Responsible Artificial Intelligence
We are an independent, nonprofit research institute that works to build ethics, accountability and transparency into AI systems: developing new algorithms, training organisations operating AI systems and providing technical guidance for AI policy development.
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.
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.
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.
Gradient’s Lachlan McCalman and Dan Steinberg presented a tutorial, along with colleagues from Element AI, at the ACM Fairness Accountability and Transparency Conference on 4 March 2021. See the video.
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.
Gradient Institute is developing new techniques and tools for machine learning based causal inference and working with the ACT Education Directorate to apply them to better understand the factors behind school student outcomes.
Gradient Institute is working with the central bank and financial regulatory body of Singapore, the Monetary Authority of Singapore, to develop methodology, metrics and tools that enable the country’s financial institutions to evaluate their AI and data analytics solutions to ensure they are being…
Gradient Institute worked with the Australian Human Rights Commission (AHRC), the Consumer Policy Research Centre, CHOICE and CSIRO’s Data61 to develop a report on algorithmic bias.
This instructor-led and guided one-day course (18 May 2021) is designed for professionals who use, or interact closely, with machine learning or statistical modelling, as well as the technical teams building them. It provides practical insights to AI uses in business in an interactive…
This two-day course (25-26 May 2021) develops the technical skills required to build systems that use machine learning to make automated decisions whilst also accounting for ethical objectives. It includes presentations, discussions, a group project, and hands-on interactive exercise modules.