Originally posted July 6, 2017
Here is an excerpt:
However, let’s look at the problem from a different angle. I was educated as an economist, so allow me to start my argument with this statement: let’s assume we have the perfect dataset. It is not only omni-comprehensive but also clean, consistent and deep both longitudinally and temporally speaking.
Even in this case, we have no guarantee AI won’t learn the same bias autonomously as we did. In other words, removing biases by hand or by construction is not a guarantee of those biases to not come out again spontaneously.
This possibility also raises another (philosophical) question: we are building this argument from the assumption that biases are bad (mostly). So let’s say the machines come up with a result we see as biased, and therefore we reset them and start again the analysis with new data. But the machines come up with a similarly ‘biased result’. Would we then be open to accepting that as true and revision what we consider to be biased?
This is basically a cultural and philosophical clash between two different species.
In other words, I believe that two of the reasons why embedding ethics into machine designing is extremely hard is that i) we don’t really know unanimously what ethics is, and ii) we should be open to admit that our values or ethics might not be completely right and that what we consider to be biased is not the exception but rather the norm.
Developing a (general) AI is making us think about those problems and it will change (if it hasn’t already started) our values system. And perhaps, who knows, we will end up learning something from machines’ ethics as well.
The info is here.