Imitating Gender as a Measure for Artificial Intelligence:

Is It Necessary?

Huma Shah

and Kevin Warwick

School of Computing, Electronics and Maths, Coventry University, 3 Gulson Road, CV1 2JH, U.K.

Deputy Vice Chancellor-Research, Alan Berry Building, Priory Street, Coventry University, CV1 5FB, U.K.

Keywords: Cisgender, Gender, Gender-blur, Gender-in-AI, Imitation Game, Machine Intelligence, Sex,

Simultaneous-comparison, Transgender, Turing Test.

Abstract: Should intelligent agents and robots possess gender? If so, which gender and why? The authors explore one

root of the gender-in-AI question from Turing’s introductory male-female imitation game, which matured to

his famous Turing test examining machine thinking and measuring its intelligence against humans. What we

find is gender is not clear cut and is a social construct. Nonetheless there are useful applications for gender-

cued intelligent agents, for example robots caring for elderly patients in their own home.

1 INTRODUCTION

Ex Machina (Universal, 2014) features a cinematic

full robot Turing test (Harnad and Scherzer, 2008).

This is conducted between a male human and an

artificial intelligence “housed in a beautiful female

robot” not born of god or woman (Henry, 2014). The

question posed in the film is not what the human feels

about the AI, the question is how the female robot

feels about the male human (Figure 1). We ask, are

there instances when gender-in-AI could be

appropriate? The heart of this enquiry is founded in

Alan Turing’s man-woman imitation game, which

gave rise to his famous Turing test (Turing, 1950).

Figure 1: Ex Machina: Female AI and male human Turing

Test Judge (Universal Pictures).

The authors posit that there are gendered

applications for AI, for example in healthcare where

‘gender attributed AI’ could be appropriate for robo-

carers (CompanionAble, 2012), or in virtual

assistants (Artificial Solutions, 2015). We begin by

reviewing the attitudes, opinions and assessments of

the gender game.

2 GENDER IN THE IMITATION

GAME

Performance in chess was Turing’s initial comparison

measure for a machine player against a human player

(Shah, 2010). In proposing his question-answer test

Turing (1950) introduced the idea through a gender

game (see Figure 2). In this game a human

interrogator of either sex simultaneously questions

two hidden interlocutors: one man and one woman.

The purpose of the man is to pretend to be a woman;

the woman’s task is to tell the truth. The interrogator

must determine the actual woman. Replacing one of

the hidden interlocutors with a machine Turing (1950:

p. 435) asked:

“May not machines carry out something

which ought to be described as thinking

but which is very different from what a

man does?”

Turing quite rightly raised that question realising

after WWII that man does not think like every other

126

Shah, H. and Warwick, K.

Imitating Gender as a Measure for Artiﬁcial Intelligence: - Is It Necessary?.

DOI: 10.5220/0005673901260131

In Proceedings of the 8th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2016) - Volume 1, pages 126-131

ISBN: 978-989-758-172-4

man; man does think like woman; an Occidental

woman may not think like a woman from the Orient.

Figure 2: Gender Imitation Game.

Gender is regarded as an important feature in

Turing’s game by some (Copeland & Proudfoot 2008;

Sterrett, 2000; Lassègue, 1996; Hayes & Ford, 1995;

Genova, 1994). The contention is that both man and

machine impersonating a woman provides a stronger

test for intelligence. However, neither of these

researchers have explained what they mean by gender

nor have they provided empirical evidence to

substantiate their claim.

2.1 Gender Vs. Sex

Turing did not term his man-woman imitation game a

gender one, or his man-machine an artificial test

When considering gender and whether it is relevant

to agents’ development we face a number of salient

questions. If we are developing intelligent agents to

interact with, and support, humans ought we not to

audit:

• Whether sex and gender are the same thing?

• Regardless of your ‘self and socially

established’ gender do you remain the same sex

you were born?

• How many genders are there?

• Can a human be one gender physically and

another psychologically?

• Should an AI have a sex: be given male or

female genitals?

• Do we build agents and robots genderless?

• Do we innovate for human sensibilities?

• Do we make assumptions about the gender of

agent and robot developers?

The gender spectrum (2015) includes:

1. Cisgender: born as man or woman and identify

as same in life,

2. Inter: such as Hermaphrodite, could be due to

presence of both male and female reproductive

organs at birth,

3. Transgender: crossed over after birth – for

example, former American male Olympic

athlete Bruce Jenner sex-changed to Caitlyn

Jenner (IBT, 2015).

