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.

Gradient Institute is participating in the AgileAus20 conference during the month of October. Join our chief practitioner, Dr Lachlan McCalman, for a workshop on Building Ethical AI systems. Over the day, you will explore key considerations for individuals and organisations wanting to embed ethics into their AI. The course is highly interactive and doesn’t require coding skills or a data science background.

Lachlan presented a preview of this workshop at Responsible Tech Summit 2020. Click on this link to watch the workshop preview recording.

Register now to participate in Melbourne, Wednesday 14/10/2020; or Sydney on Friday 16/10/2020.

We’re recruiting for our Summer Scholar program 2020-2021. This is a fantastic opportunity for undergraduate students to get some hands-on experience working on problems on the frontier of artificial intelligence and with a not-for-profit organisation. Our summer scholars will work on concrete research projects translating ethical principles and ideals into the design and implementation of AI systems. To qualify, applicants need to have an excellent academic record, be passionate about exploring new questions, and want to devote their energy to making the world a better place. Applications close 15/08/2020.

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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