The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. After a 2019 research paper demonstrated that commercially available facial analysis tools fail to work for women with dark skin, AWS executives went on the attack. Instead of offering up more equitable performance results or allowing the federal government to assess their algorithm like other companies with facial recognition tech have done, AWS executives attempted to discredit study coauthors Joy Buolamwini and Deb Raji in multiple blog posts. More than 70 respected AI researchers rebuked this attack, defended the study, and called on Amazon to stop selling the technology to police, a position the company temporarily adopted last year after the death of George Floyd. But according to the Abuse and Misogynoir Playbook, published earlier this year by a trio of MIT researchers, Amazon's attempt to smear two Black women AI researchers and discredit their work follows a set of tactics that have been used against Black women for centuries.

Our ability to collect and record information in a digital form has exploded as has our adoption of AI systems, which use data to make decisions. But data isn't neutral, and sexism, racism and other forms of discrimination are showing up in our data products. Catherine D'Ignazio, an assistant professor of urban science and planning at the Massachusetts Institute of Technology (MIT), argues we need to do better. Along with Lauren Klein, who directs the Digital Humanities Lab at Emory University, she is the co-author of the new book Data Feminism, which charts a course for a more equitable data science. D'Ignazio also directs MIT's new Data and Feminism lab, which seeks to use data and computation to counter oppression.

Suppose you would like to know mortality rates for women during childbirth, by country, around the world. One option is the WomanStats Project, the website of an academic research effort investigating the links between the security and activities of nation-states, and the security of the women who live in them. The project, founded in 2001, meets a need by patching together data from around the world. Many countries are indifferent to collecting statistics about women's lives. But even where countries try harder to gather data, there are clear challenges to arriving at useful numbers -- whether it comes to women's physical security, property rights, and government participation, among many other issues.

Automated decision making comes of age.