For an understanding of gender in different

cultures - Hijras in the Indian sub-continent identified

as feminised males, see Newman (2002), or identity-

based determination of gender - when a person’s

gender is authenticated by other people, see

Westbrook (2013). Newman details Western

interpretations of sex and gender: the former is the

“biological status of a person as either male or female

based on anatomical characteristics”, with the latter

“used to refer to socially constructed roles and

cultural representations” (2002, p. 353). Real life

cases show the ambiguity and messiness in clearly

defining sex or gender. The case of female South

African runner Caster Semenya is one. Semenya was

made to undergo gender tests to prove she was female

following accusations of being male, “because she

had elevated testosterone levels” (Telegraph, 2015).

Was Turing quite naïve then, or perhaps

mischievous? In drawing a distinction between men

and women he attributed imitation game roles,

possibly based on a belief that woman’s capacity is

better for telling the truth and the man’s ability greater

at pretence.

2.2 Sex and Intellectual Capacity

With the complexity involved in defining gender we

turn to the assumptions about gender and intellectual

capacity in the imitation game. According to

Lassègue (1996) Turing’s method of explaining his

simultaneous comparison game is ambiguous leading

to confusion concerning the function of the machine.

Lassègue (1996) interprets the role of the man in the

game as attempting to deceive by imitating “the

woman and the machine the two of them” (p.7). The

confusion extends to an interpretation that the

machine must imitate a man imitating a woman.

Hayes and Ford (1995) see the machine in such a

scenario as a “mechanical transvestite” (p. 973).

Genova accepts “Turing never speaks directly

about gender” (1994: p. 322). Turing’s topic of

consideration was not ‘computing, gender and

intelligence’, it was exploring the intellectual

capacity of a machine (Shah, 2010). Genova believes

Turing created more than just a machine-human

comparison test. She believes Turing questioned the

very nature of thinking and “how it should be

measured” (1994: p. 313). Genova claims “the game

centers on gender questions, not species ones ...

whether it [the machine] can fool player C into

believing it is one kind of human rather than another,

i.e. male not female” (p. 314). However, this is not

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127

borne out by Turing’s sample interrogator-witness

interaction (1950: p.446):

Interrogator: Would you say Mr. Pickwick

reminded you of a Christmas Day?

Witness: In a Way.

As evidence of gender significance, Genova

points to the first instance when a machine becomes

involved in Turing’s imitation game. Genova claims

Turing’s radical idea charged “thinking be measured

by gender miming” (1994: p. 315). Genova points to

the initial participants in the man-woman game and

how they were replaced. Turing evolved the

introductory scenario with three participants - man

(A), woman (B) and interrogator (C) with the

intriguing question (1950:p. 434):

“what will happen when a machine takes

the part of A [man] in this game?”

Turing’s usual questions of “chess and logical

games” were replaced with proposals to “measure

thought by the commonplace and presumably ‘easy’

activity of being male or female” (Genova, 1994: p.

315). Turing did pose gender questions initially:

“length of hair” (1950: p. 433), but after the digital

computer C was introduced into the game as player A

(played by the man in Turing’s explanatory scenario),

and the man moved to player B (played by the woman

earlier), Turing used “specimen questions”, such as

poetry and arithmetic, and intellectual games: “please

write me a sonnet on the subject of the Forth Bridge”;

“Add 34957 to 70764” and “Do you play chess?”

(1950: p.434).

The succeeding hidden entities, man and machine,

are again interrogated by a human player C who can

be of either sex. Genova asked “why would he

[Turing] be so careful about the gender assignments

in laying out the game, i.e. A is a man, B is a woman?”

(1994: p. 314). Genova overlooks that the man-

woman game was preparatory for the machine-human

test. By the fourth unfolding of the three-participant

imitation game for machine thinking Turing sets out

the new participants as follows:

player A = a digital computer (C);

player B = (hu)man;

player C = human interrogator.

2.2.1 Imitating a Woman

Turing matured the gender discrimination scenario to

an interrogation of a machine that is simultaneously

compared against a human. Turing did not direct that

the part of B, played by a woman in the male-female

scenario, should be played by a man pretending to be

a woman, Hayes and Ford’s interpretation (1995).

Turing had opportunity to be explicit in his work

before his death in 1954 had he intended the machine

and the human both to imitate a woman in the

machine-human comparison. Turing surely did not

shy of scripting on other radical items, such as extra-

sensory perception and telepathy (1950: p. 453).

