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Turbocharging ethical AI in Australia

Gradient Institute and Ethical AI Advisory have formed an alliance to tackle one of the most urgent problems facing Australia today: the development and deployment of artificial intelligence that is both ethical and trustworthy.

Ethics of insurance pricing

Gradient Institute Fellows Chris Dolman, Seth Lazar and Dimitri Semenovich, alongside Chief Scientist Tiberio Caetano, have written a paper investigating the question of which data should be used to price insurance policies. The paper argues that even if the use of some "rating factor" is lawful and helps predict risk, there can be legitimate reasons to reject its use. This suggests insurers should go beyond immediate business and legal considerations, but in addition be guided by a more holistic ethical framework when considering whether to use a certain rating factor to set insurance premiums.

Submission to Australian Human Rights Commission

Gradient Institute Fellow Kimberlee Weatherall and Chief Scientist Tiberio Caetano have written a submission to the Australian Human Rights Commission on their "Human Rights and Technology" discussion paper.

Converting ethical AI principles into practice

Our Chief Scientist, Tiberio Caetano, has summarised some lessons we have learned over the last year creating practical implementations of AI systems from ethical AI principles. Tiberio is a member of the OECD's Network of Experts in Artifical Intelligence and wrote this article for the network's blog.

Fast methods for fair regression

Gradient Institute has written a paper that extends the work we submitted to the 2020 Ethics of Data Science Conference on fair regression in a number of ways. First, the methods introduced in the earlier paper for quantifying the fairness of continuous decisions are benchmarked against “gold standard” (but typically intractable) techniques in order to

Causal inference with Bayes rule

Finn Lattimore, a Gradient Principal Researcher, has published her work on developing a Bayesian approach to inferring the impact of interventions or actions. The work, done jointly with David Rohde (Criteo AI Lab), shows that representing causality within a standard Bayesian approach softens the boundary between tractable and impossible queries and opens up potential new

Practical challenges for ethical AI (White Paper)

Gradient has released a White Paper examining four key challenges that must be addressed to make progress towards developing ethical artificial intelligence (AI) systems. These challenges arise from the way existing AI systems reason and make decisions. Unlike humans, AI systems only consider the objectives, data and constraints explicitly provided by their designers and operators.

Our response to “Artificial Intelligence: Australia’s Ethics Framework”

Gradient Institute made a submission to the Australian Department of Industry, SCience, Energy and Resources for the public consultation on the discussion paper “Artificial Intelligence: Australia’s Ethics Framework (A Discussion Paper)” released by the Department of Industry, Innovation and Science on 5 April 2019.