During the Long Hot Summer of 1967, race riots erupted across the United States. The 159 riots—or rebellions, depending on which side you took—were mostly clashes between the police and African Americans living in poor urban neighborhoods. The disrepair of these neighborhoods before the riots began and the difficulty in repairing them afterward was attributed to something called redlining, an insurance-company term for drawing a red line on a map around parts of a city deemed too risky to insure.

In an attempt to improve recovery from the riots and to address the role redlining may have played in them, President Lyndon Johnson created the President’s National Advisory Panel on Insurance in Riot-Affected Areas in 1968. The report from the panel showed that once a minority community had been redlined, the red line established a feedback cycle that continued to drive inequity and deprive poor neighborhoods of financing and insurance coverage—redlining had contributed to creating poor economic conditions, which already affected these areas in the first place. There was a great deal of evidence at the time that insurance companies were engaging in overtly discriminatory practices, including redlining, while selling insurance to racial minorities, and would-be home- and business-owners were unable to get loans because financial institutions require insurance when making loans. Even before the riots, people there couldn’t buy or build or improve or repair because they couldn’t get financing.

Because of the panel’s report, laws were enacted outlawing redlining and creating incentives for insurance companies to invest in developing inner-city neighborhoods. But redlining continued. To justify their discriminatory pricing or their refusal to sell insurance in urban centers, insurance companies developed sophisticated arguments about the statistical risks that certain neighborhoods presented.

The argument insurers used back then—that their job was purely technical and that it didn’t involve moral judgments—is very reminiscent of the arguments made by some social network platforms today: That they are technical platforms running algorithms and should not be, and are not, involved in judging the content. Insurers argued that their job was to adhere to technical, mathematical, and market-based notions of fairness and accuracy and provide what was viewed—and is still viewed—as one of the most essential financial components of society. They argued that they were just doing their jobs. Second-order effects on society were really not their problem or their business.

Thus began the contentious career of the notion of “actuarial fairness,” an idea that would spread in time far beyond the insurance industry into policing and paroling, education, and eventually AI, igniting fierce debates along the way over the push by our increasingly market-oriented society to define fairness in statistical and individualistic terms rather than relying on the morals and community standards used historically.

Risk spreading has been a central tenet of insurance for centuries. Risk classification has a shorter history. The notion of risk spreading is the idea that a community such as a church or village could pool its resources to help individuals when something unfortunate happened, spreading risk across the group—the principle of solidarity. Modern insurance began to assign a level of risk to an individual so that others in the pool with her had roughly the same level of risk—an individualistic approach. This approach protected individuals from carrying the expense of someone with a more risk-prone and costly profile. This individualistic approach became more prevalent after World War II, when the war on communism made anything that sounded too socialist unpopular. It also helped insurance companies compete in the market. By refining their risk classifications, companies could attract what they called “good risks.” This saved them money on claims and forced competitors to take on more expensive-to-insure “bad risks.”