Ayanna Howard
Organisations are deploying AI in hiring, healthcare, and operations before they understand whose assumptions are encoded in those systems. AI bias is not a data problem – it is a design problem, and it traces directly to the homogeneity of the teams building the tools. The second risk is less visible: research shows that humans routinely defer to automated systems in ways that go well beyond the reliability of those systems, including in high-stakes scenarios. Boards that have approved AI adoption have often not reckoned with either problem.
Ayanna Howard is a roboticist and Dean of Engineering at The Ohio State University whose research on AI bias and human overtrust – grounded in 250+ peer-reviewed publications and the book Sex, Race, and Robots – gives organisations the evidence base to govern AI adoption, not just adopt it.
Full Profile
Why organisations work with Ayanna Howard
- Her lab produced the first peer-reviewed algorithms quantifying how robot mistakes erode human trust – including in emergency scenarios where people followed automated systems away from clearly marked exits. That finding reframes the AI governance question from “is our system accurate?” to “are our people dangerously deferring to it?”
- Her book Sex, Race, and Robots (2021) makes a specific, falsifiable argument: AI bias originates with the designer, not the data. That argument changes where leadership teams assign accountability – and what they need to do before deployment, not after.
- She holds board seats at Autodesk and Motorola Solutions while running one of the largest engineering schools in the US. She understands AI decisions from lab to boardroom, and she speaks to both with equal authority.
- Zyrobotics, the company she founded from her Georgia Tech research, has commercialised AI for children with motor and cognitive challenges – she has direct product experience of translating research into regulated, real-world applications.
- As the 2021–2022 ACM Athena Lecturer and a Fellow of IEEE, AAAI, AAAS, and the National Academy of Inventors, her credentials carry weight in technical due diligence conversations as well as executive ones.
Biography highlights
- Dean of the College of Engineering, The Ohio State University – first woman in the role; manages a $360M annual budget, 12 departments, and 11,900+ students
- Senior Robotics Researcher and Deputy Manager, Office of the Chief Scientist, NASA Jet Propulsion Laboratory – contributed to Mars rover and arctic robotics programmes
- Named 2021–2022 ACM Athena Lecturer for fundamental contributions to accessible human-robotic systems and AI
- Author of Sex, Race, and Robots: How to Be Human in the Age of AI (Audible Studios/Brilliance Audio, 2021)
- Fellow of IEEE, AAAI, AAAS, and National Academy of Inventors; elected member of the American Academy of Arts and Sciences
- Independent director, Autodesk Inc. and Motorola Solutions Inc. boards
- Founder, Zyrobotics – Georgia Tech spin-off developing AI-powered educational and therapy products for children with special needs
- Named to Forbes America’s Top 50 Women in Tech and Business Insider’s 23 Most Powerful Women Engineers; featured in TIME, Vanity Fair, CNN, and NPR
Biography
When an AI system makes a flawed decision, most organisations blame the data. Ayanna Howard’s research makes a harder argument: bias in AI is a design failure, and it traces directly to who builds the system. Her audiobook Sex, Race, and Robots (2021) put that argument to a public audience. Her more than 250 peer-reviewed publications put it to the scientific record.
Her experimental work sharpens the stakes. Howard’s Human-Automation Systems Lab produced the first algorithms to quantify how robot mistakes erode human trust – including in emergency scenarios, where research subjects followed an automated guide away from clearly marked exits. The implication is direct: humans are overtrusting AI systems in ways that matter for consequential decisions in healthcare, hiring, and security.
Her authority on these questions reaches across three distinct domains. She spent twelve years at NASA’s Jet Propulsion Laboratory – as Senior Robotics Researcher and Deputy Manager in the Office of the Chief Scientist – developing autonomous systems for Mars and arctic terrain. She then led one of the US’s most productive human-robotics labs at Georgia Tech. She now serves as Dean of Engineering at The Ohio State University – its first female dean – overseeing twelve departments, a $360M budget, and more than 11,900 students.
Howard also holds board seats at Autodesk and Motorola Solutions, and founded Zyrobotics, which translates her research into AI-powered educational and therapy products for children with special needs. She is a Fellow of IEEE, AAAI, AAAS, and the National Academy of Inventors, and the 2021–2022 ACM Athena Lecturer. For boards and leadership teams asking serious questions about AI governance, she offers peer-reviewed research, institutional scale, and direct commercial accountability – in the same person.
Key speaking topics
- AI bias and responsible system design
- Human overtrust in automated systems
- Human-robot interaction and trust
- Assistive and healthcare robotics
- AI governance and ethics
- AI in business: risk and opportunity
- Engineering leadership and workforce diversity in STEM
Ideal for
- Corporate boards and C-suite leaders with AI governance responsibility
- Chief Technology Officers and Chief Digital Officers overseeing AI deployment
- R&D and innovation leadership in technology, healthcare, and manufacturing organisations
- Engineering and technology leadership development programmes
Audience outcomes
- A specific reframe of AI accountability: bias is a design responsibility, not a data problem – with implications for who in the organisation owns it
- Evidence-based understanding of how humans overtrust automated systems, and what that means for governance and deployment decisions
- Practical criteria for evaluating AI systems before they are placed in consequential roles
- Clearer understanding of where workforce diversity in technical teams changes AI outcomes – and how to make the case internally
- A more confident, evidence-grounded position for boards and leadership teams facing AI adoption decisions
Talks
Examines how AI and robotics are reshaping employment and social mobility, and what organisations and educators must do to build a capable, inclusive technical workforce.
Key takeaways:
- How to integrate STEM learning effectively across age groups, from early childhood through higher education
- How to build sustained interest and capability in AI-related fields among underrepresented communities
- What organisations can do to support the pipeline of technical talent the AI economy demands
Based on her widely viewed TED Talk, this presentation explores how embedding principles from human behaviour and neuroscience makes AI systems safer, more effective, and more aligned with social norms.
Key takeaways:
- How human intelligence and behaviour can inform the design of robotic and AI systems
- Why embedding rules of engagement is critical to safe and effective human–robot interaction
- The implications of more human-like AI systems for technology companies, businesses, and consumers
Explores the role of robotics and AI in healthcare, particularly in supporting children with special needs and elderly patients, drawing on Howard’s own research and commercial product development.
Key takeaways:
- How robotic systems can support physical therapy and rehabilitation for children with motor limitations
- The potential for AI-enabled robots to monitor health and provide emotional support
- Practical considerations for integrating assistive robots into healthcare settings
A facilitated session examining both the operational advantages and unintended consequences of AI deployment – helping leaders assess risks and prepare for responsible implementation.
Key takeaways:
- How AI tools can enhance data analysis and operational efficiency
- Key questions organisations should ask before implementing AI systems
- How to identify and mitigate risks including privacy, data misuse, and unintended discriminatory outcomes
Addresses how human bias becomes embedded in AI systems, and how excessive trust in automated decisions creates ethical and social consequences organisations are often unaware of.
Key takeaways:
- How bias shapes the development and outputs of AI algorithms
- The risks associated with over-reliance on automated systems in high-stakes decisions
- Strategies to mitigate or prevent bias in next-generation AI technologies