Summary

The gender digital divide creates a data gap that is reflected in the gender bias in AI. Who creates AI and what biases are built into AI data, can perpetuate, widen, or reduce gender equality gaps.

Thought Prompt:

Have you ever been frustrated with Siri, Alexa, or Google not understanding your instructions or blatantly ignoring your voice frequently? Do you use AI technology to process job applicants and notice a pattern in the applicant pools generated? Did you know that gender and racial biases have been found in AI programming and the problem is only growing each day? 😱

That’s right, our advanced technology is not immune to biased and discriminatory algorithms. This raises important questions about AI’s ethics and impact on society. A study by the Berkeley Haas Center for Equity, Gender and Leadership analyzed 133 AI systems across different industries and found that about 44 percent showed gender biases, and 25 percent exhibited both gender and racial biases.

What?! AI technology considers gender?!

The Problem:

AI exhibits gender bias because of the data it had been trained on, specifically, “word embedding”. This method encodes words in machine learning to convey their meanings and associations with other words, enabling machines to understand and interact with human language. If the AI is trained on data that associates women and men with different and specific skills or interests, it will generate content reflecting that bias.

🤖 Who creates AI and what biases are built into AI data (or not), can perpetuate, widen, or reduce gender equality gaps.

According to the Global Gender Gap Report of 2024, the gender disparity is over-emphasized in the technology workforce with women making up only 30% of the AI and big data workforce, 31% of the programming jobs, and 31% of network and cybersecurity positions. Even though more women are graduating and joining STEM careers today than ever before, they are consistently kept in entry-level positions and less likely to hold leadership positions that could influence the tone of AI algorithms. Meaning, that AI is mostly developed by men and trained on datasets that are primarily based on men, which means AI is designed to work best FOR men. Think of how this affects recruitment and hiring, especially when most companies use some form of AI technology to filter applicants and find potential candidates. Think about how this bias provides inaccurate answers to patients using recent medical or health provider apps based on AI processing technology.

The Future:

Removing gender bias in AI starts with prioritizing gender equality as a goal, as AI systems are conceptualized and built. This includes assessing data for misrepresentation, providing data that is representative of diverse gender and racial experiences, and reshaping the teams developing AI to make them more diverse and inclusive. There is a crucial necessity to incorporate a wide range of expertise in AI development, including gender-specific knowledge. This will enhance the performance of machine learning systems, contributing to the pursuit of a more equitable and sustainable world. 

💪 The AI field needs more women, and that requires enabling and increasing girls’ and women’s access to and leadership in STEM and ICT education and careers.

In the fast-evolving AI sector, the absence of diverse racial and gender perspectives, data, and decision-making could prolong significant inequalities in the future. This oversight could potentially lead to a decline in service quality and biased decision-making across various sectors, including jobs, credit, and healthcare. UN Women position paper on the GDC provides concrete recommendations to harness the speed, scale, and scope of digital transformation for the empowerment of women and girls in all their diversity, and to trigger transformations that set countries on paths to an equitable digital future for all.

💭 Let’s start a conversation and discuss how we can address this issue and create a more inclusive future for all.

#AI #ArtificialIntelligence #GenderEquality #GenderDisparity #EqualityInAI #InclusiveTechnology #EthicsInTech #BreakingBarriers #TechTalk #DiversityandInclusion #HigherEducation #ResearchTopics

 

Sources:

L. Nicoletti and D. Bass. 9 June 2023, “Humans are biased. Generative AI is even worse.” Bloomberg. https://www.bloomberg.com/graphics/2023-generative-ai-bias/

Smith, G., & Rustagi, I. (2021). When Good Algorithms Go Sexist: Why and How to Advance AI Gender Equity. Stanford Social Innovation Review. https://doi.org/10.48558/A179-B138

UN Women Headquarters. (2024). Placing gender equality at the Heart of the Global Digital Compact: Taking forward the recommendations of the sixty-seventh session of the Commission on the Status of Women. https://www.unwomen.org/en/digital-library/publications/2024/03/placing-gender-equality-at-the-heart-of-the-global-digital-compact

World Economic Forum (WEF). 11 June 2024. The Global Gender Gap Report 2024. https://www.weforum.org/publications/global-gender-gap-report-2024