This has been a really thorny question in 'developed' countries, e.g. the USA, even before deep learning, robotics, and autonomous vehicles. People tend to trust automated reasoning, even if it has inscrutable, or sometimes obvious biases. Status-quo bias then prevents people from changing their policies. An example is the incarceration rates of African-Americans, where some jurisdictions use mathematical models to predict recidivism rates for first-time offenders. Seems like a good idea, until one reads that one of the key metrics of this model is a suspect's relationship with someone who's already been incarcerated. People have been given longer sentences because they were related to someone who was already in prison. Therefore, almost twice as many black people in the USA are incarcerated. A lot of these administrators think that they're ushering in a more efficient future, and have abdicated their ethical responsibilities. I think this is the part that's problematic. Humans have ethical responsibilities to one another that they somehow suspend when they start uncritically trusting models built on assumptions that they don't have an incentive to question. Mathematical models don't, and can't (yet) encode these responsibilities because they're a very long-term negotiation, fundamental to the history and fabric of our various societies. Here's a deeper discussion of this problem, from the Future of Life Institute: https://futureoflife.org/2017/07/31/towards-a-code-of-ethics-in-artificial-i... On Mon, Aug 28, 2017 at 4:23 PM anyega jefferson via kictanet < kictanet@lists.kictanet.or.ke> wrote:

https://www.theatlantic.com/technology/archive/2017/08/inside-waymos-secret-... Interesting article on efforts to build mobility as a service.

The rise of self-driving cars and other applications of machine learning such as the use of robotics in surgeries, who bears legal responsibility when a mishap happens? The software developer or the manufacturer?

-- Anyega M Jefferson

jeffersonanyega@gmail.com

0703824326

Start where you are,use what you have and do what you can.

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