---
title: "Trustworthy AI for Post-Stroke Care"
authors:
  - "Victor F. Lopes de Souza"
doi: "10.24963/ijcai.2026/945"
url: "https://ijcai.org/proceedings/2026/945"
pdf: "https://ijcai.org/proceedings/2026/0945.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "8333-8334"
track: "Doctoral Consortium"
keywords:
  - "AI Ethics, Trust, Fairnes: Explainability and interpretability"
  - "AI Ethics, Trust, Fairnes: Trustworthy AI"
  - "Humans and AI: Applications"
  - "Uncertainty in AI: Uncertainty representations"
---

# Trustworthy AI for Post-Stroke Care

Victor F. Lopes de Souza

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 8333-8334. DOI: [10.24963/ijcai.2026/945](https://doi.org/10.24963/ijcai.2026/945)

## Abstract

As AI systems are increasingly deployed in high-stakes settings, two concerns become particularly important: cautiousness (awareness of model limitations for risk-aware decisions) and explainability (providing human-understandable justifications). My PhD research develops methods that enable both, from theoretical and application perspectives. On the theoretical side, I formalize cautious and interpretable models for multiple uncertainty frameworks, with algorithms for explanation and preference elicitation under uncertainty. On the application side, I implement these methods in post-stroke rehabilitation, designing AI systems that analyze multimodal clinical data to support individualized rehabilitation planning.