Sara Hooker is a prominent researcher in the field of artificial intelligence, known for her work at the intersection of deep learning, interpretability, and fairness. As of 2025, she serves as the head of Cohere for AI, the research division of the AI company Cohere, where she leads efforts to advance open-source AI research and promote responsible development practices.
Hooker is widely recognized for her contributions to model interpretability, particularly her work on understanding how neural networks learn and make decisions. She has published influential papers on topics such as feature attribution, the impact of training data on model behavior, and the challenges of evaluating large-scale AI models. Her work has been instrumental in highlighting the importance of understanding not just what AI models do, but why they do it, a concern that has become central to the field.
Before joining Cohere, Hooker completed her undergraduate studies at the University of Toronto, where she first became interested in artificial intelligence. She later pursued graduate studies at the same institution, earning a master's degree and a PhD in computer science. During her doctoral research, she focused on machine learning and neural networks, laying the groundwork for her later work on interpretability.
After completing her PhD, Hooker worked as a research scientist at Google Brain, where she contributed to projects involving large-scale machine learning models. Her time at Google allowed her to engage with some of the most advanced AI systems in the world and deepened her interest in understanding their inner workings.
Throughout her career, Hooker has been a vocal advocate for the importance of transparency and accountability in AI. She has argued that the AI community often relies on benchmarks and metrics that can be misleading, and she has proposed more rigorous evaluation methods to ensure that AI systems are robust and reliable. Her research has also explored the ethical implications of AI, including issues of bias and fairness, and she has worked to develop techniques that mitigate these problems.
In addition to her technical work, Hooker is known for her commitment to increasing diversity in technology. She has spoken openly about the challenges faced by women and underrepresented groups in AI and has worked to create more inclusive environments in research settings. She has also been involved in initiatives to bring AI education to underserved communities.
Hooker continues to be an active researcher and a prominent figure in the AI landscape. Her work exemplifies the growing recognition that building powerful AI systems requires not only technical expertise but also a deep understanding of their societal impact.