Contrary to what tech news outlets like Mashable, Techcrunch, and Wired tell us about the future of work and the purported "rise of the machines", we're seeing that Machine Learning developments are still far away from general AI, and hence far away from becoming real problem solvers. However, it is precisely because of this early stage of development that engineers and mathematicians need to talk about the real, down-to-earth implications of imbuing their algorithms and models with ethical considerations. We will discuss these, specifically for supervised, unsupervised and reinforcement learning, and will share some flops and bloopers from both sides of the border. We will then finish this talk with 3 frameworks for ethical modelling.
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