Articles

  • Caution: metaverse ahead
    Gradient Institute’s Chief Practitioner, Lachlan McCalman wrote this latest blog post about the metaverse on Medium. The post argues that the metaverse has the potential to have a profound impact on the world, and as a result, we would be wise to plan conservatively and ensure that this technological convergence helps, rather than hurts, humanity
  • De-risking automated decisions
    Today, in collaboration with Minderoo Foundation, we are releasing a report on de-risking automated decisions, which includes practical guidance for AI governance and AI risk management. Many organisations are using AI to make consequential decisions, such as deciding who gets insurance, a loan, or a job. When humans delegate decisions to AI, problems can happen
  • AI Impact Control Panel
    In partnership with Minderoo Foundation, Gradient Institute has released the first version of our AI impact control panel software. This tool helps decision-makers balance and constrain their system’s objectives without having to be ML experts. There is no objectively ‘correct’ solution to this balance of objectives: the answer depends on the values and priorities of
  • Designing a practical approach to AI fairness
    Gradient Institute, along with collaborators from ServiceNow, Vector Institute and The University of Tübingen, just published an article in the January edition of IEEE Computer laying out conceptual foundations for practical assessment of  AI fairness.  The article describes an AI fairness assessment approach developed by the authors along with collaborators from financial institutions,  tech companies
  • Welcome to the Ethical AI team
    We are excited to announce that Gradient Institute and Ethical AI Advisory have joined together! We have been working together as partners for the last year and realised how complementary we were – with Gradient working on the technical aspects of ensuring AI is used responsibly and Ethical AI Advisory working on the organisational and
  • Machine learning as a tool for evidence-based policy
    In this article, Gradient’s Dan Steinberg and Finn Lattimore show how machine learning can be used for evidence-based policy. They show how it can capture complex relationships in data, helping mitigate bias from model mis-specification and how regularisation can lead to better causal estimates.
  • AI-LEAP Call for Papers
    AI-LEAP is a new Australia-based conference aiming at exploring Artificial Intelligence (AI) from a multitude of perspectives: Legal, Ethical, Algorithmic and Political (LEAP). It draws broadly on computer science, social sciences and humanities to provide an exciting intellectual forum for a genuinely interdisciplinary exchange of ideas on what’s one of the most pressing issues of our times. The first edition will take place in Canberra in December 2021.
  • Explainer on Causal Inference with Bayes Rule
    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.
  • Practical fairness assessments for AI systems in finance
    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.
  • Next-best-action for social good
    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.
  • Artificial intelligence can deepen social inequality. Here are 5 ways to help prevent this
    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.
  • Ethics of insurance pricing
    Gradient Institute Fellows Chris Dolman, Seth Lazar and Dimitri Semenovich, alongside Chief Scientist Tiberio Caetano, have written a paper investigating the question of which data should be used to price insurance policies. The paper argues that even if the use of some “rating factor” is lawful and helps predict risk, there can be legitimate reasons to reject its use. This suggests insurers should go beyond immediate business and legal considerations, but in addition be guided by a more holistic ethical framework when considering whether to use a certain rating factor to set insurance premiums.
  • Converting ethical AI principles into practice
    Our Chief Scientist, Tiberio Caetano, has summarised some lessons we have learned over the last year creating practical implementations of AI systems from ethical AI principles. Tiberio is a member of the OECD’s Network of Experts in Artifical Intelligence and wrote this article for the network’s blog.
  • Fast methods for fair regression
    Gradient Institute has written a paper that extends the work we submitted to the 2020 Ethics of Data Science Conference on fair regression in a number of ways. First, 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
  • Using probabilistic classification to measure fairness for regression
    In this paper 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.
  • Causal inference with Bayes rule
    Finn Lattimore, a Gradient Principal Researcher, has published her work on developing a Bayesian approach to inferring the impact of interventions or actions. The work, done jointly with David Rohde (Criteo AI Lab), shows that representing causality within a standard Bayesian approach softens the boundary between tractable and impossible queries and opens up potential new
  • Practical challenges for ethical AI (White Paper)
    Gradient has released a White Paper examining four key challenges that must be addressed to make progress towards developing ethical artificial intelligence (AI) systems. These challenges arise from the way existing AI systems reason and make decisions. Unlike humans, AI systems only consider the objectives, data and constraints explicitly provided by their designers and operators.
  • Whose ethics?
    We at the Gradient Institute are often asked who decides the particular ethical stance encoded into an ethical AI system. In particular, because we work on building such systems, the question also takes the form of “whose ethics” we will encode into them. Our Chief Practitioner, Lachlan McCalman, has written a blog post to address such questions.
  • Ignorance isn’t bliss
    Societies are increasingly, and legitimately, concerned that automated decisions based on historical data can lead to unfair outcomes for disadvantaged groups. One of the most common pathways to unintended discrimination by AI systems is that they perpetuate historical and societal biases when trained on historical data. Two of our Principal Researchers, Simon O’Callaghan and Alistair Reid, discuss whether we can improve the fairness of a machine learning model by removing sensitive attribute fields from the data.
  • Helping machines to help us
    Our Chief Scientist, Tiberio Caetano, has written a blog post outlining Gradient Institute’s approach to building ethical AI.