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The machine learning models employing reinforcement learning often generate brutally efficient, out of the box solutions to problems that humans would not have considered.

That's great, if that's what you want.

And if those solutions are accurate as well as efficient.

Most of what people call AI is really machine learning, as you suggest. In this case, it is likely that data scientists poured petabytes of images that are tagged (by some overlord) as X or Y or Z etc into some canned program that spits out an algorithm that, after cascading data and gaps between success and failure back and forth until "convergence", separates input images X, Y, and Z etc buckets.

If these models are supported by decent humans, there will be an attendant suite of monitoring and back testing routines to ensure the out-of-sample performance is strong. This should be doubley so if there is unsupervised learning involved.

Unlike statistical or econometric or biostatistics modeling, ML is not transparent and is fraught with "black box" unintended consequences. Even the most noble of data scientists can build biased models if their data are biased, and the nature of ML optimization and its lack of transparency will codify that bias. A better check on bias, is the character of the model development team. Nobody is perfect, but if you have love for your fellow man regardless if they lean left or right or whatever, or if they listen to Springsteen, more often than not you should be ok.

But nobility is in the eye of the beholder. If it is an accident, then this demonstrates that FB aren't the Masters of the Tech Multiverse they make themselves out to be...any model built by any good development team has overrides and guardrails to prevent blatantly stupid outcomes (like this). I mean...a simple line of code saying "If YHAT is ("primate", "monkey", etc) then end" will work. The alternative explanation, is this FB incident shows the TRUE bias of allegedly woke tech overlords: they really DO see blacks as less-than-human.

I was on a technical demo team around Christmas last year. We had 1 week to demonstrate capability. The input data was TLE (Two Line Element) of every satellite for the period 2000 to 2020 (June) and AIS data of every vessel on the seas over the same time frame. The task was to identify if ANY vessel had evaded the observation foot print of the satellites. Cite the vessel(s) with date/time of the start/end of the voyage evaded. Python, Dask and Spark running in ECM with S3 data stores achieved the objected.

The scope of that work didn't draw in ML, but the team members had the capability had it been part of the challenge. It's fun to work with competent co-workers. It did sort of spoil the Christmas holiday, but government proposals tend to do that.