IDEAS home Printed from https://ideas.repec.org/p/ecl/harjfk/rwp19-010.html
   My bibliography  Save this paper

Human Rights, Artificial Intelligence and Heideggerian Technoskepticism: The Long (Worrisome?) View

Author

Listed:
  • Risse, Mathias

    (Harvard Kennedy School)

Abstract

My concern is with the impact of Artificial Intelligence on human rights. I first identify two presumptions about ethics-and-AI we should make only with appropriate qualifications. These presumptions are that (a) for the time being investigating the impact of AI, especially in the human-rights domain, is a matter of investigating impact of certain tools, and that (b) the crucial danger is that some such tools--the artificially intelligent ones--might eventually become like their creators and conceivably turn against them. We turn to Heidegger's influential philosophy of technology to argue these presumptions require qualifications of a sort that should inform our discussion of AI. Next I argue that one major challenge is how human rights will prevail in an era that quite possibly is shaped by an enormous increase in economic inequality. Currently the human-rights movement is rather unprepared to deal with the resulting challenges. What is needed is greater focus on social justice/distributive justice, both domestically and globally, to make sure societies do not fall apart. I also argue that, in the long run, we must be prepared to deal with more types of moral status than we currently do and that quite plausibly some machines will have some type of moral status, which may or may not fall short of the moral status of human beings (a point also emerging from the Heidegger discussion). Machines may have to be integrated into human social and political lives.

Suggested Citation

  • Risse, Mathias, 2019. "Human Rights, Artificial Intelligence and Heideggerian Technoskepticism: The Long (Worrisome?) View," Working Paper Series rwp19-010, Harvard University, John F. Kennedy School of Government.
  • Handle: RePEc:ecl:harjfk:rwp19-010
    as

    Download full text from publisher

    File URL: https://research.hks.harvard.edu/publications/getFile.aspx?Id=1748
    Download Restriction: no
    ---><---

    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:ecl:harjfk:rwp19-010. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/ksharus.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.