Gender Bias and Challenges in AI and Data Governance

Gender is a complex and multifaceted concept that encompasses a wide range of identities and experiences. However, gender biases are often embedded in AI and data governance systems, leading to significant challenges for individuals who do not conform to traditional gender norms. A 2023 study by O’Connor explores the relationship between gender bias and AI, highlighting the claims of neutrality often made about such technologies [1]. These biases can manifest in a variety of ways, from the performance and output of AI models to the underrepresentation of women and gender diversity in data and AI positions [2][3]. It is crucial to understand the complexity of gender and its impact on AI and data governance to address these biases effectively.

Identifying gender biases in AI and data governance is essential to mitigating their impact on individuals and society. A 2022 study by Shrestha outlines the different themes and mitigation strategies that have been proposed in academic publications regarding gender biases in ML and AI algorithms [4]. Concerns regarding racial or gender bias in AI have arisen in applications as varied as hiring, policing, judicial sentencing, and financial decision-making [5]. To address these biases, it is necessary to take an intersectional, transdisciplinary and multistakeholder approach to ethical AI that considers questions around gender, race, ethnicity, socioeconomic status, and other factors [6].

Despite the challenges posed by gender biases in AI and data governance, there are opportunities to address these issues and promote greater equity and inclusion. A 2021 podcast by McKinsey and Citi explores how gender bias is reflected in AI and why it is important to consciously debias these systems [7]. Additionally, a 2023 study by Varsha asserts that AI biases and vulnerabilities experienced by people across industries lead to gender biases and racial disparities [8]. Data governance can play a critical role in overcoming these challenges by promoting transparency, accountability, and ethical decision-making [9]. By working to address gender biases in AI and data governance, we can create a more equitable and just society for all.

References

1. Gender bias perpetuation and mitigation in AI technologies. (n.d.) Retrieved August 11, 2023, from link.springer.com/article/10.1007/s00146–023–01675–4
2. What ChatGPT Tells Us about Gender: A Cautionary Tale …. (n.d.) Retrieved August 11, 2023, from www.mdpi.com/2076-0760/12/8/435
3. Bias in the machine: How can we address gender bias in AI?. (n.d.) Retrieved August 11, 2023, from www.raspberrypi.org
4. Exploring gender biases in ML and AI academic research …. (n.d.) Retrieved August 11, 2023, from www.ncbi.nlm.nih.gov/pmc/articles/PMC9593046/
5. Artificial intelligence and bias: Four key challenges. (n.d.) Retrieved August 11, 2023, from www.brookings.edu
6. Addressing Gender Bias to Achieve Ethical AI. (n.d.) Retrieved August 11, 2023, from theglobalobservatory.org
7. A conversation on artificial intelligence and gender bias. (n.d.) Retrieved August 11, 2023, from www.mckinsey.com
8. How can we manage biases in artificial intelligence systems. (n.d.) Retrieved August 11, 2023, from www.sciencedirect.com/science/article/pii/S2667096823000125
9. Webinar: Addressing the gender bias in artificial …. (n.d.) Retrieved August 11, 2023, from oecd.ai/en/webinar-gender-bias-artificial-intelligence-data