Tag Archives: Groupthink

Updates from Chicago Booth

Can regulation rein in algorithmic bias?
By Sendhil Mullainathan – Last year, you published a paper documenting how an algorithm used by health-care organizations generated racially biased results. What takeaways did that offer in terms of how algorithmic bias differs from human bias?

That paper might be, by some measures, among the strangest papers I’ve ever worked on. It’s a reminder of the sheer scale that algorithms can reach.

Exact numbers are hard to get, but about 80 million Americans are evaluated through this algorithm. And it’s not for some inconsequential thing: it is an algorithm used by many health-care systems to decide which patients should get put into what are called care-management programs. Care-management programs are for people who are going to be at the hospital a lot. If you have many conditions, you’re going to be in the system frequently, so you shouldn’t have to go through the normal front door, and maybe you should have a concierge who works just with you. You get additional resources to manage this complex care.

It costs a lot of money to put somebody in a care-management program. You really want to target these programs. So the question is, who should be in them?

Over the past five years, there have been algorithms developed using health records of people to figure out who is at highest risk of using health care a lot. These algorithms produce a risk score, and my coresearchers and I wanted to know if there was any racial bias in these scores.

The way we looked for it was to take two people given the same score by the algorithm—one white and one Black. Then we looked at those two people and asked whether, on average, the white person had the same level of sickness as the Black person. What we found is that he or she didn’t, that when the algorithm gives two people the same score, the white person tends to be much healthier than the Black person. And I mean much healthier, extremely so. If you said, “How many white people would I have to remove from the program, and how many Black people would I have to put in, until their sickness levels were roughly equalized?” you would have to double the number of Black patients. It is an enormous gap.

I say it’s one of the craziest projects I’ve worked on in part because of the sheer scale of this thing. But there are a lot of social injustices that happen at a large scale. What made it really weird was when we said, “Let’s figure out what’s causing it.” In the literature on algorithmic bias, everyone acts like algorithms are people, like they’re biased [in the sense that people are]. It’s just a little piece of code. What went wrong in the code?

What we found is something that we’re finding again and again in all of our A.I. work, that every time you see that an algorithm has done something really bad, there’s no engineering error. That’s very, very different than the traditional bugs in code that you’re used to: when your computer crashes, some engineering bug has shown up. I’ve never seen an engineering bug in A.I. The bug is in what people asked the algorithm to do. They just made a mistake in how they asked the question. more>

Related>

Beware The Perils Of Groupthink, Yet Meetings Can Still Be Useful

BOOK REVIEW

Wiser: Getting Beyond Groupthink to Make Groups Smarter, Authors: Reid Hastie and Cass Sunstein.
The Conversation – Traditional groups exacerbate some individual judgment and decision biases. Examples include the planning fallacy, in which people underestimate how much time will be needed to complete a task, and the sunk costs fallacy, irrationally investing more in a project because so much has been put into it already, when it would be better to just let it go.

But groups also cure some individual bad habits. Among these are anchoring (a tendency to rely on the first piece of evidence offered), availability (overestimating unlikely events) and some forms of narrow framing.

This is where the important role of leaders come into play, to prevent groupthink and bring out the best in their employees. more> https://goo.gl/tX215a

Group smarts

By Jane C Hu – In non-human creatures such as fish, bees, ants and even bacteria, individuals form ‘swarms’ to coordinate complicated behaviors such as group size and where to forage and build homes. Through swarming, humans have created things such as Wikipedia, which has no central leadership but produces reasonably accurate encyclopedia entries. Human language might be the result of swarming; robotic simulations of proto-language suggest that we happened upon language through an iterative process that resembles other species’ swarm intelligence.

Collaboration is the hard part, and this is where teams can fall apart. Individuals come to a team with a whole host of cognitive biases, and while one’s intuition might be that a diversity of perspectives could mitigate those biases, collaboration can actually amplify biases such as our tendencies to overestimate how much control we have over events and how much we can generalize from a small sample of data.

A key factor in these types of mistake is complacency, a hallmark of group behavior. To preserve unity, each individual member avoids being ‘the difficult one’ who rocks the boat; doubts go unvoiced. Insularity can exacerbate the problem; a team might descend further into its own cocoon, writing off any indications that its decisions or plans won’t work, and distancing itself from potential naysayers by viewing outsiders as dumb or even malicious. Complacency in a team’s expertise becomes so entrenched that it turns into overconfidence. more> https://goo.gl/oqd2up

Updates from CHICAGO BOOTH

BOOK REVIEW

Wiser: Getting Beyond Groupthink to Make Groups Smarter, Authors: Cass R. Sunstein and Reid Hastie.


Polarization: One reason groups fail
By Cass R. Sunstein, Reid Hastie – As the saying goes, two heads are better than one, and if this is so, then three heads should be better than two, and and with a hundred or a thousand, well, things are bound to go well.

But the history of the human species also suggests that often, groups fail to live up to their potential. Many groups turn out to be foolish.

One reason: groups tend to get more extreme—as, for example, when a group of people inclined to suffer from excessive optimism becomes still more optimistic as a result of internal discussions.

Group polarization has been found in hundreds of studies involving more than a dozen countries, including the United States, France, Afghanistan, and Germany. more> http://tinyurl.com/p5lux9s

Related>