Bias Is a Big Problem. But So Is ‘Noise.’

Daniel Kahneman, O. Sibony & C.R. Sunstein

The New York Times

Originally posted 15 May 21

Here is an excerpt:

There is much evidence that irrelevant circumstances can affect judgments. In the case of criminal sentencing, for instance, a judge’s mood, fatigue and even the weather can all have modest but detectable effects on judicial decisions.

Another source of noise is that people can have different general tendencies. Judges often vary in the severity of the sentences they mete out: There are “hanging” judges and lenient ones.

A third source of noise is less intuitive, although it is usually the largest: People can have not only different general tendencies (say, whether they are harsh or lenient) but also different patterns of assessment (say, which types of cases they believe merit being harsh or lenient about). 

Underwriters differ in their views of what is risky, and doctors in their views of which ailments require treatment. 

We celebrate the uniqueness of individuals, but we tend to forget that, when we expect consistency, uniqueness becomes a liability.

Once you become aware of noise, you can look for ways to reduce it. For instance, independent judgments from a number of people can be averaged (a frequent practice in forecasting). 

Guidelines, such as those often used in medicine, can help professionals reach better and more uniform decisions. 

As studies of hiring practices have consistently shown, imposing structure and discipline in interviews and other forms of assessment tends to improve judgments of job candidates.

No noise-reduction techniques will be deployed, however, if we do not first recognize the existence of noise. 

Noise is too often neglected. But it is a serious issue that results in frequent error and rampant injustice. 

Organizations and institutions, public and private, will make better decisions if they take noise seriously.

The info is here.

Danny Kahneman on AI versus Humans

NBER Economics of AI Workshop 2017

Here is a rough translation of an excerpt:

One point made yesterday was the uniqueness of humans when it comes to evaluations. It was called “judgment”. Here in my noggin it’s “evaluation of outcomes”: the utility side of the decision function. I really don’t see why that should be reserved to humans.

I’d like to make the following argument:

1. The main characteristic of people is that they’re very “noisy”.
2. You show them the same stimulus twice, they don’t give you the same response twice.
3. You show the same choice twice I mean—that’s why we had stochastic choice theory because thereis so much variability in people’s choices given the same stimuli.
4. Now what can be done even without AI is a program that observes an individual that will be better than the individual and will make better choices for the individual by because it will be noise-free.
5. We know from the literature that Colin cited on predictions an interesting tidbit:
6. If you take clinicians and you have them predict some criterion a large number of time and then you develop a simple equation that predicts not the outcome but the clinicians judgment, that model does better in predicting the outcome then the clinician.
7. That is fundamental.

This is telling you that one of the major limitations on human performance is not bias it is just noise.
I’m maybe partly responsible for this, but people now when they talk about error tend to think of bias as an explanation: the first thing that comes to mind. Well, there is bias. And it is an error. But in fact most of the errors that people make are better viewed as this random noise. And there’s an awful lot of it.

The entire transcript and target article is here.