More data can make you more wrong
spectator.co.uk
spectator.co.uk
with a summary here: https://newschicagobooth.uchicago.edu/newsroom/problem-slow-...
The key point neglected in the original article is that the jurors were being asked about intent and premeditation in the slow-mo study. My knee-jerk reaction when first reading this article was to assume that the jurors might have just been able to conquer reasonable doubt more with slo-mo, but the questions seemed well designed to isolate the fact that slo-mo is creating a false narrative (even when the viewers can see the real-time clock clicking over at a very slow rate).
It would be interesting to see whether viewers who got to see both clips would still have the same perception, and whether ordering matters.
It doesn't address the ordering question.
Thanks for the links. A lot of data there. Hope I don't reach the wrong conclusion. ;)
If we're not careful, we'll take all the old problems caused by unjustified discrimination based on factors like gender or skin colour, which today we (at least most of us) would consider irrelevant to making most decisions, and multiply them up many times over to apply to each input into an automated decision-making process. How do machine learning and statistical analysis tools distinguish between a causal relationship and mere correlation? And how often will they actually demonstrate the latter, yet be treated as if they had found the former?
They don't.
>"And how often will they actually demonstrate the latter, yet be treated as if they had found the former?"
Every time the former occurs it is incorrect.
They have no reason to make such a distinction.
If you're trying to look for subjects of with a particular property X, but X is hidden, but you know property Y correlates with X, the correct action is to choose subjects with property Y. The causative relationship (or lack thereof) between X and Y is irrelevant.
As a simple example, if you're hiring people to carry 80-pound bags of grain between trucks, you want the property of physical strength. But, if you only have resumes to go on, you can't observe physical strength directly. But, gender correlates with physical strength, and names correlate with gender, so you'd be rational to choose the resumes with recognizably male names.
The reason we avoid discriminating between various protected groups isn't because groups don't have characteristics. If that was the case, such discrimination would be pointless and people would not bother doing it because it would have a cost but no benefit. Discrimination based on hair color is like this. Nobody does it and we don't need campaigns to stop it.
In reality, groups of people do have characteristics - physical, psychological, emotional, intellectual, cultural. However, we avoid discriminating between protected groups because we believe it's wrong to subject an individual to that kind of discrimination, even if such discrimination would be rational for the discriminator. Such discrimination violates our western ideals of individual rights and equal individual opportunity.
I don't think most of us would have a problem with discriminating on an inherent property of a group that is relevant to the decision being made. Indeed, often neither do anti-discrimination laws. Such discrimination can be objectively justified.
For example, consider a job where a certain level of fitness is a functional requirement, say a firefighter whose role includes being able to carry someone out of a burning building. Setting a relatively high bar for physical strength is going to discriminate against female job applicants and those with various physical disabilities as their respective groups, but it's not discriminating against someone because they're female or in a wheelchair, it's discriminating against them because they can't carry someone out of a burning building and so that person is going to die. I don't think most of us would have a problem with this kind of rule. The only thing unfair here is life not making us all equal, and there isn't much we can do about that.
The problems usually start when either you discriminate against a whole group based on some property that is somewhat correlated with membership of the group but not inherent, or you discriminate against a whole group based on a property that is inherent but isn't actually relevant to the decision being made.
For example, if a job involves sitting at a desk and using a computer all day, a woman or someone in a wheelchair can presumably do that just as well as our hypothetical strong male. In this case, discrimination on the basis of gender or physical strength simply isn't relevant to the decision being made but sadly does sometimes happen because of individual prejudice, and thus we have laws to protect those in more vulnerable situations.
Unfortunately, the kinds of tools and mathematical analyses we're talking about here don't necessarily see those situations any differently. They may learn the wrong lesson if there are coincidental, misleading correlations in whatever data is used to train them, which brings us back to where we came in.
In the cricket case, the problem seems to be that small differences in a player's reaction time can make the difference between acceptable and unacceptable actions. Why should it matter if the player intentionally blocked the ball? Justice is much more difficult when rules depend on precise subjective judgement.
The same is true of the murder case. Clearly the robbers committed a second degree murder. In the hypothetical cases, was the prosecution pushing for first degree murder instead? Why rely on precise subjective judgement?
This like trying to decide from a grainy photo whether a character is an 'A' or a '4'. It's an unavoidably difficult problem, but in the two cases above, we can change the rules to avoid it.
As to the case at hand, I have similar questions. I would think in the U.S. this would have just been a felony murder case where intentions are irrelevant. Agree that a first-degree murder charge in a robbery case would be odd (unless the robbery was a cover story for the planned murder??).
As for sports, the pendulum seems to shifted in the other direction with most rules of the 'strict' variety. And I would agree with you that any rule that can be formulated as a strict rule, should be.
To add to it, data-gathering by NSA can indeed lead to the wrong conclusions. Or statistics in scientific experiments that are wrong, due to selecting only a part of the data-set.
Would have liked to have been given a more concrete example of this, because I don't have a strong enough imagination to really understand that scenario. Finding the suspects fingerprints in more places around the crime scene? I don't know, I guess that could help convict but could see it just as easily distracting the jury and muddling the case.