The Good Robot

In this episode we talk to Arjun Subramonian, a Computer Science PhD student at UCLA conducting machine learning research and a member of the grassroots organisation Queer in AI. In this episode we discuss why they joined Queer in AI, how Queer in AI is helping build artificial intelligence directed towards better, more inclusive, and queer futures, why ‘bias’ cannot be seen as a purely technical problem, and why Queer in AI rejected Google sponsorship. 

In this episode we chat to Su Lin Blodgett, a researcher at Microsoft Research in Montreal, on whether you can use AI to measure discrimination, why AI can never be de-biased, and how AI shows us that categories like gender and race are not as clear cut as we think they are.

Ever worried that AI will wipe out humanity? Ever dreamed of merging with AI? Well these are the primary concerns of transhumanism and existential risk, which you may not have heard of, but whose key followers include Elon Musk and Nick Bostrom, author of Superintelligence. But Joshua Schuster and Derek Woods have pointed out that there are serious problems with transhumanism’s dreams and fears, including its privileging of human intelligence above all other species, its assumption that genocides are less important than mass extinction events, and its inability to be historical when speculating about the future. They argue that if we really want to make the world and its technologies less risky, we should instead encourage cooperation, and participation in social and ecological issues. 

Science fiction writer Chen Qiufan ( Stanley Chen), author of Waste Tide, discusses the feedback loop between science fiction and innovation, what happened when he went to live with shamans in China, how science fiction can also be a psychedelic, and why it’s significant that linear time arrived from the West and took over ideas of circular or recurring time between Chinese dynasties. 

In this episode, the historian of science Lorraine Daston explains why science has long been allergic to emotion, which is seen to be the enemy of truth. Instead, objective reason is science’s virtue. She explores moments where it’s very difficult for scientists not to get personally involved, like when you’re working on your pet hypothesis or theory, which might lead you to select data that confirms your hypothesis, or when you’re confronted with some anomalies in your dataset that threaten a beautiful and otherwise perfect theory. But Lorraine also reminds us that the desire for objectivity can itself be an emotion, as it was when Victorian scientists expressed their heroic masculine self-restraint. She also explains why we should only be using AI for the parts of our world which are actually predictable, and how it’s not just engineers who debug algorithms, now that task is being outsourced to us - the consumers - as we’re the ones who are now forced to flag downstream effects when things go wrong.

How should governments collect personal data? In this episode, we talk to Dr Kevin Guyan about the census, and the best ways of asking people to identify themselves. We discuss why surveys that you fill in by hand offer less restrictive options for self-identification than online forms, and how queer communities are not just identified but produced through the counting of a census. As Kevin reminds us, who does the counting affects who is counted. We also discuss why looking at histories of identifying as heterosexual and cisgender is also beneficial to queer communities. 

In this episode we speak to two brilliant professors here at Cambridge, Mónica Moreno Figueroa and Ella McPherson about a data project they launched at the University of Cambridge to track everyday racism in the university. We discuss using technology for social good without being obsessed with the technology itself and the importance of tracking how racism dehumanises people, confuses us about each other, and causes physical suffering, which students of colour have to deal with on top of the ordinary stress of their uni degree. 

In this episode, we talk to Louise Amoore, professor of political geography at Durham and expert in how machine learning algorithms are transforming the ethics and politics of contemporary society. Louise tells us how politics and society have shaped computer science practices. This means that when AI clusters data and creates features and attributes, and when its results are interpreted, it reflects a particular view of the world. In the same way, social views about what is normal and abnormal in the world are being expressed through computer science practices like deep learning.  She emphasises that computer science can solve ethical problems with help from the humanities, which means that if you work with literature, languages, linguistics, geography, politics and sociology, you can help create AIs that model the world differently. 

In this episode we talk to Sarah Franklin, a leading figure in feminist science studies and the sociology of reproduction. In this tour de force of IVF ethics and feminism through the ages, Sarah discusses ethical issues in reproductive technologies, how they compare to AI ethics, how feminism through the ages can help us, Shulamith Firestone’s techno-feminist revolution, and the violence of anti-trans movement across the world. 

In this episode we chat to Michelle N. Huang, Assistant Professor of English and Asian American literature at Northwestern University. Chatting with Michelle is bittersweet, as we think collectively together about anti-Asian racism and how it intersects with histories and representations of technological development in the context of intensified violence against Asian American and Asian diaspora communities during the COVID-19 pandemic. We discuss why the humanities really matter when thinking about technology and the sciences, Michelle’s amazing film essay Inhuman Figures which examines and subverts racist tropes and stereotypes about Asian Americans; why the central idea of looking at what's been discarded, devalued, and finding different values and ways of doing things defines the power of feminist science studies; and what it means to think about race on a molecular level.