Conference paper Open Access
The need of standardised metadata to encode causal relationships: Towards safer data-driven machine learning biological solutions
Beatriz Garcia Santa Cruz;
Carlos Vega;
Frank Hertel
In this paper, we discuss the importance of considering causal relations in the development of machine learning solutions to prevent factors hampering the robustness and generalisation capacity of the models, such as induced biases. This issue often arises when the algorithm decision is affected by confounding factors. In this work, we argue that the integration of causal relationships can identify potential confounders. We call for standardised meta-information practices as a crucial step for proper machine learning solutions development, validation, and data sharing. Such practices include detailing the dataset generation process, aiming for automatic integration of causal relationships.
| Name | Size | |
|---|---|---|
|
Authors__guidelines_for_CIBB_short_papers (1).pdf
md5:e79ab151c2eae88041ddf91b58ca231d |
360.3 kB | Download |
| All versions | This version | |
|---|---|---|
| Views | 77 | 77 |
| Downloads | 43 | 43 |
| Data volume | 15.5 MB | 15.5 MB |
| Unique views | 63 | 63 |
| Unique downloads | 33 | 33 |
- Publication date:
- November 16, 2021
- DOI:
-
- Keyword(s):
- confounders causality metadata machine learning systems biology
- License (for files):
- Creative Commons Attribution 4.0 International