Leaders in Ethical AI Research

Women in positions of influence within academic and research circles can push the boundaries of ethical AI research. Their work can illuminate the ways in which AI impacts different genders differently, leading to more humane and ethical AI systems.

Women in positions of influence within academic and research circles can push the boundaries of ethical AI research. Their work can illuminate the ways in which AI impacts different genders differently, leading to more humane and ethical AI systems.

Empowered by Artificial Intelligence and the women in tech community.
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Aditi Godbole
Senior Data Scientist at SAP

The sudden rise in AI has created many opportunities such as Personalization, improved customer and business experience, Predictive capabilities, and better decision-making models. However, this sudden rise has also brought some concerns about using and implementing ethically and sustainably across the industry.
One of the major challenges in Ethical AI is gender bias in AI, As AI systems are conceptualized and built they lack high-quality data that may not cover datasets for underrepresented communities such as Women. Based on research and studies done by Berkeley Hass Center for Equity & Gender, 44 percent of systems have shown gender bias
Women can bring diverse perspectives to AI development by assessing data that is misrepresented and can provide data sets that represent women and their experiences. Only 30 percent of women are currently working in AI as per the global gender gap report of 2023. We need more women researchers in this field who can bring industry-wide standards to bring fairness and transparent policies and address potential biases and their impacts on different groups of people. Women can also encourage female representation, participation, and engagement in all the stages from building policies, governance, and implementing solutions across the industry to ensuring AI systems are developed and used responsibly

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Maybel Lagumbay
Customer and Community Support Manager at WomenTech Network

Women in AI, data science, and research can advance ethical AI by examining how algorithms, datasets, and automated systems affect different populations. Their work can contribute to stronger approaches to fairness, transparency, bias detection, and accountability while highlighting issues that may otherwise be overlooked. Increasing women's participation in AI research also brings broader perspectives to the development of technical and industry standards for building trustworthy AI systems.

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Sisanda Skweyiya
Senior Business Analyst at Accenture

I believe women can play an important role in shaping ethical AI by bringing an ethical lens into the practical decisions made throughout the AI lifecycle.
In roles such as Business Analysis, product and digital transformation, women can ask questions that go beyond whether an AI solution works: Who could be affected? Can people understand the decision being made? What happens when the data is wrong? And who is accountable when something goes wrong?
Ethical AI shouldn't be an afterthought. It should be considered when defining the problem, gathering requirements, designing processes and measuring outcomes.
Sometimes ethical leadership is simply having the confidence to ask “Should we do this?” rather than only “Can we do this?”
That shift can help ensure AI remains innovative while still being responsible, transparent and centred around the people it is designed to serve.

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Jiahuan Li
Founder, Keyu Ecosystem at Ziran-hypnosoul

Ethical AI conversations often stay abstract — principles on a slide, not decisions in a product. As the founder of Keyu Ecosystem, I've had to make these calls concretely, without a research team or academic framework to lean on, just direct product decisions.

One example: building an anti-fraud tool (Vision Shield), I deliberately chose not to profile users by age, language ability, or demographic group — even though "elderly users" or "immigrants" would be the obvious, data-friendly targeting choice. My own case research showed victims span IT professionals, a retired bank director, and myself — vulnerability to manipulation is a universal cognitive pattern, not a demographic trait. Designing around that meant harder, more general detection logic, but a fairer product.

A second principle I hold firm to: never let the tool imply more certainty than it has. Every result carries a plain disclaimer — this is AI-assisted, not infallible, and real verification always goes through independent, official channels. In fraud-detection specifically, false confidence is itself a harm.

I don't have an academic research platform. What I have is the accountability of building alone, for real users, where every ethical shortcut would show up as someone's real financial loss. That constraint, I've found, produces its own rigor.

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