Raw LLM Responses

Not necessarily. Also your analogy doesn't really work here. The elevator is limited to the strength of the cables and the equipment while nokias facial recognition algorithms aren't limited to one demographic. Their implementation just simply didn't account for differences in features, which is hard to do but regardless, it didn't work optimally by their standards. The bias would come from the dataset. If the data set includes more Asian faces as data points, you might be able to reduce that bias. If you're Nokia and you're trying to sell in an Asian country, you'll want your non blink feature to work with minimal error. However, you would notice that then you might have another issue of potentially misidentifying a caucasian user who is blinking as Asian. The solution usually just involves more data points in your data set but sometimes the bias is unavoidable, especially if you are in an application where diverse data is sparse, but if you aren't in that situation, with enough epochs and larger training data sets, the bias will be reduced. I believe the issue is that you're associating the more often used definition of bias instead of the statistics definition which refers to the tendency of a measurement process to over or underestimate the value of a population parameter. They're not saying that the math is biased, but rather the implementation is not reaching the optimal solution in the given cases. If we're conscious of how bias in datasets affects the performance of algorithms, we'll be able to design them better.