Breaking through the silicon wall: gendered opportunities and risks of new technologies

Abstract

Technology design and development has traditionally been characterized by a lack of attention to women’s priorities and activities; a lack of analysis of gendered impacts; and the influence of socio-cultural gender norms that position technology as a male pursuit. Advances are seen, but progress continues to be slow. For example, women are highly-represented in biology globally, but participation drops significantly in computational biology, and digital gender gaps in ownership and information and communication technology skills persist. The term “silicon wall” calls attention to the constraints faced by women and under-represented groups in the design, implementation, and appropriation of new technology. At the same time, the acceleration of technology-driven development poses new risks, in the form of AI and digital-based monetary systems, for example. These trends may reverse momentum in gender equality and empowerment through effects on labor force participation and economic opportunities, health and wellbeing, and (lack of) financial inclusion. Steps need to be taken to address gaps, constraints, and lack of opportunities that penalize women and underrepresented groups, in order to break through the silicon wall. This article builds on a forthcoming UNCTAD report to assess the intersection of digital technologies as they intersect with gender, diversity in the technology workplace, and development, in order to understand risks and opportunities for innovation and implementation of new technologies.

Keywords:

• Gender
• technology
• science
• development
• digital
• AI

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Diversity numbers vary also – at Google, 48.3 percent of the US workforce is white, 43.2 percent is Asian, 6.9 percent Latino, 5.3 percent Black, and 0.8 percent Native American (Google, 2022).

2 https://play.google.com/store/apps/details?id=com.multiverse.jarvis

3 Content in this section comes from Gendered Innovations http://genderedinnovations.stanford.edu/index.html

4 https://knowyourmeme.com/memes/sites/tay-ai. An alternate bot, Zo, was subsequently introduced - https://www.mobilescout.com/microsoft/news/n76914/zo-microsoft-new-ai-chatbot.html

5 Word embedding is an approach to represent text data as vectors and used in many machine learning and natural language processing tasks

6 http://genderedinnovations.stanford.edu/case-studies/facial.html#tabs-2

7 “Deepfake” technologies manipulate or generate visual and audio content to replace a person in an existing image or video with someone else's likeness. They have been used in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud (Kietzmann et al., 2020).

8 A first pilot of the G-App was carried out at an UNCTAD meeting. See https://www.womenatthetable.net/g-app for more information.

9 UNESCO defines researchers as “professionals engaged in the conception or creation of new knowledge. They conduct research and improve or develop concepts, theories, models, techniques instrumentation, software or operational methods, in the framework of R&D projects” (UNESCO, 2019b).