Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices
arXiv:2606.07068v1 Announce Type: new Abstract: Background: Since 1990 many feature selection methods have been proposed across heterogeneous applications. To validate the usefulness of a new method, it needs to be compared against at least one baseline method from the existing literature on a feature selection task using at least one dataset. Recent developments in tabular Deep Learning (DL) and data valuation in Machine Learning (ML) suggest that the evaluation of new methods, algorithms, and models may be consciously or unconsciously biased. We hypothesise that a similar trend exists in fea
This paper highlights emerging concerns around potential biases in AI/ML model evaluation, particularly as AI research accelerates and its real-world applications become more ubiquitous.
Understanding and mitigating bias in feature selection evaluation is critical for developing robust, fair, and reliable AI systems, which impacts their trustworthiness and widespread adoption.
The focus on a deeper meta-analysis of evaluation methodologies suggests a growing maturity in AI research, moving beyond just new model development to scrutinizing foundational assumptions.
- · AI ethics researchers
- · Organizations developing fair AI models
- · Regulatory bodies in AI
- · Developers of biased AI models
- · Systems relying on poorly vetted feature selection methods
Increased scrutiny of existing AI evaluation techniques and a push for more rigorous, bias-aware methodologies.
Development of new tools and standards for identifying and mitigating bias in AI model development lifecycle.
Improved public and institutional trust in AI systems due to enhanced transparency and fairness.
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