Genova ignores where, anticipating the objection

of consciousness in the machine (‘Argument 4’ in

section 6: Contrary Views on the Main Question,

1950), Turing referred to a two-participant scenario

dispensing with the hidden male comparator

altogether: “player B omitted” (p. 446) with the

machine undergoing direct questioning by a human

interrogator (Shah, 2013; Shah, 2011). Genova

discounts Turing’s pointer to real-life one-to-one

situations in interviewer/interviewee scenes, “under

the name of viva voce to discover whether someone

really understands something or has learnt it parrot

fashion” (1950: p. 446). Turing’s 1952 BBC radio

discussion shows that he did not exclude women from

acting as the interrogator of the machine ‘witness’ in

his one-to-one test (Shah, 2013).

2.3 Turing & Gender

Genova (1994) states “computing accomplishes the

miracle of creation” (p. 320), viewing the computer

“as the ultimate kind of dynamic technology” (p.

322). Turing’s personal life made it abhorrent for him

to intimately participate in creating another intelligent

being (Shah, 2014; Hodges, 1992), what Genova

refers to as Turing’s “sexual dilemma” (1994: p. 317),

so he conceived an alternative process bringing a

thinking entity into the world, as opposed to the

‘natural one’ (Henry, 2014). Genova concludes “in

Turing’s brave new world, female machines are

absent ... inability to keep his personal life out of his

scientific one” (p. 324). Genova’s desire for female

machines is pertinent, especially in the development

of gendered robots having persuasive power in

human-robot interaction (Siegel, Breazeal & Norton,

2009) and the human disposition to assign a robot as

a ‘he’, for example, in the case of NASA’s robot

astronaut Robonaut (Dattaro, 2015)

Genova’s question of why the female should tell

the truth in the introductory man-woman imitation

game marking “her as an inferior thinker” (1994: p.

319), echoes Lassègue (1996) who sees it as an

absence of strategy, the “odds are weighed too

heavily against the woman” (p. 6). That the man’s

task is to deceive exposes a view that deception

requires being clever in a way that a woman may not

be, or, as Lassègue (1996) put it, to Turing there was

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a “secret connection between gender and

intelligence” (p. 8).

2.3.1 Female Impersonation

Sterrett (2000) puts forward a test for machine

intelligence that is more American-centric than

anthropocentric. Her illustration involves knowledge

of baseball, an American sport: “Three strikes and

you’re out” (p.85). Sterrett interprets two distinct

formulations in Turing’s imitation game, both focus

on the three-participant test:

i) An ‘original game’ featuring a computer

or a man imitating a woman compared

against a woman, and

ii) The ‘standard test’ involving the

determination of which is a machine and

which is human.

Sterrett does not provide empirical evidence for

the supposition that her two tests yield different

results after examining different competencies: “one

employs a better characterization of intelligence” (p.

79). Sterrett’s coalescence with the “revisionist line”

(Piccinini, 2000: p. 112), provides no confirmation

that both man and machine impersonating the fairer

sex while the interrogator questions to find the real

woman, is a better test for intelligence. Dennett

(2004) does not see Turing committing himself to

such a view, that for a machine to think it has to think

“just like a human being – any more than he was

committing himself to the view that for a man to

think, he must think exactly like a woman” (p.270).

Sterrett (2000) advocates female impersonation

asserting that the original imitation game is the

stronger test for machine intelligence. Unlike

Turing’s intention, in Sterrett’s test the man’s

performance is central to the imitation game. Sterrett

justifies her view from an early Turing statement

(1950: p.434):

“what will happen when a machine takes

the part of A [the man] in this game? Will

the interrogator decide wrongly as often

when the game is played like this as he

does when the game is played between a

man and a woman”?

Sterrett suggests the machine’s intelligence can be

measured “by comparing the frequency with which it

succeeds in causing the interrogator to make the

wrong identification [that it is a woman] with the

frequency with which a man does so” (2000: p.83).

Sterrett’s test would have the interrogator kept in the

dark about the real point of the game, i.e., to find the

machine, instead be tasked to uncover the real

woman. However it might occur to a participant,

convened for an experiment involving interrogation,

that a machine might be present in one of the pairs.

Piccinini (2000) points out, “if Turing meant the

interrogator to ignore the real purpose of the game

why didn’t he say so?” (p.113).

Sterrett contrasts the double human-pair original

game with what she refers to as the standard Turing

test - another term for Genova’s species test: pitting a

machine against a hidden human with the interrogator

questioning both to discern the natural from the

artificial. Sterrett compares the interrogator

attempting to distinguish between a man and a

woman, when faced with two pairs of hidden entities

- man-woman / machine-woman with the machine-

human scenario, writing that “one need only pause to

consider the quantitative results each [original game

and standard test] can yield” (2000: p. 83). However

in actual results realised from practical Turing test

experiments, without imitating a woman machines

have been misclassified as human (Shah & Warwick,

forthcoming; Warwick & Shah, 2015; Warwick &

Shah, 2014abc; Shah & Warwick, 2010).

