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The Ethics of Data Science Conference

27 March, 2019 - 29 March, 2019

$50 – $1000

From self-driving cars to automated personal assistants, artificial intelligence is well on its way to impacting every facet of our daily lives. Already, industries and governments are relying on machine learning to make important decisions that will have a real effect on the lives of consumers and citizens.

This raises an important question: how can we make algorithms more ethical than humans?

Hosted by the University of Sydney from 27-29 March 2019, this forum will bring together world-renowned experts to address the current crisis in confidence around algorithms.

Algorithms are a fundamental tool in everyday machine learning and artificial intelligence, but experts have identified a number of ethical problems, including biased modelling and inaccurate data. Our speakers – who span diverse disciplines such as ethics, law, and artificial intelligence – will:

  • review current research and practice relating to the ethics around algorithms
  • identify solutions for creating a new generation of ethical data science techniques.

This symposium will review current research and practice relating to the ethical aspects of algorithms, assess limitations, and identify the work required to pave the way for a new generation of ethical data science techniques. This symposium is an exciting opportunity to exchange views on the viability, legitimacy, and complexity of algorithmic decision-making.

Topics covered include:

  • biased data
  • accountability, consequences and costs of automated decisions
  • personal data usage in political campaigns and election forecasting
  • Morally based decision-making under uncertainty
  • privacy and cybersecurity
  • automation and economic consequences
  • artificial intelligence and human rights


27 March, 2019
29 March, 2019
$50 – $1000


F23 Building Auditorium
Corner of Eastern Avenue and City Road The University of Sydney
Sydney, NSW Australia
+ Google Map


The Warren Centre
Centre for Translational Data Science

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