This will be a fascinating session on Australia's computational past and future. Looking forward to it!! pearceycentenary.org.au

.Gradient Institute chief scientist Tiberio Caetano shared the importact of ethical AI in decision making today and in the future. Thanks for having us innovationbay

The anatomy of a decision...

We had some papers accepted into the Ethics of Data Science conf in Sydney next week. "On the impossibility of formalising fairness in ML" by @Finn71454004 and "Designing ethical algorithms has ethical pitfalls" by Tiberio Caetano and others. See you there sydney.edu.au/data-science/n…

Our latest blog post "Whose Ethics?" is a discussion of who decides the particular ethical stance encoded into ethical AI. gradientinstitute.org/blog/4/ #ai #ethics #machinelearning

This reflects our current thinking about ethical AI in general and within the Australian context in particular. gradientinstitute.org/news . We submitted it to the consultation on “AI: Australia’s Ethics Framework”, developed by Data61 and released by Department of Industry, Science and Resources. #aiethics

At Australia with CSIRO Data61’s CherylG supporting Gradient Institute CEO Bill Simpson-Young at the Launch of their #Canberra campus. Gradient are tackling the ethics of artificial intelligence #AI #tech

We have released a whitepaper describing four key challenges that must be addressed in order to make ethical AI real. gradientinstitute.org/blog/5/

If you aren't one of the lucky 14k people at #neurips2019 and you're in Sydney, come and join us at free satellite events on Thu and Mon evenings. Hosted by Sydney AI Hub Women+ in ML/DS and Gradient Institute bit.ly/neurips-sydney

Our new blog post (w. Criteo AI Lab's David Rohde) explains our #neurips workshop paper. We show that representing causality within a std Bayesian approach interpolates between tractable and impossible queries opening up new approaches to causal inference. gradientinstitute.org/blog/6/

How do we build Good AI systems? Bill Simpson-Young from Gradient Institute opens the event with @thewarrencentre

Our paper on a new way of assuring fairness for continuous-valued decisions (like risk scores, credit scores, payments, etc) has been accepted into EDSC2020. Read the draft at arxiv.org/abs/2001.06089

Article in the Financial Review about the impact of AI on insurance with some Gradient Institute quotes eg about the importance of distributing error of predictions fairly. afr.com/technology/aut…

New Gradient Institute paper on new fast methods for fair regression. Useful for fast estimation of fair risk scores, credit scores, personalised payments and other applications with continuous-valued decisions. arxiv.org/abs/2002.06200

Our recent blog post on representing causal models within a standard Bayesian framework has prompted an active discussion amongst top causal inference experts statmodeling.stat.columbia.edu/2020/01/27/cau… Finn

We are thrilled to announce our involvement in the Monetary Authority of Singapore's Veritas work. We are developing the methodology and metrics for measuring fairness in customer marketing in the finance industry (with IAG Firemark Labs and HSBC). mas.gov.sg/news/media-rel…

We’re recruiting for our Summer Scholar program 2020-21. Gain research experience working at the frontier of ethical AI with experienced AI researchers and practitioners. Positions in Sydney and Canberra. Applications close 15/08/2020. gradientinstitute.org/docs/GI-Summer… #machinelearning

The Commission has launched a new technical paper today showing how businesses can take practical steps to address algorithmic bias in AI systems. humanrights.gov.au/about/news/new… Big thanks to our project partners Gradient Institute CHOICE Consumer Policy Research Centre Data61

Interested in Ethical AI? Sign up for a Summer Scholarship at the Gradient Institute working with some great people in this space like Tiberio Caetano and Bill Simpson-Young gradientinstitute.org/about-us/summe… #AI

Read a new article by Gradient's Dan Steinberg and Finn Lattimore showing how machine learning can be used for evidence-based policy (capturing complex relationships in data, mitigating bias in models and using regularisation for better causal estimates) gradientinstitute.org/machine-learni…