Listen now | Part 7 of 8 in the ML syllabus series. This set of topics was either going to be the foundation to start this series or it was going to be collected as a set of thoughts at the end. You can tell that obviously I demurred from starting with ethics, fairness, bias, and privacy in machine learning until the full foundation was set for the topics under consideration.
Your posts are always comprehensive and well researched. It is sobering that "ethics, fairness, bias, and privacy", all of which are CLEARLY signficant concerns regarding ML are difficult to find a lot of content about. It would not be surprising that the founder advantage drives the research to take shortcuts in order to press an advantage. Perhaps this is not true, but it would not be surprising if it is. This would be not unlike research direction in the life-sciences and life creating/manipulating technologies like CRISPR.
Your posts are always comprehensive and well researched. It is sobering that "ethics, fairness, bias, and privacy", all of which are CLEARLY signficant concerns regarding ML are difficult to find a lot of content about. It would not be surprising that the founder advantage drives the research to take shortcuts in order to press an advantage. Perhaps this is not true, but it would not be surprising if it is. This would be not unlike research direction in the life-sciences and life creating/manipulating technologies like CRISPR.