The Art in Artificial Intelligence: Make the Robots Serve the Public Good

IMF Blog, January 11, 2018

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Canonical URL: The Art in Artificial Intelligence: Make the Robots Serve the Public Good

Author: Brian McNeill
Date: January 11, 2018
Core message: Artificial intelligence (AI) has rapidly matured and entered daily life; governments, policymakers, and the IMF must harness AI’s benefits and ensure AI serves the public good.

Four areas of artificial intelligence and machine learning of importance to the IMF’s work:
- Governance
- Labor markets
- Taxes
- Social equity

Issues to address before basing analysis or policy advice on Big Data or algorithms:
- Provenance of data.
- Matters of privacy and informed consent.
Characteristics of Big Data that complicate its use:
- Dynamic and heterogeneous.
- May originate in sectors that do not map cleanly to the IMF’s existing lines of responsibility or expertise.
- Examples: data generated from e-commerce, the Internet of Things, satellite data, or supply-chain and logistics data.
Institutional need:
- Both the IMF and countries will need to develop expertise in the use of micro-level data.

Expected structural changes:
- Fewer middle-skilled jobs (examples provided): insurance claims processing; jobs performed in a constrained physical space, like fork-lift operator or order expeditor.
- These jobs have been more resistant to offshoring or automation so far but may disappear as AI improves and robots make decisions in ambiguous situations.
Policy implications:
- Impacts on education, retirement, and social welfare programs.
- Potential for large numbers of middle-class jobs to be eliminated, leading to unemployment or underemployment.
- Some jobs will require extensive retraining to ensure workers can perform new tasks.
- Many countries are already facing rapidly aging populations; premature exit of workers from the labor market would make it more difficult for governments to fund social-welfare and retirement benefits.

Fiscal challenge described:
- If labor markets rapidly shed middle-skilled or low-skilled jobs, tax structures will need to reflect the decreasing share of GDP attributable to wages and salaries.
Current revenue reliance cited:
- Among the Organization for Economic Cooperation and Development countries, roughly half of government revenue is derived from individual income or social insurance taxes.
Policy consideration:
- Tax structures will need to change to sustain government revenues near current levels and to avoid creating further disincentives to job creation.
- Example mentioned: Microsoft founder Bill Gates suggested that a tax might be levied on robots.

Concerns about computer-driven decision-making:
- Systems should be open to scrutiny and inspection.
- Must not be automated versions of mental models that embed legacies of social inequality.
Example risk:
- Use of data for personalized pricing based on predictive models could lead to redlining of particular groups of customers and further marginalization, creating a self-fulfilling prophecy.

Contrast with economists’ practice:
- Economists build models and refine them to reduce error and improve robustness.
Challenge with many AI methods:
- Some methods are impervious to external analysis because AI learns and adapts with new data; after millions of iterations, the algorithm can change substantially.
- The explanation “The algorithm told me to do it,” is unlikely to withstand public inquiry as a basis for policy development.

Institutional response:
- The IMF will continue to bring in experts to promote information exchange.
- The IMF will develop training so staff can work with emerging AI technologies.
Intended outcome:
- These activities will help the IMF work with its member countries to make artificial intelligence serve the public good.

Source: The Art in Artificial Intelligence: Make the Robots Serve the Public Good — Brian McNeill, January 11, 2018