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This morning on NPR, I listened to a short report on how people tend to lose trust in computer algorithms much more quickly than we lose trust in humans. We compare a world of computer mistakes to a false world of zero human mistakes. For example, if driverless cars get popular and one causes an accident, trust in driverless cars will plummet, even if driver-ful cars cause more accidents. The report was merely interesting until Steve Inskeep and Shankar Vedantam used a hypothetical example far closer to home: law school admissions.

INSKEEP: I wonder if there's another factor that comes into play here because you said one other reason that we trust humans is we presume that humans can learn. Aren't we entering a world in which the algorithms themselves will be learning more and more?

VEDANTAM: In fact, I think we've already entered that world, Steve. Algorithms not only learn, but they learn very well. So one of the things that computers are very good at doing is quickly learning how much weight to attach to the different components of a decision. So if you have students applying to law school, the computer might be able to say, here's how much attention you pay to their grades in college, to their LSAT scores, to their recommendation letters. Now, of course, many of us are comfortable with algorithms making decisions for other people. We just don't like it when algorithms make decisions about us.

INSKEEP: (Laughter) Don't make a decision about my law school application.

VEDANTAM: Precisely. Because we think we're unique and special, how can it possibly be that an algorithm can judge us?

First, I'm very interested in why Vedantam used this example -- does he know of a law school that uses an admissions algorithm?

Second, if Vedantam does know of a law school that uses an admissions algorithm, I'll bet a lot of money that the algorithm isn't weighting and using factors that we believe will predict a student's future success, whether in law school or in the practice of law. I'm willing to bet that the algorithm judges whether the law students will improve/weaken their class profile of USNEWS purposes.

Finally, I wish we did live in the false world that Inskeep and Vedantam believe we live in -- where admissions committees not only are interested in the "whole file" of a candidate and treat them as unique and special and not as numbers, but also the second-best world that Inskeep and Vedantam are predicting -- where committees use algorithms to predict candidate success in the classroom and beyond based on markers and proxies. I was talking to a physician friend who serves on the admissions committee of his medical school alma mater. (Can you imagine a law school having practicing attorneys on admissions?) He said they were not that interested in grades. He said they looked for "hungry" and proxies for that. He said his ideal med school candidate was someone who worked 40 hours a week during undergrad. That sounds like a good world to me.

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This morning on NPR, I listened to a short report on how people tend to lose trust in computer algorithms much more quickly than we lose trust in humans. We compare a world of computer mistakes to a false world of zero human mistakes. For example, if driverless cars get popular and one causes an accident, trust in driverless cars will plummet, even if driver-ful cars cause more accidents. The report was merely interesting until Steve Inskeep and Shankar Vedantam used a hypothetical example far closer to home: law school admissions.

INSKEEP: I wonder if there's another factor that comes into play here because you said one other reason that we trust humans is we presume that humans can learn. Aren't we entering a world in which the algorithms themselves will be learning more and more?

VEDANTAM: In fact, I think we've already entered that world, Steve. Algorithms not only learn, but they learn very well. So one of the things that computers are very good at doing is quickly learning how much weight to attach to the different components of a decision. So if you have students applying to law school, the computer might be able to say, here's how much attention you pay to their grades in college, to their LSAT scores, to their recommendation letters. Now, of course, many of us are comfortable with algorithms making decisions for other people. We just don't like it when algorithms make decisions about us.

INSKEEP: (Laughter) Don't make a decision about my law school application.

VEDANTAM: Precisely. Because we think we're unique and special, how can it possibly be that an algorithm can judge us?

First, I'm very interested in why Vedantam used this example -- does he know of a law school that uses an admissions algorithm?

Second, if Vedantam does know of a law school that uses an admissions algorithm, I'll bet a lot of money that the algorithm isn't weighting and using factors that we believe will predict a student's future success, whether in law school or in the practice of law. I'm willing to bet that the algorithm judges whether the law students will improve/weaken their class profile of USNEWS purposes.

Finally, I wish we did live in the false world that Inskeep and Vedantam believe we live in -- where admissions committees not only are interested in the "whole file" of a candidate and treat them as unique and special and not as numbers, but also the second-best world that Inskeep and Vedantam are predicting -- where committees use algorithms to predict candidate success in the classroom and beyond based on markers and proxies. I was talking to a physician friend who serves on the admissions committee of his medical school alma mater. (Can you imagine a law school having practicing attorneys on admissions?) He said they were not that interested in grades. He said they looked for "hungry" and proxies for that. He said his ideal med school candidate was someone who worked 40 hours a week during undergrad. That sounds like a good world to me.

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