Responsible domestic robotics: exploring ethical implications of robots in the home. - Lachlan Urquhart; Dominic Reedman-Flint; Natalie Leesakul (J. Inf. Commun. Ethics Soc., 2019)

Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning Architecture. - Jimiama Mafeni Mase; Natalie Leesakul; Fan Yang; Grazziela P. Figueredo; Mercedes Torres Torres (CoRR, 2021)

A Blockchain-Based Architecture to Manage User Privacy Preferences on Smart Shared Spaces Privately. - Charles V. Neu; Joel Gibson; Roben Castagna Lunardi; Natalie Leesakul; Charles Morisset (DPM/CBT@ESORICS, 2022)

Position Paper: The role of law in achieving privacy and security measures in smart buildings from the GDPR context. - Natalie Leesakul; Charles Morisset (EuroS&P Workshops, 2023)

Facial identity protection using deep learning technologies: an application in affective computing. - Jimiama M. M. Mase; Natalie Leesakul; Grazziela P. Figueredo; Mercedes Torres Torres (AI Ethics, 2023)

Not the Law's First Rodeo: Towards regulating trustworthy collaborative industrial embodied autonomous systems. - Natalie Leesakul; Jérémie Clos; Richard Hyde (TAS, 2024)

A Survey of Lay People's Willingness to Generate Legal Advice using Large Language Models (LLMs). - Tina Seabrooke; Eike Schneiders; Liz Dowthwaite; Joshua Krook; Natalie Leesakul; Jérémie Clos; Horia A. Maior; Joel E. Fischer (TAS, 2024)

Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM. - Eike Schneiders; Tina Seabrooke; Joshua Krook; Richard Hyde; Natalie Leesakul; Jérémie Clos; Joel E. Fischer (CHI, 2025)