Advertisement

AI & SOCIETY

, Volume 34, Issue 1, pp 145–152 | Cite as

Towards a unified framework for developing ethical and practical Turing tests

  • Balaji Srinivasan
  • Kushal ShahEmail author
Open Forum

Abstract

Since Turing proposed the first test of intelligence, several modifications have been proposed with the aim of making Turing’s proposal more realistic and applicable in the search for artificial intelligence. In the modern context, it turns out that some of these definitions of intelligence and the corresponding tests merely measure computational power. Furthermore, in the framework of the original Turing test, for a system to prove itself to be intelligent, a certain amount of deceit is implicitly required which can have serious security implications for future human societies. In this article, we propose a unified framework for developing intelligence tests which takes care of important ethical and practical issues. Our proposed framework has several important consequences. Firstly, it results in the suggestion that it is not possible to construct a single, context independent, intelligence test. Secondly, any measure of intelligence must have access to the process by which a problem is solved by the system under consideration and not merely the final solution. Finally, it requires an intelligent agent to be evolutionary in nature with the flexibility to explore new algorithms on its own.

Keywords

Artificial intelligence Imitation game Turing test Machine ethics 

Notes

Acknowledgements

The authors thank the anonymous reviewer for very useful comments. A part of this work was done, while the authors were at the Indian Institute of Technology (IIT) Delhi, India.

References

  1. Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 elections. NBER working paper 23089Google Scholar
  2. Arleback JB, Bergsten C (2013) On the use of realistic fermi problems in introducing mathematical modelling in upper secondary mathematics. In: Lesh R, Galbraith P, Haines C, Hurford A (eds) Modeling students’ mathematical modeling competencies. International perspectives on the teaching and learning of mathematical modelling. Springer, DordrechtGoogle Scholar
  3. Bringsjord S, Bello P, Ferrucci D (2001) Creativity, the Turing test, and the (Better) Lovelace test. Mind Mach 11:3CrossRefzbMATHGoogle Scholar
  4. Burgess A (2013) A clockwork orange. Penguin Books, LondonGoogle Scholar
  5. Calude CS, Jurgensen H (2005) Is complexity a source of incompletenesss? Adv Appl Math 35:1MathSciNetCrossRefzbMATHGoogle Scholar
  6. Chaitin GJ (1982) Godel’s theorem and information. Int J Theor Phys 21:941CrossRefzbMATHGoogle Scholar
  7. Cohen PJ (2008) Set theory and the continuum hypothesis. Dover, New YorkGoogle Scholar
  8. Conroy NJ, Rubin VL, Chen Y (2015) Automatic deception detection: methods for finding fake news. Proc Assoc Inf Sci Technol 52:1CrossRefGoogle Scholar
  9. Gardner H (2011) Frames of mind: the theory of multiple intelligences. Basic Books, New YorkGoogle Scholar
  10. Godel K (1940) The consistency of the continuum-hypothesis. Princeton University Press, New JerseyzbMATHGoogle Scholar
  11. Halmos PR (1973) The legend of John von Neumann. Am Math Mon 80:382–394MathSciNetCrossRefzbMATHGoogle Scholar
  12. Legg S, Hutter M (2007) Universal intelligence: a definition of machine intelligence. Mind Mach 17:391CrossRefGoogle Scholar
  13. Levesque HJ (2014) On our best behaviour. Artif Intell 212:27MathSciNetCrossRefzbMATHGoogle Scholar
  14. Levesque HJ, Davis E and Morgenstern L (2011) The Winograd schema challenge. In: AAAI Spring symposium: logical formalizations of commonsense reasoningGoogle Scholar
  15. Luger GF, Chakrabarti C (2017) From Alan Turing to modern AI: practical solutions and an implicit epistemic stance. AI Soc 32:321CrossRefGoogle Scholar
  16. Markowitz DM, Hancock JT (2014) Linguistic traces of a scientific fraud: the case of diederik stapel. PLoS One 9:e105937CrossRefGoogle Scholar
  17. Oppy G, Dowe D (2016) The Turing test, the Stanford encyclopedia of philosophy. In: Zalta EN (ed). https://plato.stanford.edu/archives/spr2016/entries/turing-test/. Accessed 15 Aug 2017
  18. Silver D et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484CrossRefGoogle Scholar
  19. Tesla N (1937) A machine to end war, PBS.org. In: Viereck GS (ed). https://www.pbs.org/tesla/res/res_art11.html. Accessed 10 Sept 2017
  20. Turing A (1950) Computing machinery and intelligence. Mind 59:433MathSciNetCrossRefGoogle Scholar
  21. Warwick K, Shah H (2016a) Can machines think? A report on Turing test experiments at the Royal Society. J Expt Theo AI 28:989CrossRefGoogle Scholar
  22. Warwick K, Shah H (2016b) The importance of a human viewpoint on computer natural language capabilities: a Turing test perspective. AI Soc 31:207CrossRefGoogle Scholar
  23. Warwick K, Shah H (2016c) Effects of lying in practical Turing tests. AI Soc 31:5CrossRefGoogle Scholar
  24. Wu Y et al (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv:1609.08144
  25. Warwick K, Shah H (2016d) Taking the fifth amendment in Turing’s imitation game. J Expt Theor AI 29:1Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology (IIT) MadrasChennaiIndia
  2. 2.Department of Electrical Engineering and Computer ScienceIndian Institute of Science Education and Research (IISER)BhopalIndia

Personalised recommendations