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

AAAI 2021 pp. 15755-15756

doi:10.1609/AAAI.V35I18.17874 /aaai/2021/baniecki2021aaai-responsible/

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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Cite

Text

Baniecki and Biecek. "Responsible Prediction Making of COVID-19 Mortality (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17874

Markdown

[Baniecki and Biecek. "Responsible Prediction Making of COVID-19 Mortality (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/baniecki2021aaai-responsible/) doi:10.1609/AAAI.V35I18.17874

BibTeX

@inproceedings{baniecki2021aaai-responsible,
  title     = {{Responsible Prediction Making of COVID-19 Mortality (Student Abstract)}},
  author    = {Baniecki, Hubert and Biecek, Przemyslaw},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15755-15756},
  doi       = {10.1609/AAAI.V35I18.17874},
  url       = {https://mlanthology.org/aaai/2021/baniecki2021aaai-responsible/}
}