Who Should Stop Unethical A.I.?

In US society/laws, it seems that the vast majority of blame is ascribed to the entity who last causes an event to occur. So the blame in the following sequence:

1. Miner mines iron

2. Manufacturer makes knife

3. Retailer sells knife to person A

4. Person A kills person B with the knife

I think most people living in the US would be ok with the general idea that person A should be held responsible for killing person B, and not miner/manufacturer/retailer. Perhaps I'm wrong.

Would you consider the knife retailer equivalent to the torture rack manufacture in your example?

Any model created using biased data will inherently mirror that bias unless there are active steps made to counteract this effect.

For example, basically any financial evaluations of US citizens will likely result in an inherent bias against Black people due to institutional biases such as redlining that have long lasting socioeconomic and demographic repercussions. This might mean that something as simple as incorporating the zip code of a home into a mortgage pricing AI can end up with a racial bias.

> Some of the conversation touched on the reviewing and publishing process in computer science. “Curious if there have been discussions around having ethics review boards at either conferences or with funding agencies (like IRB) to guide AI research,” one person wrote. (An organization’s institutional review board, or I.R.B., performs an ethics review of proposed scientific research.)

I wonder if a good solution would be simply to have a rating system for accepted papers, with ratings applied by the reviewers. Basically, you'd have two numbers:

Useful rating: On a scale of 1 to 10, is this a useful system that solves a real problem to make life generally better?

Problematic rating: on a scale of 1 to 10, can this system be used in ways that create new problems or exacerbate old ones?

So, let's use the example from the article of a system that predicts what a person looks like from their voice. Maybe the author submits it to the conference, and the reviewers accept the paper, and give it a useful rating of 3/10 (it could be used to identify criminal suspects with low probability, maybe?), and give it a problematic rating of 8/10 (could be used to discriminate against minorities or automatically apply cultural stereotypes; could be used for surveillance, could falsely implicate innocent people for crimes, etc...).

The author could then decide whether they want to continue publishing the research, or drop it. For all published papers, the ratings are listed in the table of contents of the proceedings, and the papers in each section are sorted with the most useful first and the most problematic last.

1. How does one distinguish between software that can be used for surveillance and software that cannot?

What is the killer app for the technology under discussion? If the killer app is improving camera filters to make more vibrant pictures but it can also be used for surveillance then you do not worry about it being used for surveillance. If however the killer app is surveillance then you worry about it.

As a general rule surveillance however is not the killer app of these technologies, surveillance is just an instance of the killer app, and the killer app is the more general one of privacy violation of which surveillance is an instance.