Category: Article

Choose a category

Caution: metaverse ahead

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 and ensure that this technological convergence helps, rather than hurts, humanity

De-risking automated decisions

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. Many organisations are using AI to make consequential decisions, such as deciding who gets insurance, a loan, or a job. When humans delegate decisions to AI, problems can happen

AI Impact Control Panel

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. There is no objectively ‘correct’ solution to this balance of objectives: the answer depends on the values and priorities of

Designing a practical approach to AI fairness

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.  The article describes an AI fairness assessment approach developed by the authors along with collaborators from financial institutions,  tech companies

Welcome to the Ethical AI team

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

Machine learning as a tool for evidence-based policy

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.

AI-LEAP Call for Papers

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’s one of the most pressing issues of our times. The first edition will take place in Canberra in December 2021.

Explainer on Causal Inference with Bayes Rule

In this article, Finn Lattimore and David Rohde show how a Bayesian approach to inferring the impact of interventions or actions representing causality softens the boundary between tractable and impossible queries, and opens up potential new approaches to causal inference.

Practical fairness assessments for AI systems in finance

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.

Next-best-action for social good

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.