In one study published 15 years ago, two people applied for a job. Their résumés were about as similar as two résumés can be. One person was named Jamal, the other Brendan.

In a study published this year, two patients sought medical care. Both were grappling with diabetes and high blood pressure. One patient was black, the other was white.

Both studies documented racial injustice: In the first, the applicant with a black-sounding name got fewer job interviews. In the second, the black patient received worse care.

But they differed in one crucial respect. In the first, hiring managers made biased decisions. In the second, the culprit was a computer program.

As a co-author of both studies, I see them as a lesson in contrasts. Side by side, they show the stark differences between two types of bias: human and algorithmic.

[Question: “Who or what created the algorithm?”]

Marianne Bertrand, an economist at the University of Chicago, and I conducted the first study: We responded to actual job listings with fictitious résumés, half of which were randomly assigned a distinctively black name.

The study was: “Are Emily and Greg more employable than Lakisha and Jamal?”

The answer: Yes, and by a lot. Simply having a white name increased callbacks for job interviews by 50 percent.

I published the other study in the journal Science in late October with my co-authors: Ziad Obermeyer, a professor of health policy at University of California at Berkeley; Brian Powers, a clinical fellow at Brigham and Women’s Hospital; and Christine Vogeli, a professor of medicine at Harvard Medical School. We focused on an algorithm that is widely used in allocating health care services, and has affected roughly a hundred million people in the United States.

To better target care and provide help, health care systems are turning to voluminous data and elaborately constructed algorithms to identify the sickest patients.

We found these algorithms have a built-in racial bias. At similar levels of sickness, black patients were deemed to be at lower risk than white patients. The magnitude of the distortion was immense: Eliminating the algorithmic bias would more than double the number of black patients who would receive extra help. The problem lay in a subtle engineering choice: to measure “sickness,” they used the most readily available data, health care expenditures. But because society spends less on black patients than equally sick white ones, the algorithm understated the black patients’ true needs.