arXiv:2209.01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed. However, in a number of settings, we may be concerned that our training sample is biased in the sense that some groups (characterized by either observable or unobservable attributes) may be under- or over-represented relative to the general population; and in this setting empirical risk minimization over the training set may fail to yield
The proliferation of AI models across critical domains necessitates robust methods for handling biased training data to ensure fair and accurate decision-making.
Addressing sample bias in AI training is crucial for the fairness, reliability, and societal acceptance of AI systems, particularly as they are deployed in sensitive applications.
Improved methodologies for de-biasing AI training data could lead to more robust and equitable AI applications, reducing potential harms from biased outcomes.
- · AI developers
- · Underrepresented groups
- · Researchers in stat.ME and cs.LG
- · Ethical AI initiatives
- · Organizations relying on unmitigated biased AI
- · AI models without bias correction
- · Sectors with high data disparities
More accurate and fair AI models will emerge due to better bias correction methods.
Public trust in AI systems will increase as concerns about systemic bias are addressed.
Regulatory bodies may codify requirements for bias mitigation in AI, impacting development standards across industries.
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Read at arXiv cs.LG