10 Machine Learning Ethics Mini-Essays

1. Unemployment.

What happens after the end of jobs?

I have mixed feelings about making jobs obsolete. When talk turns to “Look at trucking: it currently employs millions of individuals in the United States alone,” (quoted from original assignment text) I like to invoke the image of millions of individuals who lost their jobs tilling fields with oxen. Seriously, every single person whose survival is not tied to tilling fields with oxen is better off, whether they’re making the most of it or not. We have been automating jobs for a very, very long time. I was on a software development team in the 1990s that probably put many chemical, paper, and printing workers out of their jobs, by creating the first web-based system that allowed remote digital soft proofing of print jobs. A great advancement in efficiency! The advent of consumer digital photography arrived soon after, wiping out even more of those jobs related to the processing of film. I certainly don’t feel bad about all the chemicals that are no longer manufactured and set loose on the World. Or all that paper. But do I feel bad for those workers? Maybe I do. Did they live in a place where their financial security and modern skills re-training were assured by the country to which they had been paying taxes? HA! They probably had to get jobs driving trucks.

The hope is, as we automate jobs, we can move on to bigger and better things. We don’t till fields with oxen, we drive trucks. We don’t drive trucks, we’re YouTubers. The YouTubers of today have no idea what will be a hot job when AIs become the most efficient purveyors of video content. We certainly know that Machine Learning Specialist is going to be pretty hot in the coming years. But how good at preparing people for the future is our society now? In What Happens if Robots Take The Jobs? The Impact of emerging technologies on employment and public policy[1], Darrell West advances a number of ideas that address the need for society to adapt to disruptive technological change. “There needs to be ways for people to live fulfilling lives even if society needs relatively few workers,” West writes.One recommendation is “retraining accounts”, which are publicly funded accounts for fundingof re-training. This approach purports to offer the availability of free education, without as much potential for people becoming full-time students and not returning to the workforce.

Also important is Curricular Reform. In an age of constant change, it is important for school boards to rapidly adapt to the changing demands of the job market. I have witnessed this myself – my 12-year-old daughter is making websites in her Grade 7 class. Once the domain of specialists, technology now allows almost anyone with creative spirit to perform this task. This is part of the process by which technology turns into progress. As once we learned to use machines to till our fields and truck our vegetables, we now learn to use machines to publish and distribute our ideas to the world. Finally, West speaks of an “artisanal economy”, in which mundane tasks such as driving and plowing with oxen are performed by machines, while humans participate in the supply and demand of art, culinary delights, music, research, websites, YouTube videos, exploration, and the like. Sounds very Utopian. But I fear we will need more than re-training accounts and modernized curricula to get there.


10 Machine Learning Ethics Mini-Essays

2. Inequality.

How do we distribute the wealth created by machines?

One simple, effective, but unpalatable solution to this would be to tax the owners of the machines that do the jobs formerly done by humans, and then provide a basic minimum income to all the humans. AI or no AI, wealth inequality and a lack of access to quality education are serious issues already. As our ability to automate human activity accelerates, so too does the impact of these problems. Education is very difficult for people struggling to pay the bills. In an ideal world, or a well-run country, there would be thousands of people in programs just like York’s Machine Learning Certificate, and they would be able to focus on theprograms, rather than scrambling full-time to keep their lives together and doing what they can at school in the meantime. And so I believe, in addition to the educational improvements outlined in Question #1, Unemployment, it’s really time for society to disrupt the disrupting power of disruptive technologies by implementing a Basic Minimum Income.

Unfortunately, the political resistance to this idea is very strong. Ontario was going to do experiments with Basic Minimum Income — but our habitual lurching from governing party to governing party have left that experiment on the cutting room floor. People, especially in North America, don’t like the idea of giving people money for free. Or, the idea of “stealing” tax money from the job creators. But at some point, we must recognize that their primary role,is not that of job creators. They create jobs if they absolutely have to. If they can automate instead, rest assured, for the benefit of the shareholders, they will. As robots and AIs do more and more of the work, and less and less people are needed to do these tasks, it seems reasonable that the robots, or their owners, are rewarded a little less for the service they no longer provide to society. Charles Kenny looks at ethical and practical issues surrounding Basic Minimum Income in Give Poor People Cash[2]. Central to his thesis, and that of others who have studied this and other social programs, is that it is the most efficient, flexible,and productive way to deliver a social safety net. Basic Minimum Income completely avoids the inefficiencies and frauds associated with benefits programs that are targeted, controlled, or conditional. It also catalyzes economic growth through the flexibility it brings to how the money is spent, or invested, into the local economy. Including, of course, allowing people to focus more on education and become more productive members of society.