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Articles, opinion pieces, white papers, reports and news releases

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Investigating Manipulative Applications of Generative AI

8 December 2023

With support from the Minderoo Foundation, Gradient Institute is exploring how bad actors could leverage the latest pre-trained language models for personalised persuasion and manipulation.

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Gradient Institute Receives Donation from Cadent for AI Safety Research

9 November 2023

Gradient Institute has received a donation from Cadent, an ethical technology studio, to develop research on technical AI Safety.

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Gradient Institute policy recommendations for the AI Safety Summit

27 October 2023

Gradient Institute has prepared a policy paper offering recommendations for the AI Safety Summit, which is to be hosted by the UK Prime Minister Rishi Sunak on the 1st and 2nd of November.

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Telstra Foundation supports Gradient Institute research towards safer mental health chatbots

16 August 2023

Telstra Foundation, through its Young & Connected Fund, has provided Gradient Institute with a seed grant to work towards the safe and effective use of AI and Large Language Models (LLM) in mental health chatbots. Gradient, with its technical expertise in the safe and reponsible use of AI, will use the seed grant to work with mental health experts, young people and others to develop the scope...

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Submission response to AI regulation discussion paper

1 August 2023

Gradient Institute submitted a response to the Department of Industry, Science and Resources' discussion paper “Safe and Responsible AI in Australia”.

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New report to help businesses implement responsible AI

22 June 2023

Gradient Institute, in collaboration with Australia's National Artificial Intelligence Centre (hosted by CSIRO), has released a report that outlines practical measures for businesses to adopt Responsible Artificial Intelligence.

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Gradient starts delivering Generative AI risk control training courses

6 June 2023

Many companies are starting to use applications that use generative AI technologies such as large language models (LLMs). They often know about the risks such as inaccurate results, bad behaviour and privacy/security issues but they struggle in knowing how to control those risks in their organisation. Gradient Institute now offers training courses (2 hours, half-day and full-day) to help...

Tiberio Caetano in Australian Financial Review

Regulators urged to monitor the AI power struggle closely for potential risks

1 May 2023

Dr. Tiberio Caetano, co-founder and chief scientist of Gradient Institute, warns that regulators need to start paying attention to anyone using vast amounts of computer power in machine learning to prevent “bad actors” from creating harmful new AI systems.

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Gradient Fellow Chris Dolman awarded Actuary of the Year

15 September 2022

Gradient Institute Fellow Chris Dolman has been awarded Actuary of the Year by the Actuaries Institute of Australia. Some excerpts from the official announcement:

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Australia's original disruptive festival is back for 2022

24 August 2022

We're excited for this important event run by our long-time collaborators The Ethics Centre! Truth, trust, tech, tattoos, and taboos: Australia’s original disruptive festival, the Festival of Dangerous Ideas returns live and in-person to Carriageworks, Sydney 17–18 September, 2022.

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Banking Finance + Oath (BFO) AI-driven marketing in financial services

9 June 2022

The BFO Young Ambassadors collaborated with Gradient Institute to investigate the ethical concerns surrounding the use of AI in financial services. While some areas of the industry, such as credit scoring, have gained awareness of AI ethics, little attention has been given to AI-driven marketing. Developing a thorough understanding of the ethical considerations will help financial services...

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Yaya Lu joins Gradient Institute as Senior Consultant

2 June 2022

Yaya’s background includes researcher and business analyst in the Australian Public Service, focusing on generating user-centric outputs to address Whole-of-Government and agency needs. With a degree in Software Engineering (Honours) from The Australian National University she is a passionate advocate for bridging the gap between understanding technology and the people who use it, as well as...

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Policy submission on the regulation of AI and automated decision making

24 May 2022

Gradient Institute submitted a response to the Department of the Prime Minister and Cabinet’s Digital Technology Taskforce Issues Paper “Positioning Australia as a Leader in Digital Economy Regulation – Automated Decision Making and AI Regulation”.

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4 May 2022

Future.NSW presented by NSW Government held their thought-leadership event on the 2nd May, 2022. The event brought together the public, the private sector, business and industry leaders and experts that help shape NSW Government. NSW Government supports the building of a smarter, customer centric and a fully digital NSW. Working together with NSW residents, the private sector, industry and...

