We are an independent, not-for-profit research institute whose purpose is to progress the research, design, development and adoption of ethical AI systems.

Our Work

AI System Consulting

We evaluate, design, implement and measure AI systems to check them against ethical goals. We also design, implement and disseminate open source AI-based decision-making tools based on our research into the new science of ethical AI. We then work together with people responsible for decision-making systems to configure them to achieve ethical goals.
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Research

We undertake scientific research in collaboration with universities and other research institutions to advance a science of ethics for AI, and share the findings across the academic community through publications and presentations.
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Training

We provide training and education to people responsible for the technical, managerial, as well as policy and decision-making aspects of AI-based decision systems.
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News

Gradient is active in the research and broader communities of ethical AI. Below are our latest announcements, articles written by Gradient team members, and events we have organised or are attending. For more news please see our news and events page.
This paper extends the work submitted by Gradient to the 2nd Ethics of Data Science Conference on fair regression in a number of ways. Firstly, 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.
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A paper authored by Gradient Institute researchers has been accepted at the 2nd Ethics of Data Science Conference, to be held in Sydney from 25-27 March 2020. The paper studies 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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In this paper (to be presented at the second Ethics of Data Science Conference) we study the problem of how to create quantitative, mathematical representations of fairness that can be incorporated into AI systems to promote fair AI-driven decisions. For discrete decisions (such as accepting or rejecting a loan application), there are well established ways to quantify fairness.
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Gradient Institute, along with the Centre of Translational Data Science from the University of Sydney and the Humanising Machine Intelligence project from the ANU is organising the second edition of the Ethics of Data Science Conference, which will take place from 25-27 March 2020 at the University of Sydney.
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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.
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