With a background in Human–Computer Interaction and Behavioural Science, Chiara has dedicated her entire career to design. Her work explores how complex technologies shape human decision-making, grounded in years of hands-on experience across R&D, product innovation, and digital transformation.
Based mostly in London, UK, Chiara has collaborated with organisations such as the BBC, the University of Cambridge, and Intel, where she contributed to early research on autonomous vehicles, drones, and service ecosystems. At Elsevier, she led design and AI innovation, guiding a multidisciplinary team of designers and researchers in developing tools that support scientists and academics.
Her recent work bridges design and cognitive science, focusing on how AI can enhance human judgment through trust, explainability, and transparency. Chiara’s research investigates how people interpret AI-generated medical diagnoses, providing valuable insights into human–machine collaboration.
Upcoming Talks
27 May 2026
AI can generate synthetic users, actors and agents fast, but most teams still struggle to separate what is useful from what is risky, and how to defend these methods internally. In this hands-on workshop, Laura brings practical experimentation and Chiara adds a behavioural science lens to help teams evaluate, create and use synthetic representations responsibly.
You will compare approaches from synthetic personas grounded in first party research data, to scenario based synthetic actors and task oriented agents. You will pressure test outputs for bias, validity and misuse, and learn what “good” looks like in practice so you can make confident calls in your own context.
What you will learn
- The practical differences between synthetic personas, synthetic actors and task focused agents
- When each approach helps, and when it becomes misleading, across discovery, concept testing and higher risk domains
- How to structure messy research inputs to improve quality, traceability and repeatability
- Simple data science principles for building a scalable dataset for use with foundation models
- Prompt patterns, evaluation criteria and a lightweight decision framework you can use with cross functional partners
AI and transformation consultant