A new means to detect AI Ethics violations consists of simulating an AI system to try and detect or predict that AI Ethics violations could arise. This could be used for example in the case of AI-based self-driving cars.
As you might directly guess, trying to pin down the specifics underlying these principles can be extremely hard to do. Even more so, the effort to turn those broad principles into something entirely tangible and detailed enough to be used when crafting AI systems is also a tough nut to crack.
I think you can guess where this is heading. If humans that have been making the patterned upon decisions have been incorporating untoward biases, the odds are that the data reflects this in subtle but significant ways. Machine Learning or Deep Learning computational pattern matching will simply try to mathematically mimic the data accordingly. There is no semblance of common sense or other sentient aspects of AI-crafted modeling per se.
When taking either of the two avenues, the first thing you would need to do is try to ascertain whatever you can reasonably figure out about the targeted AI system. In terms of the outputs of the targeted AI, you are likely going to only have a sparse amount of outputs. Essentially, the full range of potential output possibilities is far larger than whatever output sets you are going to be able to readily collect. As such, you might need to try and extrapolate from the outputs you do have. As usual, this can be dicey as your extrapolation might be off-target of the targeted AI.
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