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Data Work: The Hidden Talent And Secret Logic Fuelling Artificial Intelligence

What happens when new artificial intelligence (AI) tools are integrated into organisations around the world?For example, digital medicine promises to combine emerging and novel sources of data and new analysis techniques like AI and machine learning to improve diagnosis, care delivery and condition management. But healthcare workers find themselves at the frontlines of figuring out new ways to care for patients through, with – and sometimes despite – their data. Paradoxically, new data-intensive tasks required to make AI work are often seen as of secondary importance. Gina calls these tasks data work, and her team studied how data work is changing in Danish & US hospitals (Moller, Bossen, Pine, Nielsen and Neff, forthcoming ACM Interactions).Based on critical data studies and organisational ethnography, this talk will argue that while advances in AI have sparked scholarly and public attention to the challenges of the ethical design of technologies, less attention has been focused on the requirements for their ethical use. Unfortunately, this means that the hidden talents and secret logics that fuel successful AI projects are undervalued and successful AI projects continue to be seen as technological, not social, accomplishments.

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The Rise Of Online Advertising For Non-Monetary Rent Arrangements In The Online Markeplace

The growth of online marketplaces has led to an explosion of a new type of advertisement with potential tenants being asked for just more than their monthly rent. Online marketplaces have made these situations more visible through several features of the platforms that host these ads. Understanding those features can help us better understand how common internet platforms contribute to ongoing gendered inequality.

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Does AI Have Gender?

Machine learning and artificial intelligence make extraordinary discoveries possible, and autonomous systems are being rolled out in vital business and social settings including healthcare, policing, and education. Assumptions about the neutrality and objectivity of data may encode serious social and political bias into the results. High-profile examples show how these systems already incorporate into their design human flaws, biases, and assumptions, especially about women and their role in society. In this talk, Professor Gina Neff will show that explicitly thinking about gender in AI will help designers make AI systems that help humans make better—and fairer—decisions.