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.
Read more


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.
Read more


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


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.
Read more
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.
Read more
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.
Read more
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.
Read more
Gradient Institute, along with Verge Labs and Women in Machine Learning and Data Science, will be hosting two free meetup events to coincide with the NeurIPS conference. Curated content from NeurIPS will be streamed and discussed by local researchers and practitioners.
Read more

Contact Us



Subscribe to our newsletter and get notifications of our blog posts and news about upcoming events.

Please select all the ways you would like to hear from Gradient Institute:

You can unsubscribe at any time by clicking the link in the footer of our emails.

We use Mailchimp as our marketing platform. By clicking below to subscribe, you acknowledge that your information will be transferred to Mailchimp for processing. Learn more about Mailchimp's privacy practices here.

Read Gradient Institute's privacy policy here.

Gradient Instititute is enabled by the vision of:

We work to pursue our vision in partnership and collaboration with: