ntil now, we’ve been focused on the great things that we can do using ML, and on the ways that it is being used well in real-world applications. But, as you’ve seen throughout this book, ML systems are not perfect or all-knowing. Their behavior is determined by the training that we give them. The way that we train our ML systems will affect the responses that they give, and not always in a positive way. In this chapter, we’ll look at one of the most common challenges in creating AI systems: bias.
The project in this ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.
ntil now, we’ve been focused on the great things that we can do using ML, and on the ways that it is being used well in real-world applications. But, as you’ve seen throughout this book, ML systems are not perfect or all-knowing. Their behavior is determined by the training that we give them. The way that we train our ML systems will affect the responses that they give, and not always in a positive way. In this chapter, we’ll look at one of the most common challenges in creating AI systems: bias.