language processing (NLP), and video search results for hybrid machine learning efforts.