why teaching ethics and “ethics training” is problematic
After three years speaking about, writing about and training in AI Ethics, organizations I speak with report that many of the students come back with a good understanding of the elements and remedies for ethical issues. But they continue to work as before. My observation is that this is the result of four factors:
Part of the problem is the use of the term “ethics.” It’s too ineffable for most people to grasp. It harkens to Socrates or the church. Some groups have tried “trustworthy.” In an article, Artificial Intelligence and Legal Liability, the author, John Kingston, asks the question, “When an AI Finally Kills Someone, Who Will Be Responsible?” This is an issue of civil or criminal liability, which will surely be addressed by government judicial systems.
When an engineer is tasked with building AI models for radioactive waste storage, how broad should the ethical review be? I try to explain this to corporate AI engineers, that it is insufficient to address any ethical concerns of their model, such as bias or privacy. They need to look over the horizon to how it will propagate. It happens that the engineers truly understand their ethical responsibility in principle, but fail to see the bigger picture.
Not all work in AI is done in commercial companies. First, you have to consider who is developing AI apps. If you ask the technology vendors, they’ll tell you it’s the enterprise, and that’s where most of our analysis is. But there are also applications in Defense, Intelligence, NGO’s, all sorts of other big AI you don’t see, which has a different set of ethical issues. It’s distributed and diffuse.
For example, AI work at Google can be highly theoretical and distanced from the effects on people. For instance, TensorFlow is a machine learning platform, not an AI application to sell more shoes. Microsoft struggles to find a way to use its facial recognition technology without abuse of people’s privacy. One ethical requirement I have is not to let anyone hack into my systems and cause mayhem in a defense operation. On the other hand, perhaps I’m using AI technology to make better Hellfire missiles to kill people. If I’m a lender, an ethical requirement is to not discriminate against protected classes, but in 2008-2009, an emergent protected class was millions of people being foreclosed.
It’s a no-brainer that the exploding use of AI in policing is frightening. But is there some relative degree of ethics at play? It is widely claimed that predictive policing algorithms are racist. It is also widely claimed that it reduces crime. The current thrust of predictive policing initiatives is based on convincing arguments and anecdotal evidence rather than systematic empirical research. Racist algorithms can be fixed, but how many innocent people are affected by it until it is? Compare this to the working poor are unfairly rated for car insurance (sometimes double or triple what you will pay) because of a clear correlation between low FICO score and experience, even though there is no causal relationship. (poor people have low FICO scores because they’re poor, not because they represent an increased hazard).
Predictive policing is dramatic, but mundane things may have a much more positive effect on many more people if you can’t afford your car insurance (which is mandatory in every state, a regressive tax on the working poor primarily funds wealthy personal injury lawyers).
