We don&#x27;t need fair and unbiased inputs. In the real world these don&#x27;t exist, and the field of statistics has devoted extensive effort to the problem of getting good answers out of bad inputs.<p>E.g., for an unbiased measurement (m = x+g, g ~ N(0,\sigma^2)), you use the likelihood function G(x-m,\sigma^2). For a biased measurement g ~ N(b, \sigma^2), you use the likelihood function G(x-m+b, \sigma^2). Here G(x,\sigma^2) is the pdf of the distribution the noise g is drawn from.<p>But this is mainly just something that can be determined by studying the process. Mandating specific outcomes is just a way of ensuring that the predictor can&#x27;t discover certain facts.