Ok, you’re not particularly familiar with this 1997 video game and there’s no reason you should be because it’s disgustingly violent with most points being awarded on the quantity and artistic impression of the pedestrians you mow down.

C’mon, fun photons.

In meatspace-

A new study finds a potential risk with self-driving cars: failure to detect dark-skinned pedestrians
By Sigal Samuel, VOX
Mar 6, 2019

The list of concerns about self-driving cars just got longer.

In addition to worrying about how safe they are, how they’d handle tricky moral trade-offs on the road, and how they might make traffic worse, we also need to worry about how they could harm people of color.

If you’re a person with dark skin, you may be more likely than your white friends to get hit by a self-driving car, according to a new study out of the Georgia Institute of Technology. That’s because automated vehicles may be better at detecting pedestrians with lighter skin tones.

The authors of the study started out with a simple question: How accurately do state-of-the-art object-detection models, like those used by self-driving cars, detect people from different demographic groups? To find out, they looked at a large dataset of images that contain pedestrians. They divided up the people using the Fitzpatrick scale, a system for classifying human skin tones from light to dark.

The researchers then analyzed how often the models correctly detected the presence of people in the light-skinned group versus how often they got it right with people in the dark-skinned group.

The result? Detection was five percentage points less accurate, on average, for the dark-skinned group. That disparity persisted even when researchers controlled for variables like the time of day in images or the occasionally obstructed view of pedestrians.

“The main takeaway from our work is that vision systems that share common structures to the ones we tested should be looked at more closely,” Jamie Morgenstern, one of the authors of the study, told me.

The report, “Predictive Inequity in Object Detection,” should be taken with a grain of salt. It hasn’t yet been peer-reviewed. It didn’t test any object-detection models actually being used by self-driving cars, nor did it leverage any training datasets actually being used by autonomous vehicle manufacturers. Instead, it tested several models used by academic researchers, trained on publicly available datasets. The researchers had to do it this way because companies don’t make their data available for scrutiny — a serious issue given that this a matter of public interest.

Kartik Hosanagar, the author of A Human’s Guide to Machine Intelligence, was not surprised when I told him the results of the self-driving car study, noting that “there have been so many stories” like this. Looking toward future solutions, he said, “I think an explicit test for bias is a more useful thing to do. To mandate that every team needs to have enough diversity is going to be hard because diversity can be many things: race, gender, nationality. But to say there are certain key things a company has to do — you have to test for race bias — I think that’s going to be more effective.”

These fixes aren’t mutually exclusive. And arguably, it’s in companies’ best interest to do everything they can to root out racial bias, before people of color are forced to take the brunt of it, literally.

A Brave New World.