Responsible AI for Human Well-Being: From Wearables to Digital Healthcare

ABSTRACT

Artificial Intelligence is transforming how we understand and support human well-being. With mental health conditions now affecting one in four people globally, there is a pressing need for solutions that are effective, responsible, and accessible. By combining wearable technologies with responsible AI, we are moving toward a future where mental health can be monitored, understood, and improved in ways that are transparent, privacy-aware, and widely available.

My work is grounded in end-user and clinical studies within the fields of affective computing and digital healthcare, ensuring that research is connected to real-world needs. Drawing on more than ten years of research experience, I will share lessons learned about decoding signals of stress, mood, and cognitive states from everyday life, and how human-centered design ensures these tools truly serve people. I will highlight interdisciplinary projects that bridge well-being and technology, while showcasing the promise of explainable and trustworthy AI systems.

Looking ahead, I will discuss how these innovations could democratize access to mental healthcare and shape a new era of digital health—an era where AI predicts, explains, and adapts to individuals, enabling people to live healthier, longer, and more fulfilling lives.

BIOGRAPHY

Martin Gjoreski is a senior researcher and lecturer at the Università della Svizzera italiana (USI) in Lugano, Switzerland, and currently a visiting researcher in the Affective Computing group at the MIT Media Lab. His work focuses on responsible AI, wearable computing, and digital healthcare. He is the recipient of a highly competitive four-year fellowship from the Swiss National Science Foundation (SNSF), the Ambizione grant, which supports his independent research on explainable and privacy-preserving AI for mental health.

Martin’s PhD thesis was ranked among the top 1% in Slovenia, and he was later listed among the world’s top 2% scientists (single-year impact, 2021). He has been part of winning teams in several international machine learning competitions and collaborates with leading institutions including MIT, University of Cambridge, and the University of Queensland. He also serves as an Associate Editor at IMWUT (UbiComp) and as a Board Member of the Global SNSF Fellows Network.

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