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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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Gradient Institute Awarded Linkage Grant

12 April 2022

Our Linkage Grant Proposal “Socially Responsible Insurance in the Age of Artificial Intelligence” has been approved by the Australian Research Council.

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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.

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Machine learning helps find links between youth well-being and academic success

17 February 2022

In a first-of-its-kind study published in Scientific Reports, machine learning researchers at Gradient Institute, working in partnership with psychology researchers at the Australian National University, have found significant evidence for causal links between students’ self reported well-being and academic outcomes.

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The Responsible AI Index report has launched

14 October 2021

The Responsible AI Index report is a study of over 400 Australian-based organisations and their awareness of, intentions around, and use of Responsible AI practices. This report, a first in Australia of its kind, was launched in partnership with IAG, Telstra, Fifth Quadrant CX, and Ethical AI Advisory.

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Welcome to the Ethical AI Advisory team

6 October 2021

We are excited to announce that Gradient Institute and Ethical AI Advisory have joined together! We have been working together as partners for the last year and realised how complementary we were – with Gradient working on the technical aspects of ensuring AI is used responsibly and Ethical AI Advisory working on the organisational and governance aspects of this.

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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.

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AI-LEAP Call for Papers

12 July 2021

AI-LEAP is a new Australia-based conference aiming at exploring Artificial Intelligence (AI) from a multitude of perspectives: Legal, Ethical, Algorithmic and Political (LEAP). It draws broadly on computer science, social sciences, and humanities to provide an exciting intellectual forum for a genuinely interdisciplinary exchange of ideas on what is one of the most pressing issues of our time....

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Gradient provides AI Primer for National AI Summit

19 June 2021

At the request of the Australian Government’s Department of Industry, Science, Energy and Resources, Gradient Institute provided an interactive “Artificial Intelligence Primer” training session for attendees at the Techtonic 2.0 National AI Summit. This was to help attendees of the summit:

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.

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Tutorial: Using harms and benefits to ground practical AI fairness assessments in finance

4 March 2021

Gradient’s Chief Practitioner Lachlan McCalman and Principal Researcher Dan Steinberg presented a tutorial, together with our colleagues from Element AI, at the ACM Fairness Accountability and Transparency Conference (FAccT 2021) on 4 March 2021. You can watch a video recording of the full tutorial below.

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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.

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Monetary Authority of Singapore announces success of Veritas Initiative phase 1

6 January 2021

The Monetary Authority of Singapore (MAS) announced the successful conclusion of the first phase of the Veritas initiative which saw the development of the fairness assessment methodology in credit risk scoring and customer marketing. These are the first two use cases to help financial institutions validate the fairness of their Artificial Intelligence and Data Analytics (AIDA) solutions...

Graphical illustration of fairness in binary classification

New tools for fairer AI

24 November 2020

With companies increasingly using AI for decision making in everything from pricing to recruitment, “Addressing the problem of algorithmic bias”, explores how these decision-making systems can result in unfairness.

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Turbocharging ethical AI in Australia

23 September 2020

AI is the fastest growing technology sector in the world with more than US$35 billion invested in 2019, an 18 per cent increase on 2018 (IDC, 2020). About 40% of organisations are predicted to use AI by the end of this year.**

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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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Gradient Institute Chief Scientist appointed to OECD advisory group on artificial intelligence

15 January 2020

Gradient Institute Chief Scientist Tiberio Caetano has been appointed to the OECD Network of Experts on AI (ONE AI). The expert group provides policy, technical and business input to inform OECD analysis and recommendations. It is a multi-disciplinary and multi-stakeholder group.

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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.

Front cover of the AI Ethics Framework discussion paper

Our response to 'Artificial Intelligence: Australia's Ethics Framework'

6 June 2019

Gradient Institute has made a submission as part of the public consultation on the discussion paper “Artificial Intelligence: Australia’s Ethics Framework (A Discussion Paper)”. The paper was developed by CSIRO’s Data61 and released by the Department of Industry, Innovation and Science on 5 April 2019. You can find our submission here.

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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.

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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,...