The Good Robot

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.

In this episode we talk to Sareeta Amrute, Affiliate Associate Professor at the University of Washington who studies race, labour, and class in global tech economies. Sareeta discusses happened when Rihanna and Greta Thunberg got involved in the Indian farmers protests; how race has wound up in algorithms as an indicator of what products you might want to buy; how companies get out of being responsible for white supremacist material sold across their platforms; why all people who make technology have an ethic, though they might not know it; and what the effects are of power in tech companies lying primarily with product teams. 

In this episode Sophie, author of Full Surrogacy Now and self-defined wayward Marxist, talks about defining good technology for the whole of the biosphere, why the purity of the human species has always been contaminated by our animal and technological origins, why nature is much, much stranger than we think, what that means for the lambs that are now being grown in artificial wombs, and why technologies like birth control and IVF can never liberate women within the power dynamics of our capitalist present.

In this episode we chat to Karen Hao, a prominent tech journalist who focuses on the intersections of AI, data, politics and society. Right now she’s based in Hong Kong as a reporter for the Wall Street Journal on China, tech and society; before this, she conducted a number of high profile investigations for the MIT tech review. In our interview we chat about her series on AI colonialism and how tech companies reproduce older colonial patterns of violence and extraction; why both insiders and outside specialists in AI ethics struggle to make AI more ethical when they’re competing with Big Tech’s bottom line; why companies engaging user attitudes isn’t enough, since we can’t really ever ‘opt out’ of certain products and systems; and her hopes for changing up the stories we tell about the Chinese tech industry. 

In the race to produce the biggest language model yet, Google has now overtaken Open AI’s GPT-3 and Microsoft’s T-NLG with a 1.6 trillion parameter model. In 2021, Meg Mitchell was fired from Google, where she was co-founder of their Ethical AI branch, in the aftermath of a paper she co-wrote about why language models can be harmful if they’re too big. In this episode Meg sets the record straight. She explains what large language models are and what they do, why they’re so important to Google. She tells us why it's a problem that these models don’t understand the significance or meaning of the data that they are trained on, which means that wikipedia data can influence what these models take to be historical fact. She also tells us about how some white men are gatekeeping knowledge about large language models, as well as the culture, politics, power and misogyny at Google that led to her firing.

In this episode, we speak to Soraj Hongladarom, a professor of philosophy and Director of the Center for Science, Technology, and Society at Chulalongkorn University in Bangkok. Soraj explains what makes Buddhism a unique and yet appropriate intervention in AI ethics, why we need to aim for enlightenment with machines, and whether there is common ground for different religions to work together in making AI more inclusive. 

In this episode we chat to Os Keyes, an Ada Lovelace fellow and adjunct professor at Seattle University, and a PhD student at the University of Washington in the department of Human Centered Design & Engineering. We discuss everything from avoiding universalism and silver bullets in AI ethics to how feminism underlies Os’s work on autism and AI and automatic gender recognition technologies.

In this episode, we talk to Dr Alex Hanna, Director of Research at the Distributed AI Research Institute which was founded and directed by her ex-boss at Google Dr Timnit Gebru. Previously a sociologist working on ethical AI at Google and now a superstar in her own right, she tells us why Google’s attempt to be neutral is nonsense, how the word good in ‘good tech’ allows people to dodge getting political when orienting technology towards justice, and why technology may not actually take on the biases of its individual creators but probably will take on the biases of its organisation.

In this episode we chat to Virginia Dignum, Professor of Responsible Artificial Intelligence at the University of Umeå where she leads the Social and Ethical Artificial Intelligence research group. We draw on Dignum’s experience as an engineer and legislator to discuss how any given technology might not be good or bad, but is never valueless; how the public can participate in conversations around AI; how to combat evasions of responsibility among creators and deployers of technology, when they say ‘sorry, the system says so’; and why throwing data at a problem might not make it better.