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, 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.

We were at Responsible Tech 2020

Announcements
Jun 15, 2020

Mid-June, Gradient Institute participated in the Responsible Tech Summit 2020. One of the questions this conference focused on was how to innovate responsibly and move towards a positive future where tech is useful, trusted and trustworthy.

To help address this through a specific case-study, our Chief Practitioner Lachlan McCalman presented a talk about building more ethical AI targeted marketing systems. This talk examined some practical approaches to understanding and controlling the ethical impact of AI targeted marketing systems and how vital it is to ensure these systems do not perpetuate or reinforce systemic disadvantage or cause unintended harm. Click on this link for the recording.

Lachlan also gave a preview of the “Building Ethical AI Systems” workshop Gradient Institute will present at AgileAus20 in October this year, which will explore key considerations for individuals and organisations that want to embed ethics into their AI. Click on this link to watch the workshop preview recording.

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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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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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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This blog entry contains the executive summary of Gradient Institute’s new White Paper. The full paper can be found here. This White Paper examines four key challenges that must be addressed to make progress towards developing ethical artificial intelligence (AI) systems.
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