Advancing Gender Equality in Science: International Day of Women and Girls in Science – Part 2 (Author Q&A)

Achieving gender equality in science demands sustained, measurable action to dismantle structural barriers and create environments where women and girls can enter, progress and lead scientific careers.

To mark International Day for Women and Girls in Science, we spoke with a group of Kogan Page authors about why active encouragement and practical support for women in science matters, with a particular focus on neuroscience.

Our contributors examine the wider impact gender equity has on innovation, research quality and organizational performance, and the actionable commitments organizations need to be making. Moving beyond gestures, they address persistent challenges and misconceptions, including the historical exclusion of women from scientific research, inequitable workplace design and ongoing performance bias, that continue to limit opportunities for women across science-based industries.

Together, these perspectives offer practical strategies and real-world approaches for shaping more inclusive scientific environments where women can succeed and lead.

We are resuming this series with Kamales Lardi, author of Artificial Intelligence for Business.

Why does gender equity in science matter specifically for a field like neuroscience and how does this impact innovation, research outcomes and organizational success?

The brain is not a “one-size-fits-all” system. When we exclude women from neuroscience research, leadership and design decisions, we are actively distorting outcomes. Neuroscience informs everything from mental health treatments and pharmaceuticals to AI systems that emulate cognition, decision-making and behavior. If the underlying research is biased, those biases cascade downstream into technology, healthcare and business strategy.

A well-documented example from AI is the use of biased training data in machine-learning systems, particularly in facial recognition and automated decision-making tools. Several high-profile studies have shown that facial recognition systems trained predominantly on male and lighter-skinned datasets produced significantly higher error rates for women and people of color. This occurred because the data, design assumptions and testing environments reflected a narrow segment of the population. This is not a technical failure; it is a diversity failure, with real consequences ranging from misidentification to wrongful surveillance and exclusion from services.

From an organizational perspective, diversity across the entire innovation lifecycle, from problem definition and research design to development, testing and real-world use, leads to better risk management, more resilient products and stronger market relevance. Organizations that integrate diverse scientific perspectives innovate faster and more responsibly. That is a competitive advantage in a world where trust, ethics and explainability are becoming core business differentiators.

What are the most persistent myths or biases about women in neuroscience and what evidence-based arguments can business leaders deploy to debunk them internally and externally?

In my experience, there have been two persistent myths:

1. Myth 1: Women are “less suited” to fields like neuroscience or AI because they are supposedly less analytical or less interested in technical depth.
2. Myth 2: focusing on gender equity somehow lowers standards or prioritizes representation over competence.

Both claims are false and contradicted by decades of evidence. Women earn a significant proportion of degrees in neuroscience, psychology, biology and data-related fields globally. The core of the issue is systemic attrition. Women are more likely to leave scientific careers due to structural barriers such as biased funding decisions, lack of sponsorship, unequal caregiving expectations and exclusion from informal power networks.

From a performance standpoint, multiple studies show that diverse teams outperform homogeneous ones in complex problem-solving, innovation quality and decision-making accuracy. In research settings, gender-diverse teams produce work that is more frequently cited and more likely to challenge prevailing assumptions, a critical factor in scientific breakthroughs.

For business leaders, the most compelling argument is that bias is expensive. It leads to flawed products, reputational risk, missed markets and regulatory exposure. In an era of AI governance, ethics and stakeholder capitalism, organizations that fail to address diversity are strategically exposed. In my view, equity prioritizes removing invisible barriers so the best science can emerge.

Looking ahead, what actionable commitments, whether within your organization or the broader industry, could significantly accelerate gender equity over the next decade?

Progress over the next decade will require moving beyond performative statements to structural change.

• First, we need equity by design; intentionally embedding gender and intersectional analysis into research protocols, funding criteria, AI model development and product testing as a default.
• Second, organizations must shift from mentorship to sponsorship. Women in neuroscience and emerging tech need advocates with power who actively open doors to funding, leadership roles and high-impact projects.
• Third, transparency is critical, including publishing gender-disaggregated data on hiring, promotion, pay, research funding and publication outcomes.
• Fourth, interdisciplinary leadership matters. The future of neuroscience is deeply intertwined with exponential technologies such as AI, ethics and business strategy. Creating leadership pathways that value hybrid expertise will disproportionately benefit women and underrepresented groups who often operate at these intersections.
• And finally, we must redefine success. The next era of innovation will reward organizations that build technologies that are fair, inclusive and human-centered.

Gender equity is not a “women’s issue”, it is a systems-level innovation imperative.

We will keep this conversation on career and research equality for women and girls in science going throughout the week with key insights from our leading subject expert authors. Read part one and part three for the complete article series.