This polarization has gendered and racialized dimensions. Early waves of digital automation hit men particularly hard because of their effects on manufacturing employment: the ‘information revolution’ is often cited as a contributory cause to the gradual advance of women in many fields of work. Many of the jobs likely to be AI automated are, however, typically held by women in administrative, secretarial, sales and customer service work, while software engineering is masculinized (Howcroft and Rubery 2018). In North America, the number of women studying computer science is static or falling, while in the UK only one in five of computing students is female. It seems likely that ‘men will disproportionately benefit from the greater demand for STEM skills’ (Boston Consulting Group– Sutton Trust 2017: 21–2). Silicon Valley companies have demonstrated an incapacity to move beyond a ‘brogramming’ culture, and also display ethnic segregation: a report from the US Equal Employment Opportunity Commission found that just 8 per cent of tech sector jobs were held by Hispanics, 7.4 per cent by African Americans, and 36 per cent by women (2016). Such patterns may be intensified by algorithmic hiring tools. In 2018 it was reported that an Amazon AI recruiting technology trained predominantly on men’s résumés had unsurprisingly developed an intrinsic bias against women, having ‘effectively taught itself that male candidates were preferable’ (BBC 2018b). That the system was discarded reflects a growing awareness of and resistance to high-tech sexism, but it is not an isolated example; analysis of automated hiring processes by both LinkedIn (Reese 2016b) and Google (Gibbs 2015) suggest similar sexist biases. At the other end of the spectrum, many of the fastest growing low-wage service jobs are ones in which women and minorities are strongly represented. AI-capital may therefore mark a new round of gendered and racialized work segregation.
automating the social factory
95