Traffic Accidents and Structural Discrimination

How are traffic accidents like structural discrimination?

Here’s a pattern I’ve been thinking about lately. It has to do with a flaw in the way we think about cause and effect.

Each year in the US, we see around 40,000 people killed in traffic accidents. One way of measuring the frequency of accidents is to talk about “accidents per miles driven.” The idea is that, with more people driving, we should expect more accidents.

If there are 40,000 accidents per year, and then traffic doubles, we should see roughly 80,000 accidents per year.

That’s pretty straightforward, right?

Things get messy when we start asking about causes. For example, since we doubled traffic, and accidents doubled, we could argue that roughly half of those accidents were caused by the increased amount of traffic.

This all makes sense, but what happens when we try to apply it to an actual, specific accident?

Can you point to a single accident and say “this one was caused by increased traffic?”

No, you can’t.

And here we come to the root of the problem. It doesn’t matter what the accident looks like, you’ll always be able to find some cause which has nothing to do with the total amount of traffic on the road.

“This accident was caused because the driver turning left entered the intersection shortly after the light turned green.”

“This accident was caused because the driver entering the freeway did not match the flow of traffic.”

“This accident was caused because the driver failed to keep a safe distance from the car ahead of him.”

Someone who’s seen a bunch of accidents can swear up and down “increased traffic has not been the cause of a single accident!” — and they might be right that no single accident is entirely the fault of increased traffic.

Yet they’re wrong — because at least half of the accidents were caused by increased traffic.

What’s going on here?

Whenever a person drives, there’s some probability of them making an error. That error probability is nonzero. Not because they are a ‘bad’ person, or because they are ‘immoral’ or have done something wrong, but because we are human and human beings make mistakes.

When you add lots and lots and lots of potential for mistakes over time, you’re going to see actual, concrete events play out.

At one level, those concrete events have specific causes — a missed light, braking too late, moving too fast. “Too much traffic” never factors in there. Yet, even if each individual event has local causes, the large-scale pattern in the set of global events has a structural cause: the increase in traffic and the tendency to make mistakes.

Try looking at racism through this same lens.

Eric Garner. Strangled by police who thought he was selling loose cigarettes. Why did this man die? Because the police suspected he sold loose cigarettes? Or because he’s a black man who encountered the police in New York City?

Remember, each time an accident happens, we can point to someone “making a bad choice” and then say the accident was caused by that bad choice. The large-scale pattern of choice-making reveals another causal factor, which can be invisible if you aren’t looking for it.

If people routinely make mistakes, and there’s a direction to the mistakes — they tend to skew one way, and not the other — then we should consider that direction as a separate cause.

Each time an unarmed black man is killed by police, we can come up with all kinds of “non-racism” reasons which may honestly and accurately explain the causes leading up to the local event. And yet we’re still ignoring and blind to the large scale picture if we don’t zoom out and look at the aggregated data.

So where’s the aggregated data on police shootings? The police keep it hidden from us.

If a certain model of car continually brakes too late much more frequently than similar models, maybe that is a problem with the make of the car, not the responsibility of the individual drivers. If a certain model of car is continually hit while making left turns, maybe there is a blind spot in that car’s field of vision.

When persons of color report repeated experiences of being diminished, insulted, or aggrieved — we can look at the individual circumstances and say “no, this was caused by X, not racism” — but unless we’re looking at the overall large scale data set, we’re wrong to not consider racism.

We don’t absolve a driver of fault because “the accident was caused by too much traffic” — and we should hold people accountable when they transgress, even if there isn’t racism involved.

Still, we are willing to acknowledge that the net result of increasing traffic will be an increase in accidents. We should be willing to acknowledge the effects racism has in the large scale even if there is always a “perfectly good explanation” that doesn’t include racism. (Whether there actually is, I’m not saying. My point is that ‘explaining each incident without racism’ is not enough.)

When women report being interrupted, having their contributions downplayed, and being criticized for doing the same thing men are doing — we can look at each individual circumstance and say , “no, this discussion was just heated”, or “that corporate culture values people shouting” – but this is like looking at a series of accidents where a large semi truck clips a station wagon, and explaining how, in each circumstance, the station wagon needs to look out for the semi trucks, rather than holding the trucks accountable for changing lanes without looking and acknowledging that having the same rules for different cars will lead to more accidents of one type and not another.

Teasing out cause and effect can be a complicated ordeal, even when we are thinking clearly. If we want to be treated with respect — and I think this is something everyone deserves — lets work to treat each other with respect even when we don’t think it’s merited, and not downplay someone else’s descriptions of when they’ve faced discrimination.

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