For example, predictive analytics is a major part of the “Big Data” hype. Using data analysis to predict the weather, or the spread of diseases, is one thing ; it is another matter to predict whether a convict is likely to reoffend, whether an applicant for a loan is likely to default, or whether an insurance customer is likely to make expensive claims. The latter have a direct effect on individual people’s lives.
Naturally, payment networks want to prevent fraudulent transactions, banks want to avoid bad loans, airlines want to avoid hijackings, and companies want to avoid hiring ineffective or untrustworthy people. From their point of view, the cost of a missed business opportunity is low, but the cost of a bad loan or a problematic employee is much higher, so it is natural for organizations to want to be cautious. If in doubt, they are better off saying no.
However, as algorithmic decision-making becomes more widespread, someone who has (accurately or falsely) been labeled as risky by some algorithm may suffer a large number of those “no” decisions. Systematically being excluded from jobs, air travel, insurance coverage, property rental, financial services, and other key aspects of society is such a large constraint of the individual’s freedom that it has been called “algorithmic prison” . In countries that respect human rights, the criminal justice system presumes innocence until proven guilty; on the other hand, automated systems can systematically and arbitrarily exclude a person from participating in society without any proof of guilt, and with little chance of appeal.