Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Grèzes, and Baumard (2020)

Spanton, Rory Wilfrid; Guest, Olivia

doi:10.48550/arxiv.2202.08674

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…In such a light, we can invite introspection or dialogue on if AI in this form is a science, is virtuous, etc. The same goes for the relationships between eugenics, physiognomy, or other ancestral θ-, Θ-, and T-theories to modern computational sciences generally, and psychology and genetics specifically (see Figure 3 herein, as well as Birhane & Guest, 2021;Erscoi et al, 2023;Scheuerman et al, 2021;Spanton & Guest, 2022;TallBear, 2013;Yakushko, 2019).…”

Section: Geneological Analysismentioning

confidence: 87%

What makes a good theory, and how do we make a theory good?

Guest¹

2023

Preprint

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I present an ontology of criteria for evaluating theory to answer the titular question from the perspective of a scientist practitioner. Set inside a formal account of our adjudication over theories, a metatheoretical calculus, this ontology comprises: a) Metaphysical commitment, the need to highlight what parts of theory are not under investigation, but are assumed, asserted, or essential. b) Discursive survival, the ability to be understood by interested non-bad actors, to withstand scrutiny within the intended (sub)field(s), and to negotiate the dialectical landscape thereof. c) Empirical interface, the potential to explicate the relationship between theory and observation, i.e., how observations relate to, and affect, theory and vice versa. d) Minimizing harm, the reckoning with how theory is forged in a fire of historical, if not ongoing, abuses — from past crimes against humanity, to current exploitation, turbocharged or hyped by machine learning, to historical and present internal academic marginalization. This work hopes to serve as a possible beginning for scientists who want to examine the properties and characteristics of theories, to propose additional virtues and vices, and to engage in further dialogue. Finally, I appeal to practitioners to iterate frequently over such criteria, by building and sharing the metatheoretical calculi used to adjudicate over theories.

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Section: Geneological Analysismentioning

confidence: 87%

What makes a good theory, and how do we make a theory good?

Guest¹

2023

Preprint

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“…Practitioners have their favourite theories, and they choose what to spend their research time on, which theories to criticise, to abandon, and even which they decide to call pseudoscientific (e.g. Fleming et al, 2023;Spanton and Guest, 2022). All these ebbs and flows of accumulating, (re)building, and abandoning theoretical or empirical work boil down to factors under scientists' control in many cases, such as what theoretical framework to operate under, which methodology to use, and what assumptions to make.…”

Section: Why a Calculus? Why An Ontology?mentioning

confidence: 99%

“…promoting dehumanising and contorted self-images, which have only recently been discussed by practitioners themselves from the inside of these disciplines (viz. Birhane and Guest, 2021;van Rooij et al, 2023;Spanton and Guest, 2022;Forbes et al, 2022;van der Gun and Guest, 2023;Erscoi et al, 2023;Barlas et al, 2021). Such an emphasis is not only scientifically advantageous, but also an ethically unavoidable facet of our work.…”

Section: Minimising Harmmentioning

confidence: 99%

What Makes a Good Theory, and How Do We Make a Theory Good?

Guest

2024

Comput Brain Behav

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I present an ontology of criteria for evaluating theory to answer the titular question from the perspective of a scientist practitioner. Set inside a formal account of our adjudication over theories, a metatheoretical calculus, this ontology comprises the following: (a) metaphysical commitment, the need to highlight what parts of theory are not under investigation, but are assumed, asserted, or essential; (b) discursive survival, the ability to be understood by interested non-bad actors, to withstand scrutiny within the intended (sub)field(s), and to negotiate the dialectical landscape thereof; (c) empirical interface, the potential to explicate the relationship between theory and observation, i.e., how observations relate to, and affect, theory and vice versa; (d) minimising harm, the reckoning with how theory is forged in a fire of historical, if not ongoing, abuses—from past crimes against humanity, to current exploitation, turbocharged or hyped by machine learning, to historical and present internal academic marginalisation. This work hopes to serve as a possible beginning for scientists who want to examine the properties and characteristics of theories, to propose additional virtues and vices, and to engage in further dialogue. Finally, I appeal to practitioners to iterate frequently over such criteria, by building and sharing the metatheoretical calculi used to adjudicate over theories.

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“…And so, a certain technology may be deemed problematic in one context or point in time, but harmless or even positive in another. Damnation to the dustbin of history might be appropriate for certain technologies, for e.g., all physiognomic algorithms (although case-by-case investigations might be needed for such AI too, Spanton & Guest, 2022), but it is certainly not something this lens can on its own decide for us. Hence below, we do not ask, for example, whether sex robots should be in principle allowed or banned, but rather examine in which ways and to what extent we see sex robots being caught up in a pattern of Pygmalion displacement.…”

Section: Jennifer Rhee 2010mentioning

confidence: 99%

Pygmalion Displacement: When Humanising AI Dehumanises Women

Erscoi¹,

kleinherenbrink²,

Guest³

2023

Preprint

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We use the myth of Pygmalion as a lens to investigate and frame the relationship between women and artificial intelligence (AI). Pygmalion was a legendary ancient king of Cyprus and sculptor. Having been repulsed by women, he used his skills to create a statue, which was imbued with life by the goddess Aphrodite. This can be seen as one of the primordial AI-like myths, wherein humanity creates intelligent life-like self-images to reproduce or replace ourselves. In addition, the myth prefigures historical and present gendered dynamics within the field of AI and between AI and society at large. Throughout history, the theme of women being replaced by inanimate objects (e.g. automata, algorithms) has been repeated, and continues to repeat in contemporary AI technologies. However, this socially detrimental pattern in technology — what we dub Pygmalion displacement — is often overlooked, whether due to naive excitement about new developments, or due to an unacknowledged sexist history of the field itself. As we demonstrate herein, Pygmalion displacement prefigures heavily, but in an unacknowledged way, in the original Turing test, the imitation game: a central thought experiment, foundational to AI. With women, and the feminine generally, being both dislocated and erased from and by technology, AI is and has been (presented as) created mainly by privileged men, subserving capitalist patriarchal ends. This poses serious dangers to women and other marginalised people. By tracing the historical and ongoing entwinement of femininity (from a patriarchal perspective) and AI, we aim to understand, make visible, and start a dialogue on the ways in which AI harms women.

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Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Grèzes, and Baumard (2020)

Abstract: We thank Iris van Rooij, Sam Forbes and Brad Wyble for helpful comments on a previous draft of this manuscript.

Cited by 5 publications

References 13 publications

What makes a good theory, and how do we make a theory good?

What makes a good theory, and how do we make a theory good?

What Makes a Good Theory, and How Do We Make a Theory Good?

Pygmalion Displacement: When Humanising AI Dehumanises Women

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