Articles

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Caution: metaverse ahead

21 April 2022

Gradient Institute’s Chief Practitioner, Lachlan McCalman wrote this latest blog post about the metaverse on Medium. The post argues that the metaverse has the potential to have a profound impact on the world, and as a result, we would be wise to plan conservatively...

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De-risking automated decisions

16 March 2022

Today, in collaboration with Minderoo Foundation, we are releasing a report on de-risking automated decisions, which includes practical guidance for AI governance and AI risk management.

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AI Impact Control Panel

16 March 2022

In partnership with Minderoo Foundation, Gradient Institute has released the first version of our AI impact control panel software. This tool helps decision-makers balance and constrain their system’s objectives without having to be ML experts.

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Designing a practical approach to AI fairness

22 February 2022

Gradient Institute, along with collaborators from ServiceNow, Vector Institute and The University of Tübingen, just published an article in the January edition of IEEE Computer laying out conceptual foundations for practical assessment of AI fairness.

Charts showing linear and nonlinear models

Machine learning as a tool for evidence-based policy

23 July 2021

In this article, Gradient’s Dan Steinberg and Finn Lattimore show how machine learning can be used for evidence-based policy. They show how it can capture complex relationships in data, helping mitigate bias from model mis-specification and how regularisation can lead to better causal estimates.

Equations expanding Bayes Rule

Explainer on Causal Inference with Bayes Rule

27 April 2021

In this post we explain a Bayesian approach to inferring the impact of interventions or actions. We show that representing causality within a standard Bayesian approach softens the boundary between tractable and impossible queries and opens up potential new approaches to causal inference. This post is a detailed but informal presentation of our Arxiv papers: Replacing the do calculus with Bayes...

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Practical fairness assessment for AI systems in finance

15 April 2021

Gradient Institute’s Chief Practitioner, Lachlan McCalman, describes our collaborative work with the Monetary Authority of Singapore and industry partners to develop a practical AI Fairness assessment methodology.

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Next-best-action for social good

22 March 2021

Gradient’s Chief Scientist, Tiberio Caetano, explains how next-best-action systems are often used to optimise business metrics and individual customer outcomes, but questions whether they could also become a vehicle for promoting social good.

Stylised source code

Preventing AI from deepening social inequality

13 January 2021

An article in The Conversation by Gradient’s Tiberio Caetano and Bill Simpson-Young discussing a technical paper co-written with Australian Human Rights Commission, Consumer Policy Research Centre, CHOICE and CSIRO’s Data61.

Cover of ethics of insurance pricing article

Ethics of insurance pricing

4 August 2020

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...

Cover of AHRC submission paper

Submission to Australian Human Rights Commission

25 May 2020

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.

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Converting ethical AI principles int practice

11 May 2020

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.

Plots showing the main results of the paper

Fast methods for fair regression

25 February 2020

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 test their efficacy. The paper also adapts...

Plots showing the main results of the paper

Using probabilistic classification to measure fairness for regression

18 February 2020

Gradient Institute have released a paper (to be presented at the 2020 Ethics of Data Science Conference) studying the problem of how to create quantitative, mathematical representations of fairness that can be incorporated into AI systems to promote fair AI-driven decisions.

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Causal inference with Bayes rule

13 December 2019

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 approaches to causal...

Front cover of Gradient Institute white paper

Practical challenges for ethical AI (White Paper)

3 December 2019

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.

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Whose ethics?

25 March 2019

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.

Picture of a person with their head in the sand

Ignorance isn't bliss

6 December 2018

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,...

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Helping machines to help us

6 December 2018

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,...