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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.

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.

Whose ethics?

We at the Gradient Institute are often asked who decides the particular ethical stance encoded into an ethical AI system. In particular, because we work on building such systems, the question also takes the form of “whose ethics” we will encode into them. Our Chief Practitioner, Lachlan McCalman, has written a blog post to address such questions.

Ignorance isn’t bliss

Societies are increasingly, and legitimately, concerned that automated decisions based on historical data can lead to unfair outcomes for disadvantaged groups. One of the most common pathways to unintended discrimination by AI systems is that they perpetuate historical and societal biases when trained on historical data. Two of our Principal Researchers, Simon O'Callaghan and Alistair Reid, discuss whether we can improve the fairness of a machine learning model by removing sensitive attribute fields from the data.

Helping machines to help us

Our Chief Scientist, Tiberio Caetano, has written a blog post outlining Gradient Institute's approach to building ethical AI.