AEKTA SHAH, PhD

Dr. Aekta Shah is a researcher, thought leader, and advocate for responsible, ethical and equitable AI. Currently a Principal Researcher for Salesforce’s Responsible AI Team, she has dedicated her career to examining and mitigating biases within AI systems, championing a vision of technology that not only serves society broadly but also honors and includes the diverse experiences of women, Black, brown, Indigenous, and immigrant communities and other historically marginalized groups.

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Previously as a senior research leader for Google’s AI Products (Gemini), she spearheaded Trust + Safety initiatives to ensure that new AI tools were developed using an ethical framework that was equitable, inclusive, and actionable.

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Much of Dr. Shah’s work in AI is rooted in a deep understanding of the biases encoded within algorithms—what she describes as the “data distortions” that overlook or misinterpret those who don’t fit dominant data profiles. Through her research, Dr. Shah has exposed how skewed datasets and narrow design and engineering perspectives can lead to systems that fail or even harm marginalized groups. Her insights on algorithmic bias have informed the work of major tech companies, including IBM and Amazon, and her expertise continues to shape industry standards and practices that hold AI accountable to the people it impacts.

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With a PhD from Stanford University, Dr. Shah combines expertise in product design, machine learning, behavioral psychology, social justice, and human-centered, participatory design to advance responsible AI practices. Her research has been featured in outlets such as The Atlantic, The Nation, and The Root, and she has shared her insights at prominent venues including the White House, UC Berkeley, the Aspen Institute, and the United Nations.

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Dr. Shah’s passion lies in re-envisioning AI as an equitable tool that embodies humanity’s highest values. Her work is a call to action for building ethical technology that empowers and protects all people, especially those who have historically been silenced or overlooked.