Responsible Prediction Making of COVID-19 Mortality (Student Abstract)

Authors

  • Hubert Baniecki Faculty of Mathematics and Information Science, Warsaw University of Technology
  • Przemyslaw Biecek Faculty of Mathematics and Information Science, Warsaw University of Technology Samsung Research and Development Institute, Poland

Keywords:

Responsible Artificial Intelligence, Explainable Artificial Intelligence, COVID-19, Decision Making

Abstract

For high-stakes prediction making, the Responsible Artificial Intelligence (RAI) is more important than ever. It builds upon Explainable Artificial Intelligence (XAI) to advance the efforts in providing fairness, model explainability, and accountability to the AI systems. During the literature review of COVID-19 related prognosis and diagnosis, we found out that most of the predictive models are not faithful to the RAI principles, which can lead to biassed results and wrong reasoning. To solve this problem, we show how novel XAI techniques boost transparency, reproducibility and quality of models.

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Published

2021-05-18

How to Cite

Baniecki, H., & Biecek, P. (2021). Responsible Prediction Making of COVID-19 Mortality (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15755-15756. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17874

Issue

Section

AAAI Student Abstract and Poster Program