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monkey news from Africa (via India)

August 28, 2007

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Three very different sources of bias in AI, and how to fix them

Since our Science paper came out it's been evident that people are surprised that machines can be biased. They assume machines are necessarily neutral and objective, which is in some sense true -- in the sense that there is no machine perspective or ethics. But to the extent an artefact is an element of our culture, it will always reflect bias.

I think the problem is that people mistake computation for math. Math really is pure, has certain truth, it's eternal, it would be the same without any particular sentient species looking at it. That's because math is an abstraction that doesn't exist in the real world. Computation is a physical process. It takes time, energy, and space. Therefore it is resource constrained. This is true whether you are talking about natural or artificial intelligence. From a computational perspective there's little difference between these.

People are smart because we are able to exploit the selected "best of" other people&#…

Why (or rather, when) suffering in AI is incoherent.

I've been arguing for some months now in public talks that AI cannot be a legal person because suffering in well-designed AI is incoherent. This is not actually my own argument, but rather is due to S. M. Solaiman from their brilliant recent article Legal personality of robots, corporations, idols and chimpanzees: a quest for legitimacy.

The great thing about Solaiman's article is that they make it clear why corporations are legal persons but AI and chimpanzees aren't. Basically, the notion of legal person has been developed to synchronise with our system of justice. Justice among other things requires means of redress and coercion. A legal person most know and be able to claim their rights–they must be able to assert themselves as members of a society. This is why non-human animals (and some incapacitated humans) are not legal persons. I'm happy with definitions of "know" and "assert" that would mean that intelligent artefacts could do this, …

We Didn't Prove Prejudice Is True (A Role for Consciousness)

The good news: We know where word meanings come from We have a paper in Science,Semantics derived automatically from language corpora contain human biases (a green open access version is hosted at Bath). What this paper shows is that you can find the implicit biases humans have just by learning semantics from our language. We showed this by using machine learning of semantics from the language on the Web, and comparing that to implicit biases psychologists have documented using the Implicit Association Test (IAT). The IAT uses reaction times to show that people find it easier to associate some things than others. For example, it's easier to associate flowers with pleasant terms and bugs with unpleasant terms than the other way around. Notice that the actual statistics underlying the IAT is always about these slightly complicated, dual relative measures. It's easier to: group {flowers and pleasant terms} together, AND {unpleasant terms and insect names} (both those groupin…

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