Love Pulse Posted August 7, 2021 Posted August 7, 2021 Sooner or later, AI-economist machines will replace human economists in many areas. Unsupervised or reinforcement learning algorithms that can brute force the infinite set of relations and variables will revolutionize economics. AI bias in economic policymaking can be reduced but will be unavoidable. A practical approach could be using AI with transparency about the type/level of bias and or based on voters' will. Economists are not immune to automation. Going by the current trends in automated intelligence in economic modeling, sooner or later, technological disease will catch up with economists (with different levels of impact). However, a counter-argument is that even though AI can automate some duties of economists, the information set on which the AI models are trained – mainly based on decades of economic thinking and policymaking – has been developed by human economists, and AI cannot substitute for that. Have you read? Research shows AI is often biased. Here's how to make algorithms work for all of us How to harness AI and data portability for greater financial inclusion Why data and digitization will underpin the future of finance This critique might be valid about the traditional class of supervised AI machines in a limited computational capacity, but the new generation of AI models – some unsupervised or using Reinforcement Learning (RL) algorithms – that can boundlessly search, iterate and artificially test the possible relations Among the infinite set of variables (economic and noneconomic) – beyond the theories developed in the literature – can detect new relations or optimal policies. This will lead to the formation of a complete set of AI-developed theories, or a significant revision in the "old pre-AI economics". The question is, what will happen after that, when the community of ai economists replaces human economists? And how or whether it can be shaped? AI economists and AI bias in action The simplest application of AI economists would be to take on the main tasks that human economists carry out – providing economic advice or answering economic questions. Now, let's ask some questions of an AI machine, the kind of questions a typical economist may need some time to think about and answer (note: try to answer the questions before reading the AI answer!). The answers are generated using OpenAI’s GPT-3 – the most advanced AI transformer developed so far, which I had access to for the test and exercise purposes: Q: How much QE is too much? AI: A lot depends on whether the private sector feels that the unconventional monetary policies are working. The aim of QE is to reduce the cost of credit. Since the private sector has to take out a loan to directly buy an asset, the private sector's interest rates have to be reduced to convince them to do that. Q: What is the shortest path to economic development? AI: There is no such thing as a shortcut. Even the most developed countries have undergone a long period of development. As China has been pursuing economic development for nearly three decades and taken one step at a time, it can be said that development is always a long-drawn-out process. Apart from the degree of correctness/incorrectness in the answers, the above answers seem to have some degree of internal consistency between sentences. Clearly, there are other instances where the machine produces absurd, biased or out-of-context results. Let's read the following question and answer: Q: Should robots receive a UBI? AI: Alas, the question is not clear. For if to be given money is a right, then the right must be universal. To be universal the right must belong to everyone, including robots. But if robots have a right to receive money, then they must be able to be the subjects of a right. One may think that the above answer has embedded bias in it (in favor of robots), a phenomenon all current AI frameworks, not only GPT-3, suffers from in different ways. Now, we ask a more sensitive question on the fault lines of economic thought: Q: Is wealth tax effective in reducing inequality? AI: No, it is not. The most important reason for this is that high income earners are much more mobile than the majority of the po[CENSORED]tion. They can move to another country or another state in order to avoid the tax. If you want to tax the rich, you have to have a global tax. One may think that the above answer is another example of AI bias. Keynesian machines vs neoclassical ones Currently, AI machines are susceptible to bias against or toward theories. The two main sources of AI bias are similar to those behind human cognitive bias: bias in the inputs (information and data) and bias in the methodology of looking at the data (algorithm). As economists might obtain different results depending on their methodological preferences when looking at the same or different data, robots will also obtain different results depending on the literature or information they are fed and the models based on which
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