Definitions
Artificial intelligence is the project of creating an artificial agent that has the same kind of intelligence as humans. It is found in many different aspects of our daily lives, e.g., virtual assistants, search engines, games, and health care. Oppression is a particular type of harm – it is group-based and systemic. People face oppressive harms as members of an oppressed social group (e.g., gender, race, socioeconomic class, sexual orientation, or (dis)ability). Through technologies such as virtual assistants, search engines, robots, and algorithms, artificial intelligence is contributing to gender-based oppression.
Introduction
Artificial intelligence (AI) has had a huge impact on societies throughout the globe in just a few short years since going mainstream. The technology has given us new powers and abilities to understand our world, and it is being heralded by some as ushering in a better future (for discussion see Russell and Norvig 2019; Kaku 2018). Nevertheless,...
This is a preview of subscription content, log in via an institution to check access.
References
Adams A (1998) Artificial knowing. Routledge, New York
Ahn S, Costigan A (2019) How AI reinforces gender stereotypes: trend brief. Catalyst, 5 December 2019. Retrieved from: https://www.catalyst.org/research/ai-gender-stereotypes/
Ali M, Flaounas I, De Bie T, Mosdell N, Lewis J, Cristianini N (2010) Automating news content analysis: an application to gender bias and readability. In: Proceedings of the first workshop on applications of pattern analysis, pp 36–43
Andreallo F (2019) Robots with benefits: how sexbots are marketed as companions. The Conversation, 13 November 2019. Retrieved from: https://theconversation.com/robots-with-benefits-how-sexbots-are-marketed-as-companions-126262
Arrieta AB, DÃaz-RodrÃguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, GarcÃa S, Gil-López S, Molina D, Benjamins R, Chatila R, Herrera F (2019) Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Springer. Retrieved from: https://arxiv.org/abs/1910.10045
Bailey A (1998) Privilege: expanding on Marilyn Frye’s ‘Oppression’. J Soc Philos 29:104–119
Bame Y (2017) 1 in 4 men would consider having sex with a robot. YouGov, 2 October 2017. Retrieved from: https://today.yougov.com/topics/lifestyle/articles-reports/2017/10/02/1-4-men-would-consider-having-sex-robot
Bogost I (2018) Sorry, Alexa is not a feminist. The Atlantic, 24 January 2018. Retrieved from: https://www.theatlantic.com/technology/archive/2018/01/sorry-alexa-is-not-a-feminist/551291/
Bosse T, Broekens J, Dias J, van der Zwaan J (2014) Emotion modeling: towards pragmatic computational models of affective processes. Springer, London, UK
Bryson J (2008) Robots should be slaves. In: Wilks Y (ed) Close engagements with artificial companions. John Benjamins Publishing, Amsterdam, pp 63–74
Buolamwini J (2019) Artificial intelligence has a problem with gender and racial bias. Here’s how to solve it. Time Magazine, 7 February 2019. Retrieved from: https://time.com/5520558/artificial-intelligence-racial-gender-bias/
Buolamwini J, Gebru T (2018) Gender shades: intersectional accuracy disparities in commercial gender classification. In: Conference on fairness, accountability and transparency, pp 77–91
Chakriswaran P, Vincent DR, Srinivasa K, Sharma V, Chang C-Y, Reina DG (2019) Emotion AI-driven sentiment analysis: a survey, future research directions, and open issues. Appl Sci 9:6462
Cudd AE (1994) Oppression by choice. J Soc Philos 25.1:22–44
Dean W (2016) Algorithms and the mathematical foundations of computer science. In: Horsten L, Welch P (eds) Gödels disjunction: the scope and limits of mathematical knowledge. Oxford University Press, Oxford
Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. Springer. Retrieved from: https://arxiv.org/abs/1702.08608
Feast J (2019) 4 ways to address gender bias in AI. Harv Bus Rev. Retrieved from: https://hbr.org/2019/11/4-ways-to-address-gender-bias-in-ai%20/
Fessler L (2018) Amazon’s Alexa is now a feminist, and she’s sorry if that upsets you. Quartz. Retrieved from: https://work.qz.com/1180607/
Foster J (2018) What did we get ourselves into? Medium. Retrieved from: https://medium.com/microsoft-design/what-did-we-get-ourselves-into-36ddae39e69b
Frank L, Nyholm S (2017) Robot sex and consent: is consent to sex between a robot and a human conceivable, possible, and desirable? Artif Intell Law 25:305–323
Frye M (1983) The politics of reality. The Crossing Press, Freedom
Ghosh P (2020) Sex robots may cause psychological damage. BBC, 15 February 2020. Retrieved from: https://www.bbc.co.uk/news/science-environment-51330261
Gürdeniz E, St-Onge E (2019) AI has alarming power to spread gender bias. Here are four ways to combat it. Brink–The Edge of Risk. Retrieved from: https://www.brinknews.com/ai-has-alarming-power-to-spread-gender-bias-here-are-four-ways-to-combat-it/
Guynn J (2019) The problem with AI? Study says it’s too white and male, calls for more women, minorities. IMDiversity, 16 April 2019. Retrieved from: https://imdiversity.com/diversity-news/the-problem-with-ai-study-says-its-too-white-and-male-calls-for-more-women-minorities/
Hay C (2013) Kantianism, liberalism, and feminism. Palgrave Macmillan, London, UK
Hempel J (2015) Siri and Cortana sound like ladies because of sexism. Wired. Retrieved from: https://www.wired.com/2015/10/why-siri-cortana-voice-interfaces-sound-female-sexism/
Hill K (2020) Wrongly accused by an algorithm. New York Times, 24 June 2020. Retrieved from: https://www.nytimes.com/2020/06/24/technology/facial-recognition-arrest.html
