Mathwashing
mathwashing.com
mathwashing.com
Math _is_ neutral.
Algorithms _are_ neutral, and are simply instruction sets for operating on data.
Data is certainly be biased, but any researcher, designer, engineer, etc. worth their salt will try to minimise the inherent bias.
Machine learning is basically brute-force statistics on data. Or alternatively, iterated application of an algorithm on data. In and of itself it is neutral. Any apparent bias in the results will almost completely be a result of bias in the data, _not_ the design of the algorithm.
All of these (except data) are tools. What we should be worrying about is the specific applications of these tools. Which, I agree, should be subject to some sort of ethical review before widespread use.
Stop trying to drag math and algorithms into this political quagmire, and instead focus on the actual problem. We don't outlaw knives because they are still incredibly useful tools. We outlaw stabbing people.
Is there?
I mean, yea, I do expect it to do some optimization, in that I expect it to use some path finding algorithm with some collection of weights on the different paths, or something like that, and for the instance of the path finding problem that it produces I expect the route to be optimal, but that the problem it produces may differ in some ways from the true most correct problem to solve,
but, in the end, the solution it gives me is better than the one I would produce, so, why do I care if it isn't quite optimal under the literally perfect criteria? (I mean, if another alternative gave better results that would be better, of course. I just mean that what it is isn't bad.)
I don't think navigation is the best example for this topic though. Navigation seems like a fairly solid/well-defined problem, not like more of the more fuzzy questions, like "does this picture include a face?" .
1. You believe algorithms are biased and must be analyzed for neutrality.
2. You believe algorithms are neutral and there is no need to question them.
3. You don't have the time or the aptitude to pay attention to the algorithms that run our lives. [in effect, you behave like algorithms are neutral]
I think OP's claim is closer to 3 than 2.
[0]: my previous comment pointing that out: https://news.ycombinator.com/item?id=24629474
Not to mention they provide 3 proposals of improving things.
> It shouldn't be a mission impossible to find out how and why a decision about you was made.
That does sound attractive. But I don't see how it squares with technology. For certain technologies, the answer will be "the weights in the neural network, when applied to your attributes, yielded a score that fell above a cutoff" or "Your data attributes mapped to a certain location on an eigenvector and it fell above a cutoff defined on that eigenvector".
Is the answer to stop using such algorithms? If not, doesn't this webpage need to give us more of a hint as to how we are to overcome the apparent impasse?
Explainability is far from a solved problem but there are certainly tools available to provide at least some transparency. I guess the website could provide references but that doesn't really seem to be the resolution being aimed for.
Edit: realised some may not be familiar with the word, so https://en.wikipedia.org/wiki/Puffery