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A Multimodal Installation Exploring Gender Bias in Artificial Intelligence

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Universal Access in Human-Computer Interaction (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14020))

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Abstract

The “Blackbox AI” installation, developed as part of the EthicAI = LABS project, seeks to raise awareness about the social impact and ethical dimension of artificial intelligence (AI). This interdisciplinary installation explores various domains to bring to light the underrepresentation of women in STEM fields and the biases present in AI applications. The gender-swapped stories of women’s experiences of discrimination in the workplace, collected by survey, showcase common patterns and explore the effect of flipping the gender. The text-to-image generation experiment highlights a preference for men in STEM professions and the prevalence of social and racial biases. The facial recognition examples demonstrate the discriminatory effects of such technologies on women, while the image generation investigation poses questions about the influence of AI technology on beauty, with the aim to empower women by pointing out bias in AI tools. The ultimate goal of the project is to challenge visitors to rethink their role in creating our digital future and address the issue of gender bias in artificial intelligence.

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Acknowledgments

The authors gratefully acknowledge Goethe Institut for funding the BlackboxAI installation developed as part of the 2022 edition of the EthicAI=LAB project. In particular, we would like to express our thanks to our mentors Mihaela Constantinescu, Marinos Koutsomichalis, and Fatih Sinan Esen for their expert guidance and encouragement during the development of this work.

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Correspondence to Vivian Stamou .

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Dobreva, M., Rukavina, T., Stamou, V., Vidaki, A.N., Zacharopoulou, L. (2023). A Multimodal Installation Exploring Gender Bias in Artificial Intelligence. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14020. Springer, Cham. https://doi.org/10.1007/978-3-031-35681-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-35681-0_2

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