Distinguishing AI from Male/Female Dialogue

Huma Shah, Kevin Warwick

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?

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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