Will AI reduce poverty and financial inequality or exacerbate it?

This was one part of a paper I did for my class on AI, Automation, and the Platform Revolution. The prompt was on understanding and planning for the Digital Future of Work.

I think about financial inequality a lot, especially on the microeconomic level. My staff at Saks Fifth Avenue were often in precarious financial situations and also happen to be from minority communities. Being on time or even making it to their shift could be an issue because most of them lived further from the store, had older cars, and had less reliable transportation modes. They were one of the 40% of Americans that don't have $400 in the bank for an emergency expense.

One example that sticks out is a young black woman who would often miss work to care for her sick mom. Even though she was exceptionally bright, hardworking, and customers loved her, the nature of store-based retail means that she had 0% productivity while caring for mom. This behavior not only put me in a bind when having to enforce time and attendance policy, but it also meant that she wasn't in a position to advance her career, even though she was very talented.

AI for Tax Policy?

My research brought me to an article by Cate Lawrence for Codemotion entitled "How AI Can Help Solve the Challenges of Economic and Financial Inequality."

A team at Salesforce published research using a machine learning framework to test different types of economic policy. The AI Economist ran different scenarios to find an optimal tax policy to reduce inequality. Their research showed their model was 16% more effective than a framework proposed by MacArthur "genius" Emmanuel Saez and the National Bureau of Economic Research.

On the other hand, we've had discussions in class about algorithmic discrimination and humans' need to audit algorithms to minimize bias.

Mike Walsh writes in Harvard Business Review that "there is another more insidious side-effect of our increasingly AI-powered society — the systematic inequality created by the changing nature of work itself" (source: HBR). Walsh is also critical of the possibility of "algorithmically managed jobs" that create job polarization between high-skill employees and lower-skill contractors. We can already see the divide in gig economy companies like Uber, Grubhub, and Instacart: highly-paid computer engineers and low-paid delivery people.

To borrow the language from Charles Mann, Mike Walsh is the prophet foretelling our AI future's grim possibilities. The technological optimists are the wizards who believe our human ingenuity and altruism will lead to a better world. These wizards include the G-MAFIA (Google, Microsoft, Apple, Facebook, IBM, and Amazon), an acronym created by author Amy Webb to describe the nine tech companies that are shaping our AI future. Webb writes,"

"I firmly believe that the leaders of these nine companies are driven by a profound sense of altruism and a desire to serve the greater good: they clearly see the potential of AI to improve health care and longevity, to solve our impending climate issues, and to lift millions of people out of poverty." 

This class has raised my awareness of AI applications hoovering up large pools of public data to inform decisions. That knowledge also means I'm now conscious of the lack of data privacy regulations in the United States. 

In popular culture, the HBO show Westworld explores the perils of expansive data-collection. Publications like Wired, The Atlantic, and VentureBeat have encouraged all developers to watch Westworld. The fractured way we consume media means I'm not confident that a singular piece of content like Upton Sinclair's The Jungle will galvanize public outrage to AI's negative aspects. It's hard to paint AI a villain when it is faceless and shapeless. 

I'm hopeful that AI can reduce poverty and economic inequality, but citizens will need to organize and push governments to create and enforce regulations to achieve these goals. Establishing more vital data privacy rules and limiting what information algorithms can consider might help the government shape more equitable outcomes.