I followed a similar algorithm, considering all lives equal. Injury versus death: prevent death. Uncertainty versus death: prevent certain death, and assume passengers are more likely to survive an accident because they're better protected. Certain pedestrian death versus certain pedestrian death: prefer non-intervention over intervention. Certain passenger death versus certain pedestrian death: protect the passengers.

Justification for that last one: self-driving cars will be far safer than a human driver, such that it'll save many lives to get more people using self-driving cars sooner. Self-driving cars not prioritizing their passengers will rightfully be considered defective by potential passengers, and many such passengers will refuse to use such a vehicle, choosing to continue using human-driven cars. Thus, a self-driving car choosing not to prioritize its passengers will delay the adoption of self-driving cars, and result in more deaths and injuries overall.

"Certain passenger death versus certain pedestrian death: protect the passengers."

Pondering that question made me imagine some bad Sci-Fi future where self-driving cars end up being dangerous killer-robots for anybody but the passengers.

If pedestrians have to fear these things, because they are programmed in a "Protect the pilot above all else!" way, it might hamper adoption just as badly.

I took the same sort of dispassionate approach, valuing the lives of the passengers above all else and staying the course otherwise. I was disappointed to discover the parsing of the results had no room for such methodology. Based my entirely algorithmic approach it was determined that I favored youth and fitness.

And that's how simple algorithms, acting on non-homogenous datasets, get declared to be 'racist' (or otherwise discriminatory).

Your results might seem spurious because of the small sample size, but when aggregating the results of all the participants they will have enough data to be able to conclude how many people did act like you did with apparent preferences due to chance, and how many actually where "biased" in some way.

Fair point. If they look at all respondents who answered the 13 questions the same exact way I did, then they'd be able to see if the doctor/criminal, fat/thin, female/male distributions are noisy or correlated.