Publications

This paper critically examines how machine learning models humans in three case studies of well-being technologies, surfaces the assumptions that center normative bodies and marginalize complexities, and uses agential realism to propose more inclusive, posthuman-centered ML modeling approaches.

This essay analyzes how Generative AI is reshaping design practice and identifies five common “semantic stopsigns” — reductive framings that stall deeper inquiry. Drawing on two expert ACM‑conference workshops and interviews with design practitioners, it reframes these stopsigns into nuanced perspectives that help researchers and designers engage more productively with emerging GenAI tools.

In an era where artificial intelligence (AI) permeates every facet of our lives, this article presents and evaluates the positive AI design method aimed at supporting wellbeing in AI development.

This study benchmarks the emotional alignment of generative AI models by assessing their ability to express human emotions through imagery. Findings highlight variations in alignment by model, emotion type, and context, informing the design of emotionally expressive AI systems for wellbeing and reducing the risks of emotionally manipulative AI.