Using Explainability to Help Children UnderstandGender Bias in AI. - Gaspar Isaac Melsión; Ilaria Torre 002; Eva Vidal; Iolanda Leite (IDC, 2021)

Boosting Robot Credibility and Challenging Gender Norms in Responding to Abusive Behaviour: A Case for Feminist Robots. - Katie Winkle; Gaspar Isaac Melsión; Donald McMillan; Iolanda Leite (HRI (Companion), 2021)

Leveraging Explainability for Comprehending Referring Expressions in the Real World. - Fethiye Irmak Dogan; Gaspar Isaac Melsión; Iolanda Leite (CoRR, 2021)

Norm-Breaking Responses to Sexist Abuse: A Cross-Cultural Human Robot Interaction Study. - Katie Winkle; Ryan Blake Jackson; Gaspar Isaac Melsión; Drazen Brscic; Iolanda Leite; Tom Williams 001 (HRI, 2022)

The potential of artificial intelligence for achieving healthy and sustainable societies. - Beril Sirmaçek; S. Gupta; Fermín Mallor; Hossein Azizpour; Yifang Ban; Hamidreza Eivazi; Heng Fang; F. Golzar; Iolanda Leite; Gaspar Isaac Melsión; Kevin Smith 001; Francesco Fuso Nerini; et al. (CoRR, 2022)

Leveraging explainability for understanding object descriptions in ambiguous 3D environments. - Fethiye Irmak Dogan; Gaspar Isaac Melsión; Iolanda Leite (Frontiers Robotics AI, 2022)

Human-Feedback Shield Synthesis for Perceived Safety in Deep Reinforcement Learning. - Daniel Marta; Christian Pek; Gaspar Isaac Melsión; Jana Tumova; Iolanda Leite (IEEE Robotics Autom. Lett., 2022)

What's at Stake? Robot explanations matter for high but not low-stake scenarios. - Gaspar Isaac Melsión; Rebecca Stower; Katie Winkle; Iolanda Leite (RO-MAN, 2023)

The Right (Wo)Man for the Job? Exploring the Role of Gender when Challenging Gender Stereotypes with a Social Robot. - Alessio Galatolo; Gaspar Isaac Melsión; Iolanda Leite; Katie Winkle (Int. J. Soc. Robotics, 2023)