{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","uuid":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","normalized_title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","isbns":["9780745338613","9780745338606","9781786803955","9781786803979","9781786803962"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":225,"navigation_url":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%92%8C%E8%B5%84%E6%9C%AC%E4%B8%BB%E4%B9%89%E7%9A%84%E6%9C%AA%E6%9D%A5","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/a2eab101-adfc-4a0d-8142-63a4d11cee02","page_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/page","search_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/search"},"page":{"printed_page":105,"pdf_page":113,"physical_index":113,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"504308123d5c48b3ea60ed78378ab65e6ca5a66ac8648ae0921e0f1ae57a67bb"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"4) Social media defection. More diffuse and invisible than anti-surveillance activism is dissent by subtraction. In the wake of the Cambridge Analytica scandal, leaving social media paradoxically became a social media meme, driven by an amalgam of concerns over digital surveillance and manipulation, the psychological effects of techno-addictions, and revulsion at the extreme commodification of online environments. A ‘Delete Facebook’ campaign sprung up, then subsided. There is, however, evidence that people in the 18 to 24 age range are either curtailing or, occasionally, abandoning social media use, even while their parents become increasingly immersed (Kale 2018). In so far as ML is a big data undertaking, some AI-capital may be impeded by a slow exodus of data-subjects ‘logging off’ social media.\n\n5) Algorithmic bias busting. We have seen the tendency of AIs to amplify pre-existing sexist and racist discrimination. Businesses owned and dominated by white men, and governance systems administered by the same, are producing AI systems that discriminate against women and minorities because they are trained on data sets that reflect the historical levels of hiring, wealth, clean records and career success enjoyed by white men (Noble 2018). Such discriminatory AI affects those who encounter it on the screens of police, border guards and welfare services with particular severity. That algorithmic bias has even been named is, however, evidence of struggles against it. Groups addressing the issue range from the Algorithmic Justice League, fighting ‘the coded gaze (Buolamwini 2018) at the Massachusetts Institute of Technology, to NGOs like the San Francisco-based, non-profit Human Rights Data Analysis Group (HRDAG), which revealed algorithmic bias in US predictive policing software, to community organizations such as Silicon Valley De-Bug, which is involved in immigrant and prisoner rights and economic justice in the San Jose area, or Philadelphia’s Media Mobilizing Project, which is organizing low-wage workers and poor communities (Wykstra 2018). As correcting algorithmic bias increases the accuracy of AI predictions, it can, in theory, be serviceable to capital to improve its niche targeting of commodities and hiring of talented labour-power, even if this requires acknowledging embarrassing and expensive displays of sexism, racism and homophobia: these, at least, are the terms progressive neoliberalism sets for such initiatives.<sup>2</sup> However, in so far as algorithmic bias discriminates against people who are both poor and female, queer or racialized – hence the most vulnerable of the vulnerable – algorithmic justice movements hamper AI-capital’s disciplining and discarding of its so-called surplus populations, challenging the ‘matrix of domination’ that has informed its development (Costanza-Chock 2018).\n\nautomating the social factory\n\n105","previous_page":104,"next_page":106}