Bridging the AI Gender Gap: Leadership, Bias, and the Future

The panel discussion on "Reimagining AI through the lens of women leaders — driving innovation, equity, and impact for all." The coveted panelists who enthusiastically participated included, Rajeshwari Krishnamurthy, Professor, Great Lakes Institute of Management, Mitali Nikore, Economist and Public Policy specialist, Nikore Associates, and Sugandha Srivastava, Senior Analyst, CMR. This session was moderated by Minu Sirsalewala, Executive Editor, CyberMedia.

Artificial Intelligence (AI) is deeply embedded in our lives, shaping crucial decisions in finance, employment, and governance. However, the gender imbalance in AI leadership and policy-making raises pressing concerns about biases that could have long-term consequences. At the Dataquest Digital Leadership Conclave, industry experts and thought leaders discussed the role of women in AI, existing biases, and the steps needed to create a more equitable AI ecosystem.

The Gender Gap in AI: A Stark Reality

Despite AI’s growing influence, women remain significantly underrepresented in the field. Only 22% of AI professionals globally are women, with the numbers dropping to 14% in research and below 10% in AI policy advisory roles in India. This disparity affects the way AI systems are developed, trained, and implemented, leading to biases that mirror societal inequalities. Many datasets used to train AI models inherently carry gender biases due to historical data, exacerbating systemic discrimination in AI-driven decision-making.

Addressing AI Bias at the Root Level

Bias in AI is a growing concern, with real-world implications in hiring, financial credit allocation, and facial recognition systems. Panelists emphasized the need to ensure vernacular representation and inclusivity in AI datasets, particularly in underrepresented sectors such as healthcare and maternity care. The discussion highlighted that biases in AI often stem from the lack of diverse representation in dataset creation and policy-making, reinforcing the urgency for more women in leadership roles.

The Role of Women in AI Policy and Strategy

Interestingly, India’s national AI strategy was written by women, yet AI-driven biases persist. The discussion shed light on the fact that while policies might be inclusive on paper, implementation remains a challenge due to ingrained biases in data and decision-making. Women in AI leadership roles are essential to ensuring policies address these biases and actively work toward mitigating them.

AI’s Impact on the Care Economy and Workforce Participation

A critical area where AI could bring about significant change is the care economy. Women disproportionately spend more time on unpaid care work, limiting their participation in the workforce. AI applications could help bridge this gap by providing solutions that optimize time and improve efficiency in caregiving. Additionally, AI has the potential to enhance physical labor efficiency, which could benefit women in traditionally male-dominated industries.

Ethical AI and the Need for Responsible Leadership

The panel also underscored the importance of ethics in AI. Women are known for prioritizing ethical decision-making, making their presence in AI leadership crucial for ensuring accountability and responsible AI use. As AI’s role expands, ethical considerations, transparency, and accountability should be at the forefront of policy discussions.

Bridging the Digital Divide for Women in Rural India

The digital divide remains a major hurdle in AI inclusivity, particularly for rural women in India. A staggering 54% of women in rural areas have never used the internet, making access to AI-driven opportunities nearly impossible. AI policies must account for this gap by ensuring digital literacy initiatives and technological accessibility for rural women, enabling them to benefit from AI-driven advancements in agriculture and other sectors.

Women in AI Academia and Research

The role of academia in fostering AI literacy and inclusivity was another key focus area. Women faculty members are often more willing to adopt new technologies, yet gender biases persist even in research. AI maturity models, such as those developed by IBM Watson, could be instrumental in assessing and bridging gender disparities in AI research and education.

A Call to Action: Bold Moves for a Gender-Inclusive AI Future

As a concluding note, panelists called for concrete actions to accelerate gender inclusion in AI. Some proposed initiatives included establishing AI councils led by women, pushing international organizations to commit to AI diversity pledges, and ensuring that AI companies have at least 20% female employees and 10% women in leadership roles. Organizations like DeepSeek have already demonstrated that gender diversity in AI leadership is achievable, setting an example for others to follow.

The discussion at the Dataquest Digital Leadership Conclave reinforced that AI has the potential to be an equalizer, but only if intentional efforts are made to eliminate biases, increase female representation, and bridge the digital divide. With collective action from policymakers, academia, and industry leaders, AI can truly become a tool for equitable progress.

--Written By Bharti Trehan