Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design
Flore Vancompernolle Vromman, Corentin Vande Kerckhove, Joël Gagnon, Camille Pelletier, Yannick Dufresne, Simon Coulombe · 2026
Read Explainable AI for Well-Being Prediction From Lifestyle Data: 2-Study Design by Flore Vancompernolle Vromman, Corentin Vande Kerckhove, Joël Gagnon, Camille Pelletier, Yannick Dufresne, Simon Coulombe (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13155431.txt.
Description
Flore Vancompernolle Vromman and five colleagues test an explainable AI tool that predicts well-being from lifestyle data, in a paper dated twenty twenty-six. Well-being drives public health, yet it rests on many shifting factors. Their first study built a simple, regularized model from a small set of lifestyle and setting factors. The second study tested how different kinds of explanation shaped user satisfaction with the tool's feedback.
More than twelve hundred people took part in the final sample. Any explanation beat none. Visual and interactive explanations scored highest, with text and number formats close behind. The authors argue that clear explanations build the trust such tools need to help people reflect on their lives.
Catalog record
| CREATOR | Flore Vancompernolle Vromman, Corentin Vande Kerckhove, Joël Gagnon, Camille Pelletier, Yannick Dufresne, Simon Coulombe |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 80 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |