IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1811.06606.html
   My bibliography  Save this paper

Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps

Author

Listed:
  • Daniel Muller

Abstract

In recent years, artificial intelligence (AI) decision-making and autonomous systems became an integrated part of the economy, industry, and society. The evolving economy of the human-AI ecosystem raising concerns regarding the risks and values inherited in AI systems. This paper investigates the dynamics of creation and exchange of values and points out gaps in perception of cost-value, knowledge, space and time dimensions. It shows aspects of value bias in human perception of achievements and costs that encoded in AI systems. It also proposes rethinking hard goals definitions and cost-optimal problem-solving principles in the lens of effectiveness and efficiency in the development of trusted machines. The paper suggests a value-driven with cost awareness strategy and principles for problem-solving and planning of effective research progress to address real-world problems that involve diverse forms of achievements, investments, and survival scenarios.

Suggested Citation

  • Daniel Muller, 2018. "Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps," Papers 1811.06606, arXiv.org, revised Nov 2018.
  • Handle: RePEc:arx:papers:1811.06606
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1811.06606
    File Function: Latest version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Farber, Stephen C. & Costanza, Robert & Wilson, Matthew A., 2002. "Economic and ecological concepts for valuing ecosystem services," Ecological Economics, Elsevier, vol. 41(3), pages 375-392, June.
    2. Herbert A. Simon, 1991. "Bounded Rationality and Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 125-134, February.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1811.06606. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: http://arxiv.org/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.