If you’re struggling to accept the idea that using an algorithm is a good idea, consider whether these logical fallacies might be tripping you up.

Have you ever heard of “algorithm aversion?” Researchers describe it as being when people trust their own conclusions over the results of an algorithm, even when the algorithm is shown to perform better. You see this in action when doctors scoff at online symptom checkers or lawyers hesitate to use predictive coding. So, what drives this aversion? A few theories on why machine intelligence is off-putting to many:

Confirmation Bias

The Fallacy: This is the “tendency for people to (consciously or unconsciously) seek out information that conforms to their pre-existing view points, and subsequently ignore information that goes against them.”

In Action: Thinking of times when we’ve been right is easy; we can even reference a host of scenarios as proof! After all, it is easier to recall that which confirms our assumptions—and bolsters our egos—than that which contradicts them. Remembering when we’ve missed the mark is tougher, and recalling when machines have been correct is even more difficult. This is also true of the questions we choose to pose: we ask ones that confirm existing assumptions—and interpret new information based on this framework. So, whether it’s our memory or our perception of the world, we end up with little evidence that algorithms are right, and plenty of evidence that we are.

Read more: www.legaltechnews.com/id=1202736950956/Do-You-Have-Algorithm-Aversion#ixzz3lidESBVw

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