Data Ethics Videos - Sydney - 3 Sept 2019 #video
Matt Moore <innotecture@...>
The following talks and Q&A from our recent event may be of interest.
Passiona Cottee: https://youtu.be/0h3ekHQCK9M
Matthew Beard: https://youtu.be/Hk9bKuCRYsA
Elija Perrier: https://youtu.be/d8f34_ND2lc
Tiberio Caetano: https://youtu.be/NSkbIu2ILvc
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Data is now seen as a key resource and artificial intelligence applications are being used more and more by businesses and governments for decision-making, optimization and automation. It is becoming increasingly clear that if these activities are badly managed, people can suffer harm as a result of bad decisions, thoughtless design, and embedded prejudices. This session will explore the practicalities of: - Understanding the broader impact and risks of data design decisions. - Engaging the right stakeholder groups to ensure these risks are mitigated. - Making trade-offs in the design process.
Passiona Cottee collaborates on projects at the nexus of data, automation, privacy and ethics across both public and private sectors. She currently works as data scientist at the CBA and a sessional lecturer in privacy law at UTS. Passiona fuses Bachelor, Graduate and Masters qualifications in law and data science to enable the ethical use of machine learning.
Matt Beard is a husband, dad, pop culture nerd and moral philosopher with an academic background in applied and military ethics. He is an Associate Lecturer at the University of Notre Dame Australia. Matt is also a Fellow at The Ethics Centre, undertaking research into ethical principles for technology.
Elija Perrier is a PhD candidate in quantum machine learning & AI at UTS. He is also a professional support lawyer at Hall & Wilcox.
Tiberio Caetano is Chief Scientist at the Gradient Institute. He has spent the last 20 years working on machine learning in numerous roles as a student, researcher, academic, entrepreneur and practitioner. He spent 10 years at NICTA and in 2012 he co-founded Ambiata, a data science NICTA spin-off focusing on applying rigorous scientific methodologies for personalised decision-making using machine learning, causal inference and randomised controlled trials.