Distinguishing AI from Male/Female Dialogue

Huma Shah, Kevin Warwick

2016

Abstract

Without knowledge of other features, can the sex of a person be determined through text-based communication alone? In the first Turing test experiment enclosing 24 human-duo set-ups embedded among machine-human pairs the interrogators erred 50% of the time in assigning the correct sex to a hidden interlocutor identified as human. In this paper we present five transcripts, in four gender blur occurred: Turing test interrogators misclassified male for female and vice versa. In the fifth, machine-human conversation artificial dialogue was branded as female teen. Did stereotypical views on male and female talk sway the judges to assign one way or another? This research is part of ongoing analysis of over 400 tests involving more than 80 human judges. Can we overcome unconscious bias and improve development of agent language?

References

  1. Albrechtsen, J.S., Meissner, C.A., and Susa, K.J. (2009). Can intuition improve deception detection performance? Journal of Experimental Social Psychology. Vol. 45: pp. 1052-1055.
  2. Artificial Solutions. The Top Traits of Intelligent Virtual Assistants. White Paper. Available here: http://www.artificial-solutions.com/about-artificialsolutions/resources/registered-whitepapers/ accessed 16.10.15.
  3. De Angeli, A., and Carpenter, R., 2005. Stupid Computer! Abuse and Social Identity. Proceedings of Workshop on Abuse: the darker side of human-computer interaction. September 12: Rome. Available here: http://www.agentabuse.org/Abuse_Workshop_WS5.pdf.
  4. Ean, L.C., 2010. Face-to-face versus computer-mediated communication: exploring employees' preference of effective employee communication channel. International Journal for the Advancement of Science & Arts. Vol 1(2), 38-48.
  5. Faulkner, X., and Culwin, F., 2005. When Fingers do the Talking: a study of text messaging. Interacting with Computers. Vol. 17, 167-185.
  6. Holbrook, C., Fessler, D.M.T., and Navarrete, C.D., 2015. Looming large in other's eyes: Racial stereotypes illuminate dual adaptations for representing threats versus prestige as physical size. Evolution and Human Behaviour. In press, available from DOI: http://dx.doi.org/10.1016/j.evolhumbehav.2015.08.004.
  7. Holmes, J., 2006. Sharing a Laugh: Pragmatic aspects of humor and gender in the workplace. Journal of Pragmatics. Vol 38, 26-50.
  8. Holmes, J., 2005. Leadership Talk: How do leaders 'do mentoring' and is gender relevant? Journal of Pragmatics. Vol 37, 1779-1800.
  9. Independent, 2015a. Theresa May's speech to the Conservative Party Conference- in Full. 6 October Available here: http://www.independent.co.uk/news/ uk/politics/theresa-may-s-speech-to-the-conservativeparty-conference-in-full-a6681901.html.
  10. Independent, 2015b. Tory Party Conference 2015: David Cameron's speech in full. 7 October. Available here: http://www.independent.co.uk/news/uk/politics/toryparty-conference-2015-david-camerons-speech-in-fulla6684656.html.
  11. Loebner Prize, 2008. 18th Loebner Prize for Artificial Intelligence. http://loebner.net/Prizef/2008_Contest/ loebner-prize-2008.html.
  12. Lueptow, L.B., Garovich, L., and Lueptow, M.B., 1995. The persistence of gender stereotypes in the face of changing sex roles: Evidence contrary to the sociocultural model. Ethology and Sociobiology. Vol. 16 (6), 509-530.
  13. Pavia, W., 2008. Machine Takes on Man at Mass Turing Test. The Times UK. Available online: http://technology.timesonline.co.uk/tol/news/tech_and _web/article4934858.ece.
  14. Reading University, 2012. Computer or Human - Can you tell the difference? https://www.reading.ac.uk/newsand-events/releases/PR451417.aspx.
  15. Shah, H., 2010. Deception detection and machine intelligence in practical Turing tests. PhD thesis, University of Reading, UK.
  16. Shah, H., Warwick, K., Bland, I.M., Chapman, C.D., and Allen, M., 2012. Turing's Imitation Game: Role of Error-making in Intelligent Thought. Turing in Context II, Brussels: 10-12 October.
  17. Shah, H., and Warwick, K., 2010a. From the Buzzing in Turing's Head to Machine Intelligence Contests. Proceedings of Symposium for Towards a Comprehensive Intelligence Test. AISB Convention, De Montfort, UK, 29 March - 1 April.
  18. Shah, H., and Warwick, K., 2010b. Hidden Interlocutor Misidentification in Practical Turing tests. Minds and Machines, Vol 20(3), 441-454.
  19. Shah, H., and Warwick, K., 2008. Can Machines think? Results from the 18th Loebner Prize for Artificial Intelligence contest. The University of Reading: http://www.reading.ac.uk/15/research/ResearchRevie wonline/featuresnews/res-featureloebner.aspx accessed: 7.10.15.
  20. Turing, A.M., 1952 in Braithwaite, R., Jefferson, G., Turing, A.M., and Newman, M. Can Automatic Calculating Machines Be Said to Think? Transcript of BBC radio broadcast. In (Eds) S.B. Cooper & J. van Leeuwen, Alan Turing: His Work and Impact, Elsevier, 2013.
  21. Turing, A.M., 1950. Computing Machinery and Intelligence. MIND, Vol 59 (236), pp. 433-460.
  22. UN Broadband Commission, 2015. Cyber violence against women and girls: a world-wide wake-up call. UN Digital Development Working Group on Broadband and Gender. Report available from: http:// www.broadbandcommission.org/publications/Pages/b b-and-gender-2015.aspx accessed 7.10.15.
  23. Demchenko, E, and Veselov, V., 2008. Who Fools Whom? The Great Mystification, or Methodological Issues on Making Fools of Human Beings. In (Eds) Epstein, R., Roberts, G., and Beber, G. Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer.
  24. Warwick, K., and Shah, H., 2015. The importance of a human viewpoint on computer natural language capabilities: a Turing test perspective. AI and Society in press]. Available from http://dx.doi.org/ 10.1007/s00146-015-0588-5.
  25. Warwick, K., and Shah, H., 2014. Good Machine Performance in Turing's Imitation Game. IEEE Transactions on Computational Intelligence and AI in Games 6 (3), 289-299.
  26. Willis, J., and Todorov, A. (2006). First Impressions: Making up your mind after 100ms exposure to a face. Psychological Science 17 (7), pp 592-598.
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Paper Citation


in Harvard Style

Shah H. and Warwick K. (2016). Distinguishing AI from Male/Female Dialogue . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-172-4, pages 215-222. DOI: 10.5220/0005736802150222


in Bibtex Style

@conference{icaart16,
author={Huma Shah and Kevin Warwick},
title={Distinguishing AI from Male/Female Dialogue},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2016},
pages={215-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005736802150222},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Distinguishing AI from Male/Female Dialogue
SN - 978-989-758-172-4
AU - Shah H.
AU - Warwick K.
PY - 2016
SP - 215
EP - 222
DO - 10.5220/0005736802150222