Bias and discrimination
Decisions made by an algorithm are not necessarily any better or any worse than those made by a human. Every person is likely to have biases, even if they actively try to counteract them, and discriminatory practices can become culturally institutionalized. There is hope that basing decisions on data, rather than subjective and instinctive assessments by people, could be more fair and give a better chance to people who are often overlooked in the traditional system .
When we develop predictive analytics systems, we are not merely automating a human’s decision by using software to specify the rules for when to say yes or no; we are even leaving the rules themselves to be inferred from data. However, the patterns learned by these systems are opaque: even if there is some correlation in the data, we may not know why. If there is a systematic bias in the input to an algorithm, the system will most likely learn and amplify that bias in its output .
In many countries, anti-discrimination laws prohibit treating people differently depending on protected traits such as ethnicity, age, gender, sexuality, disability, or beliefs. Other features of a person’s data may be analyzed, but what happens if they are correlated with protected traits? For example, in racially segregated neighborhoods, a person’s postal code or even their IP address is a strong predictor of race. Put like this, it seems ridiculous to believe that an algorithm could somehow take biased data as input and produce fair and impartial output from it . Yet this belief often seems to be implied by proponents of data-driven decision making, an attitude that has been satirized as “machine learning is like money laundering for bias” .
Predictive analytics systems merely extrapolate from the past; if the past is discriminatory, they codify that discrimination. If we want the future to be better than the past, moral imagination is required, and that’s something only humans can provide . Data and models should be our tools, not our masters.