Bias In Machine Learning Systems With Katherine Bailey and Oisin Deery

Today we’re talking with Data Scientist Katherine Bailey and Professor of Philosophy Oisin Deery about bias in machine learning systems. Katherine and Oisin share their perspectives on why philosophy and ethics of AI is such a hot topic, the scandals engulfing the tech industry and how philosophy could be applied to chart a new course, and how philosophy can help us frame solutions to bias in machine learning systems.  

Katherine Bailey is the Natural Language Processing lead within Accenture Australia’s AI and

Automation Engineering group. Originally from Dublin, Ireland, her background is in software engineering and data science, with over a decade in the technology industry, primarily in Canada, the US and Australia. Prior to joining Accenture she was Principal Data Scientist at Acquia, a Boston-based Software-as-a-Service company, where she led the company’s Machine Learning initiatives. Katherine speaks and writes regularly on the topic of A.I. and is committed to dispelling the myths and removing the confusion around it, teasing apart the real from the imaginary implications of these technologies, both practical and ethical.

Oisin is Assistant Professor in the Department of Philosophy at Monash University, in Melbourne. His research interests lie at the intersection of philosophy of mind and action, metaphysics, and

ethics. Oisin also works on ethical issues related to artificial intelligence. Oisin completed his Ph.D. in philosophy in 2013 at the University of British Columbia. Katherine and Oisin have a particular interest in bias in Machine Learning systems and have presented at conferences and meetups around the world on this topic, including a presentation at Google in 2018.

  • STEAM versus STEM, how the Arts complements STEM
  • Exploring the feasibility of Artificial General Intelligence
  • How philosophy can help us think about AI and ethics
  • How we define intelligence
  • The limitations of the Turing test
  • The long history of machines fooling people
  • Consciousness and sentience in relation to AI
  • Is AI ushering in a golden age of philosophy?
  • The tendency in tech to assume a question is being asked for the first time
  • Tim Miller’s work on explainable AI and the importance of drawing on social sciences
  • How can tech companies best apply philosophy?
  • The recent scandal surrounding Google’s external AI ethics advisory council?
  • What is ethics washing?
  • How tech is impacting democracy and public debate
  • Different types of bias in word embedding how it can be addressed
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