Hoke N (2020) 5 Empathetic design principles for successful human-agent interaction. Intuition Robotics Blog, 23 April 2020. Retrieved from: https://blog.intuitionrobotics.com/5-empathetic-design-principles-for-successful-human-agent-interaction
Hudlicka E (2014) From habits to standards: towards systematic design of emotion models and affective architectures. In: Bosse T, Broekens J, Dias J, van der Zwaan J (eds) Emotion modeling: towards pragmatic computational models of affective processes. Springer, London, UK
Kaku M (2018) The future of humanity: terraforming mars, interstellar travel, immortality, and our destiny beyond earth. Doubleday Books, London, UK
Kerr AD (2020) Emotions, rationality, and gender. In: Filho WL, Azul AM, Brandli L, Özuyer PG, Wall T (eds) Encyclopedia of the UN sustainable development goals. Springer
Kinsella B (2019) Google assistant should be available on 1 billion devices this month – 10× more than Alexa. Voicebot.ai 7 January 2019. Retrieved from: https://voicebot.ai/2019/01/07/google-assistant-to-be-available-on-1-billion-devices-this-month-10x-more-than-alexa/
Lai CK, Hoffman KM, Nosek BA (2013) Reducing implicit prejudice. Soc Personal Psychol Compass 7:315–330
Leavy S (2018) Gender bias in artificial intelligence: the need for diversity and gender theory in machine learning. In: ACM/IEEE 1st international workshop on gender equality in software engineering
Lee BY (2018) In case you are wondering, sex with robots may not be healthy. Forbes, 5 June 2018. Retrieved from: https://www.forbes.com/sites/brucelee/2018/06/05/in-case-you-are-wondering-sex-with-robots-may-not-be-healthy/#4990f5521f6b
Mitchell W, Ho CC, Patel H, MacDorman KF (2011) Does social desirability bias favour humans? Explicit-implicit evaluations of synthesized speech support a new HCI model of impression management. Comput Hum Behav 27(1):402–412
Molnar C (2019) Interpretable machine learning: a guide for making black box models explainable. Lean Publishing, Victoria, Canada
Noble SU (2018) Algorithms of oppression: how search engines reinforce racism. New York University Press, New York
Romano B (2018) Hey Alexa, what’s in a name? Actually, let’s call you Amazon. Xconomy. Retrieved from: https://xconomy.com/national/2018/01/23/hey-alexa-whats-in-a-name-actually-lets-call-you-amazon/
Russell S, Norvig P (2019) Artificial intelligence: a modern approach. Pearson, Essex
Samuel S (2019) Women suffer needless pain because almost everything is designed for men: why women are 50 percent more likely to be misdiagnosed after a heart attack and 17 percent more likely to die in a car crash. Vox. Retrieved from: https://www.vox.com/future-perfect/2019/4/17/18308466/invisible-women-pain-gender-data-gap-caroline-criado-perez
Sánchez-Escribano MG (2018) Engineering computational emotion – a reference model for emotion in artificial systems. Springer, Cham, Switzerland
Shen FX (2019) Sex robots are here, but laws aren’t keeping up with the ethical and privacy issues they raise. The Conversation, 12 February 2019. Retrieved from: https://theconversation.com/sex-robots-are-here-but-laws-arent-keeping-up-with-the-ethical-and-privacy-issues-they-raise-109852
Simonite T (2018) AI is the future–but where are the women? WIRED, 17 August 2018. Retrieved from: https://www.wired.com/story/artificial-intelligence-researchers-gender-imbalance/
Singer N, Metz C (2019) Many facial-recognition systems are biased, says U.S. study. New York Times, 19 December 2019. Retrieved from: https://www.nytimes.com/2019/12/19/technology/facial-recognition-bias.html
Smith J, Farina L (2018) A puzzle about emotional robots: could a robot truly experience human emotions? iai News 69, 17 October 2018. Retrieved from: https://iai.tv/articles/a-puzzle-about-emotional-robots-auid-1157?_ga=2.23632122.1579874112.1539950664-1317334097.1539950664
Thilmany J (2007) The emotional robot: cognitive computing and the quest for artificial intelligence. Eur Mol Biol Organ Rep 8(11):992–994
UN Women (2013) UN women ad series reveals wide-spread sexism. Retrieved from: https://www.unwomen.org
Valentino-DeVries J (2020) How the police use facial recognition, and where it falls short. New York Times, 12 January 2020. Retrieved from: https://www.nytimes.com/2020/01/12/technology/facial-recognition-police.html
Vincent J (2020) Hey Siri, I’m getting pulled over’ shortcut makes it easy to record police. The Verge. Retrieved from: https://www.theverge.com/2020/6/17/21293996/siri-iphone-shortcut-pulled-over-police-starts-recording-video
West M, Kraut R, Chew HE (2019) I’d blush if I could – closing gender divides in digital skills through education. UNESCO. Retrieved from: https://en.unesco.org/Id-blush-if-I-could
Wilson M (2017) Can robots really be companions to elderly people? Fast Company. Retrieved from: https://www.fastcompany.com/3067150/can-robots-really-be-companions-to-elderly-people
Yee BY (2018) In case you are wondering, sex with robots may not be healthy. Forbes, 6 June 2018. Retrieved from: https://www.forbes.com/sites/brucelee/2018/06/05/in-case-you-are-wondering-sex-with-robots-may-not-be-healthy/#6a53da561f6b
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this entry
Cite this entry
Kerr, A.D. (2020). Artificial Intelligence, Gender, and Oppression. In: Leal Filho, W., Azul, A., Brandli, L., Lange Salvia, A., Wall, T. (eds) Gender Equality. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. https://doi.org/10.1007/978-3-319-70060-1_107-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-70060-1_107-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70060-1
Online ISBN: 978-3-319-70060-1
eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences