{
  "title": "The Art in Artificial Intelligence: Make the Robots Serve the Public Good",
  "publication": "IMF Blog, January 11, 2018",
  "sourceUrl": "https://www.imf.org/en/blogs/articles/2018/01/11/the-art-in-artificial-intelligence-make-the-robots-serve-the-public-good",
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  "summary": "Over the past few years, artificial intelligence has rapidly matured as a viable field of technology.",
  "sections": [
    {
      "heading": "Overview",
      "content": "- Author: Brian McNeill\n- Date: January 11, 2018\n- 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."
    },
    {
      "heading": "Key areas of importance to the IMF",
      "content": "- Four areas of artificial intelligence and machine learning of importance to the IMF’s work:\n  - Governance\n  - Labor markets\n  - Taxes\n  - Social equity"
    },
    {
      "heading": "Governance",
      "content": "- Issues to address before basing analysis or policy advice on Big Data or algorithms:\n  - Provenance of data.\n  - Matters of privacy and informed consent.\n- Characteristics of Big Data that complicate its use:\n  - Dynamic and heterogeneous.\n  - May originate in sectors that do not map cleanly to the IMF’s existing lines of responsibility or expertise.\n  - Examples: data generated from e-commerce, the Internet of Things, satellite data, or supply-chain and logistics data.\n- Institutional need:\n  - Both the IMF and countries will need to develop expertise in the use of micro-level data."
    },
    {
      "heading": "Labor markets",
      "content": "- Expected structural changes:\n  - Fewer middle-skilled jobs (examples provided): insurance claims processing; jobs performed in a constrained physical space, like fork-lift operator or order expeditor.\n  - 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.\n- Policy implications:\n  - Impacts on education, retirement, and social welfare programs.\n  - Potential for large numbers of middle-class jobs to be eliminated, leading to unemployment or underemployment.\n  - Some jobs will require extensive retraining to ensure workers can perform new tasks.\n  - 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."
    },
    {
      "heading": "Taxes",
      "content": "- Fiscal challenge described:\n  - 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.\n- Current revenue reliance cited:\n  - Among the Organization for Economic Cooperation and Development countries, roughly half of government revenue is derived from individual income or social insurance taxes.\n- Policy consideration:\n  - Tax structures will need to change to sustain government revenues near current levels and to avoid creating further disincentives to job creation.\n  - Example mentioned: Microsoft founder Bill Gates suggested that a tax might be levied on robots."
    },
    {
      "heading": "Social equity",
      "content": "- Concerns about computer-driven decision-making:\n  - Systems should be open to scrutiny and inspection.\n  - Must not be automated versions of mental models that embed legacies of social inequality.\n- Example risk:\n  - 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."
    },
    {
      "heading": "Methodological and accountability challenges",
      "content": "- Contrast with economists’ practice:\n  - Economists build models and refine them to reduce error and improve robustness.\n- Challenge with many AI methods:\n  - Some methods are impervious to external analysis because AI learns and adapts with new data; after millions of iterations, the algorithm can change substantially.\n  - The explanation “The algorithm told me to do it,” is unlikely to withstand public inquiry as a basis for policy development."
    },
    {
      "heading": "Next steps and IMF actions",
      "content": "- Institutional response:\n  - The IMF will continue to bring in experts to promote information exchange.\n  - The IMF will develop training so staff can work with emerging AI technologies.\n- Intended outcome:\n  - These activities will help the IMF work with its member countries to make artificial intelligence serve the public good.\n\nSource: The Art in Artificial Intelligence: Make the Robots Serve the Public Good — Brian McNeill, January 11, 2018\n\n---\n\n\nSource: https://www.imf.org/en/blogs/articles/2018/01/11/the-art-in-artificial-intelligence-make-the-robots-serve-the-public-good"
    }
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    "Authors: Brian McNeill",
    "Published: January 11, 2018",
    "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:",
    "Issues to address before basing analysis or policy advice on Big Data or algorithms:",
    "Characteristics of Big Data that complicate its use:",
    "Institutional need:",
    "Expected structural changes:",
    "Policy implications:",
    "Fiscal challenge described:",
    "Current revenue reliance cited:",
    "Policy consideration:",
    "Concerns about computer-driven decision-making:",
    "Example risk:",
    "Contrast with economists’ practice:",
    "Challenge with many AI methods:",
    "Institutional response:",
    "Intended outcome:"
  ],
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