Consequences of unaddressed bias and the importance of fairness
Ever been at the receiving end of a raw deal? Remember how that felt? Now, imagine that happening systematically, over and over again, thanks to an ML model. Not a pretty picture, right? That’s exactly what happens when bias goes unaddressed in AI systems.
Consider a recruitment algorithm that has been trained on a skewed dataset. It might consistently screen out potential candidates from minority groups, leading to unfair hiring practices. Or, imagine a credit scoring algorithm that’s a little too fond of a particular zip code, making it harder for residents of other areas to get loans. Unfair, right?
These real-world implications of bias can severely erode trust in AI/ML systems. If users feel that a system is consistently discriminating against them, they might lose faith in its decisions. And let’s be honest – no one wants to use a tool that they believe is biased against them.
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