Sterrett asserts the man pretending to be a woman

would have to “critically edit” because he cannot

change his gender enforcing “self-conscious

critiques” of his natural “trained responses”. To

Sterrett, the man’s performance would provide a

human benchmark for the machine that furnishes

“value as a test for intelligence” (2000: p. 90). But

what of individuals like Caitlyn Jenner, once Bruce

Jenner the male athlete who won gold in the 1976

Summer Olympics and now a female modelling for

Vanity Fair’s front cover (2015)? What are natural

trained responses for transgenders?

2.3.2 Self-identity & Stereotypes

Sterrett concedes she is feeding into stereotypes. She

does not clarify how or why impersonating a woman

is a better test for intelligence than responding

satisfactorily to any questions. Sterrett’s test for the

‘best female impersonator’ between a mechanical

transvestite (Hayes & Ford, 1995), and the man

impersonating a woman, could be easier for married

men, Indian Hijras and transgenders. Sterrett

simplifies and reduces gender to the binary and the

confines the interrogation to ‘topics of interest to

women’. This restricts machine development to

systems that simulate a man impersonating a woman.

Gender is more complex than division into socially

acceptable norms of ‘male’ and ‘female’. As Clarey

(2009) points out “humans like categories neat, but

nature is a slob”. Dreger (2010) shows that there

Imitating Gender as a Measure for Artiﬁcial Intelligence: - Is It Necessary?

129

needs to be clarification of the distinction between sex

and gender. Sex is a “conglomeration of anatomical

and physiological features that differ between typical

females and males ... what your body is about”

whereas gender is “who you are ... self identity”. To

Dreger, “gender role refers to your social identity” (p.

22). Hence when something as complex as gender is

so muddled and not clear-cut, Sterrett’s statement

“setting the task of cross gendering one’s responses is

a stroke of genius” (2000: p. 91) is too simple.

Stereotypical views are held by some

interrogators in practical Turing tests: a female

cybernetics undergraduate participating as a human

foil for a machine was misclassified as a male, an

instance of gender blur (see Shah and Warwick,

forthcoming). The assumption is clear: males are

more likely to study certain subjects at university than

females. However, in that same experiment, a human

control duo test embedded among machine-human

pairs, the interrogator wrongly classified the male

human as a female. In other practical Turing tests

Eugene Goostman machine, developed to imitate a

male child, was classified as a human female (Shah

and Warwick, forthcoming), while Elbot virtual robot

bereft of human characteristics was classified as a

male professor (Shah and Warwick, forthcoming;

Shah and Warwick, 2010).

3 STRENTHENING TURING’S

TEST

Purtill (1971) felt it might be fun to “program the

[imitation] game and try it on a group of students” (p.

291). The authors have conducted 5-min duration

public Turing test experiments involving male and

female students and non-students, experts and non-

experts (Warwick and Shah, 2015; Warwick and

Shah, 2014abc; Shah et al., 2012; Shah and Warwick,

2010). Interrogators were asked to identify hidden

interlocutors as:

• Machine, human or unsure?

• If human:

o Male or female?

o Age range: child, teen, adult?

o Native or non-native English

speaker?

One focus of ongoing analysis, from over 400

practical Turing tests involving more than 80

interrogators and 6 machines, is how often

interrogators assigned hidden interlocutors, human

and machine, as male or female.

Gender no longer plays a central part in Turing’s

test once the digital machine is introduced (1950: p.

446). To strengthen the test, the authors suggest

removing the ‘unsure’ option used in previous

experiments (Warwick and Shah, 2014c) and direct

the tests with the following adapted conditions:

• Increasing interrogation period every few

years;

• Ask interrogators to classify hidden

interlocutors as either machine, human

male, or human female.

In this way machine progress can be regularly

evaluated advancing artificial conversational

performance.

4 CONCLUSIONS

The crux of Turing’s game is the machine’s

intellectual capacity to respond satisfactorily to

unrestricted questions put by male or female

interrogators. The authors oppose the idea that the

machine in a Turing test should imitate a man

pretending to be a woman, because it restricts the

machine-human comparison test to a dependency on

stereotypical female-male views on societal roles.

Nonetheless gender concerns should be incorporated

in the development of AI. More research is needed to

find if embodied carers and companions or virtual

assistants are accepted more as genderless, or with

female or male, including as part of future healthcare.

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