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)
https://t.co/MCX4MADqEr
Interested in Ethical AI? Sign up for a Summer Scholarship at the @GradientInst working with some great people in this space like @tiberiocaetano and @bsimyo https://t.co/yYrdd7SHHg #AI
@IAGAust@GradientInst Thanks @IAGAust for the support of @GradientInst that made this possible! It's a great bunch of committee members on there with me and I am looking forward to NSW Govt becoming a world leader in ethical AI development and use with our help.
The Commission has launched a new technical paper today showing how businesses can take practical steps to address algorithmic bias in AI systems.
https://t.co/NhcKGYNnOc
Big thanks to our project partners @GradientInst@choiceaustralia@CPRC_research@Data61news
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. https://t.co/m1wSXT1bYT
#machinelearning
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 @IAGAust Firemark Labs and @HSBC). https://t.co/WjR0azGgAb
Our recent blog post on representing causal models within a standard Bayesian framework has prompted an active discussion amongst top causal inference experts https://t.co/5zI1Gy4cyS @Finn_Lattimore
New @GradientInst 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. https://t.co/dZ4ABZMI5Z
Article in the @FinancialReview about the impact of AI on insurance with some @GradientInst quotes eg about the importance of distributing error of predictions fairly. https://t.co/dWWdzSJ2Pn
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 https://t.co/ASEdpE6urb
Our new blog post (w. @CriteoAILab'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. https://t.co/PcG4xTFofD
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 @sydneyaihub@wimlds and @gradientinst https://t.co/oJ4Iqx1w3i
This reflects our current thinking about ethical AI in general and within the Australian context in particular. https://t.co/xcuXnzF2gX . We submitted it to the consultation on “AI: Australia’s Ethics Framework”, developed by @Data61news and released by @IndustryGovAU. #aiethics
Our latest blog post "Whose Ethics?" is a discussion of who decides the particular ethical stance encoded into ethical AI. https://t.co/4VaEYug1JM #ai#ethics#machinelearning
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 @tiberiocaetano and others. See you there https://t.co/jaTwbU3cpw