In an effort to hire better and hire faster, increasingly many organizations are turning to data-driven recruiting and hiring strategies. These strategies usually involve gathering data on applicants and trying to tie those data points to successful hires. Data scientist Jane Adams examines the ways in which these strategies actually fail to achieve the intended result, and more importantly how they perpetuate discriminatory hiring practices. The severe limitations of these strategies are not unique to recruiting and hiring. We will discuss how to think critically about data-driven strategies in a broader context, a crucial capability as these strategies are inserted into more and more aspects of our lives.
Jane Adams is a data scientist at Two Sigma Investments, where she spends a lot of time thinking about how data are going to fail. Previously, she was a data scientist at Case Commons, a nonprofit that builds software for caseworkers in child welfare, where her team was responsible for consulting on UX patterns to enhance data quality, conducting research on child welfare policy, and determining best practices using those same data, among other things. Jane holds a BA from the Gallatin School of Individualized Study at New York University and an MS in urban data science from New York University. She is a frequent speaker at local meetups and international conferences on topics ranging from how to not accidentally hurt people with data to how ants find your picnic basket.
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