Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget
arXiv:2602.17894v2 Announce Type: replace-cross Abstract: Data collection is a critical component of modern statistical and machine learning pipelines, particularly when data must be gathered from multiple heterogeneous sources to study a target population of interest. In many use cases, such as medical studies or political polling, different sources incur different sampling costs. Observations often have associated group identities - for example, health markers, demographics, or political affiliations - and the relative composition of these groups may differ substantially, both among the sour
The proliferation of AI and advanced ML systems has highlighted the inherent biases and cost inefficiencies in current data pipelines, making robust data collection methods critically important.
Optimizing data collection from heterogeneous, biased, and costly sources is crucial for developing fair, accurate, and cost-effective AI systems, impacting virtually all AI applications.
This research provides theoretical foundations and practical methods for managing data collection budgets while mitigating bias, potentially leading to more reliable AI models and more efficient resource allocation in data-intensive fields.
- · AI researchers and developers
- · Organizations with substantial data collection costs
- · Sectors reliant on sensitive data (e.g., healthcare, polling)
- · Companies building data management platforms
- · Organizations relying on brute-force, unoptimized data collection
- · AI models prone to significant bias due to poor data sourcing
Improved efficiency and accuracy in AI model training through better data selection.
Reduced operational costs for data-intensive projects and potentially more equitable AI outcomes.
Enhanced trust in AI systems due to transparent and robust data provenance and bias mitigation strategies.
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Read at arXiv cs.LG