Artificial Intelligence (AI) Dystopian Scenarios and Projected Sociotechnical Disasters: Engendering Fear and Uncertainty in Public Discourse

Friday, 20 July 2018: 18:15
Oral Presentation
Jo Ann ORAVEC, University of Wisconsin-Whitewater and Madison, USA
Artificial Intelligence (AI) application methodologies and research agendas have suffered dramatic setbacks as well as considerable successes since their origins prior to WWII. This presentation explores the recent discourse that links AI to various kinds of dystopian, disaster, and apocalyptic scenarios (including mass unemployment, robot takeovers, and human enslavement and dependence) engendering fears that that can be used opportunistically to influence public policy. Both developed and developing nations are reportedly facing challenges involving AI, with various forms of workforce displacement being projected in economic studies. Dystopian futures have indeed been outlined in science fiction narratives of past decades; however, many recent portraits of the future are being proposed by entrepreneurs and scientists (including Elon Musk and Stephen Hawking) who often buttress their projections with specific technological details, timeframes, and even full-blown research agendas. The presentation maps the themes of AI disaster projections and links them to overall societal anxieties about the future of society in relation to AI (including surveillance and cybersecurity concerns). It also discusses modes through which AI disaster themes are reaching the public, with various social media venues and thinktank efforts along with more traditional books and television outlets conveying information about potential threats to humanity. The AI hype and backlash factors are also explored in this presentation, with historical background on how “AI winters” emerged when many well-funded AI advances did not provide adequate return on investment and failures appeared to outpace successes.

Some references: Johnson, D. G., & Verdicchio, M. (2017). AI Anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267-2270.

Kurzweil, R. (2016). The Singularity Is Near. In Ethics and emerging technologies, ed. by Ronald Sandler. New York: Springer.

Oravec, J. A. (2014). Expert Systems and Knowledge-Based Engineering (1984-1991): Implications for Instructional Systems Research. International Journal of Designs for Learning, 5(2).