Artificial Intelligence and Income Inequality - Seeker's Thoughts

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Artificial Intelligence and Income Inequality


 An AI can simulate an economy millions of times to create fairer tax policy

When you see your surroundings, you come across rich and poor. However, when we see poverty, we consider that as a curse of destiny etc, however, in terms of economics this is considered as a system failure. So, the question is – can we make the world better?

Can we have a world which has better economies, and no income inequalities?

What is income inequality?

Income inequality is an economic inequality where a number of people has lesser income, and some people have more income. This is also known as an aphorism,’ the rich get richer and poor get poorer’.

There are both ethical and political reasons for wanting to address the growing gap between rich and poor, according to experts ranging from economists and political scientists to social workers and activists.
Higher economic inequality creates social problems like – obesity, mental illness, homicides, teenage births, incarceration, child conflicts and drug use etc.  The situation does not end here- it also has lower life expectancy, educational performance, trust among strangers, woman’s status and social mobility as well.

The situation- why cities have different culture and more freedom than the villages? The answer is due to economic inequalities.
These above situations have been very well supported by the researches of British researchers Richard G. Wilkinson and Kate Pickett.

How do we measure income inequality?

Income inequality and income disparity segregations can be analysed through a variety of segmentations. Segmentations of income disparity analysis are used for analysing different types of income distributions.
The different types of income segmentations studied when analysing income inequality may include distributions for:
  • Male vs. female
  • Ethnicity
  • Geographic location
  • Occupation
  • Historical income
What are steps taken by the governments to tackle the income inequality?

Every developing or developed or poor country has its own challenges. However, there certain proven methods which are applicable in tackling the income inequality.

One of the most effective tools policymakers have to address it is taxation: governments collect money from people according to what they earn and redistribute it either directly, via welfare schemes, or indirectly, by using it to pay for public projects.

But though more taxation can lead to greater equality, taxing people too much can discourage them from working or motivate them to find ways to avoid paying—which reduces the overall pot.

Other tools for reducing the economic inequality can be-
Ø  Increase the minimum wage.
Ø  Build assets for working families.
Ø  Invest in education.
Ø  Make the tax code more progressive.
Ø  End residential segregation.
Ø  Using the help of artificial intelligence

How Artificial Intelligence can help in economy?

Artificial intelligence  is capable of beating humans at complex problems, and games. Therefore, if trained well, there is a possibility that it can run better economy, because it is not as greedy and corrupt as humans are when it comes to implementation.

Achieving a balance, and reducing the income inequality has been so far evidently difficult, and the governments have been working to settle this problem.

What is the core problem of income inequality and economics?

The economics contains the behavior of people, and the behavior of people is complex, and gathering data about behavior and synthesizing that data remains an herculean task.
Wherever, there comes the complexity, the man kind has always taken help of computers and AI’s, so it was also suggested by the Scientists of US Business Technology Company that Artificial Intelligence can help in better Policy Formulation.

Led by Richard Socher, the team has developed a system called the AI Economist that uses reinforcement learning—the same sort of technique behind DeepMind’s AlphaGo and AlpahZero—to identify optimal tax policies for a simulated economy.

The tool is still relatively simple (there’s no way it could include all the complexities of the real world or human behaviour), but it is a promising first step toward evaluating policies in an entirely new way. “It would be amazing to make tax policy less political and more data driven,” says team member Alex Trott.


In one early result, the AI found a policy that—in terms of maximizing both productivity and income equality—was 16% fairer than a state-of-the-art progressive tax framework studied by academic economists. The improvement over current US policy was even greater. “I think it's a totally interesting idea,” says Blake LeBaron at Brandeis University in Massachusetts, who has used neural networks to model financial markets. 

How Artificial Intelligence can contribute?

There are set of workers who have different level of skills, and specialization. Some workers may be lower skilled, some may be higher skilled, and their earning levels also differ. Therefore, in the end of the year, AI controlled policy maker, which uses it is own reinforment learning algorithms, can devise a tax rate. As the policymaker’s ultimate goal is to boost the productivity as well as income of all the workers.

Both reinforcement-learning models start from scratch, with no prior knowledge of economic theory, and learn how to act through trial and error—in much the same way that DeepMind’s AIs learn, with no human input, to play Go and StarCraft at superhuman levels. 


Neural networks have been used to control agents in simulated economies before.
 But making the policymaker an AI as well leads to a model in which the workers and policymaker continually adapt to each other’s actions.

This dynamic environment was a challenge for the reinforcement-learning models since a strategy learned under one tax policy may not work so well under another.

But it also meant the AIs found ways to game the system. For example, some workers learned to avoid tax by reducing their productivity to qualify for a lower tax bracket and then increasing it again.

The Salesforce team says this give-and-take between workers and policymaker leads to a simulation more realistic than anything achieved by previous models, where tax policies are typically fixed.

The tax policy that the AI Economist came up with is a little unusual. Unlike most existing policies, which are either progressive (that is, higher earners are taxed more) or regressive (higher earners are taxed less), the AI’s policy cobbled together aspects of both, applying the highest tax rates to rich and poor and the lowest to middle-income workers.

 Like many solutions that AIs come up with—such as some of AlphaZero’s game-winning moves—the result appears counterintuitive and not something that a human might have devised. But its impact on the economy led to a smaller gap between rich and poor.

To see if the AI-generated tax policy would influence human behaviour in a similar way, the team tested it on more than 100 crowd workers hired through Amazon’s Mechanical Turk, who were asked to take control of the workers in the simulation.

They found that the policy encouraged the humans to play in much the same way as the AIs, suggesting—at least in principle—that the AI Economist could be used to influence real economic activity.

What are the advantage of AI powered Simulation?

Usually it is hard to come up with optimal tax theories which were based on the past, as future constantly changes.
Therefore, the AI powered simulation can tweak parameters to explore different scenario and conditions.

For example- a pandamic like COVID 19 has impacted upon the world and in economics terms as well.
An AI can model the impact of pandemic by adding constrains like social distancing, restricted access to resources or by removing people from work force.
The team accepts that some economists will need persuading. To that end, they are releasing their code and inviting others to run their own models through it. In the long run, this openness will also be an important part of making such tools trustworthy.

A Conclusive Note

Learning from past experience through feedback loops (formulating, implementing, evaluating, calibrating), is common practice, just the feedback loops have become increasingly shorter.

Artificial intelligence, or AI, is a tool that can assist in this complex process. AI helps to sift through data and identify patterns that could improve decision-making, providing critical insights that may previously have been invisible.

Therefore, AI can definitely play a greater role however, certain dilemma like --fairness, social justice etc. including  who decides what is ethical? Should be considered as well.

But getting policymakers to prioritize these policies will depend on the actions of advocates, voters, and other supporters with a vision for a fair and inclusive society so strong, those things should also be included in policy making.

Better Policies, and careful implementation has potential to lift the families from poverty, and reduce the income inequality. While there is still some disagreements of the best way to reduce inequality, there is a growing consensus that inequality should be reduce.
The IMF joined this consensus in finding that inequality reduces overall economy growth as well as challenges basic democratic principle and fairness.

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