Abstract
Despite the evolution of norms and regulations to mitigate the harm from biases, harmful discrimination linked to an individual’s unconscious biases persists. Our goal is to better understand and detect the physiological and behavioral indicators of implicit biases. This paper investigates whether we can reliably detect racial bias from physiological responses, including heart rate, conductive skin response, skin temperature, and micro-body movements. We analyzed data from 46 subjects whose physiological data was collected with Empatica E4 wristband while taking an Implicit Association Test (IAT). Our machine learning and statistical analysis show that implicit bias can be predicted from physiological signals with 76.1% accuracy. Our results also show that the EDA signal associated with skin response has the strongest correlation with racial bias and that there are significant differences between the values of EDA features for biased and unbiased participants.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Empatica. https://www.empatica.com
Project Implicit. https://implicit.harvard.edu/implicit/
Amodio, D.M.: The neuroscience of prejudice and stereotyping. Nat. Rev. Neurosci. 15(10), 670–682 (2014)
Amodio, D.M., Devine, P.G., Harmon-Jones, E.: Individual differences in the regulation of intergroup bias: the role of conflict monitoring and neural signals for control. J. Pers. Soc. Psychol. 94(1), 60 (2008)
Bizzego, A., Battisti, A., Gabrieli, G., Esposito, G., Furlanello, C.: Pyphysio: a physiological signal processing library for data science approaches in physiology. SoftwareX. 10, 100287 (2019)
Blascovich, J., Mendes, W.B., Hunter, S.B., Lickel, B., Kowai-Bell, N.: Perceiver threat in social interactions with stigmatized others. J. Pers. Soc. Psychol. 80(2), 253 (2001)
Di Lascio, E., Gashi, S., Santini, S.: Unobtrusive assessment of students’ emotional engagement during lectures using electrodermal activity sensors. Proc. ACM Interactive Mob. Wearable Ubiquitous Technol. 2(3), 1–21 (2018)
Graves, R.E., Cassisi, J.E., Penn, D.L.: Psychophysiological evaluation of stigma towards schizophrenia. Schizophr. Res. 76(2–3), 317–327 (2005)
Greco, A., Valenza, G., Lanata, A., Scilingo, E.P., Citi, L.: cvxEDA: a convex optimization approach to electrodermal activity processing. IEEE Trans. Biomed. Eng. 63(4), 797–804 (2015)
Greenwald, A.G., Poehlman, T.A., Uhlmann, E.L., Banaji, M.R.: Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. J. Pers. Soc. Psychol. 97(1), 17 (2009)
Greenwald, A.G., McGhee, D.E., Schwartz, J.L.: Measuring individual differences in implicit cognition: the implicit association test. J. Pers. Soc. Psychol. 74(6), 1464 (1998)
Mendes, W.B., Blascovich, J., Hunter, S.B., Lickel, B., Jost, J.T.: Threatened by the unexpected: physiological responses during social interactions with expectancy- violating partners. J. Pers. Soc. Psychol. 92(4), 698 (2007)
Unconscious Bias. https://diversity.ucsf.edu/resources/unconscious-bias
van Gent, P., Farah, H., van Nes, N., van Arem, B.: HeartPy: a novel heart rate algorithm for the analysis of noisy signals. Transport. Res. F: Traffic Psychol. Behav. 66, 368–378 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nikseresht, F., Yan, R., Lew, R., Liu, Y., Sebastian, R.M., Doryab, A. (2021). Detection of Racial Bias from Physiological Responses. In: Ahram, T.Z., Falcão, C.S. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2021. Lecture Notes in Networks and Systems, vol 275. Springer, Cham. https://doi.org/10.1007/978-3-030-80091-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-80091-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80090-1
Online ISBN: 978-3-030-80091-8
eBook Packages: EngineeringEngineering (R0)