arXiv:2506.11653v3 Announce Type: replace-cross Abstract: Dataset bias often leads deep learning models to exploit spurious correlations instead of task-relevant signals. We introduce the Standard Anti-Causal Model (SAM), a unifying causal framework that characterizes bias mechanisms and yields a conditional independence criterion for causal stability. Building on this theory, we propose DISCO$_m$ and sDISCO, efficient and scalable estimators of conditional distance correlation that enable independence regularization in gradient-based models. Across six diverse datasets, our methods consistent
The proliferation of deep learning across critical applications makes the issue of dataset bias and spurious correlations increasingly urgent, driving research into robust mitigation techniques.
Bias mitigation is crucial for the reliability, fairness, and trustworthiness of AI systems deployed in high-stakes environments, directly impacting their societal acceptance and economic utility.
New theoretical frameworks and computational tools like DISCO offer more effective ways to build causally stable AI models, potentially reducing the deployment risks associated with biased data.
- · AI developers
- · Model auditors and certifiers
- · Industries relying on AI for critical decision-making (e.g., healthcare, finance
- · Datasets providers focusing on diversity and representativeness
- · Developers ignoring bias mitigation
- · AI systems prone to spurious correlations
- · Organizations deploying biased AI without oversight
More robust and reliable deep learning models reduce instances of algorithmic failure and unfair outcomes.
Increased public trust in AI systems could accelerate their adoption across sensitive domains, but also necessitates stronger regulatory frameworks for bias.
A competitive advantage for entities that successfully integrate advanced bias mitigation techniques, leading to higher-performing and ethically sound AI products.
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