Evaluation Sovereignty in Metadata-Driven Classification: A Multi-Track Framework for Weakly Supervised Information Systems
arXiv:2606.13436v1 Announce Type: new Abstract: Evaluation in machine learning is typically treated as a neutral measurement process. However, in operational information systems, evaluation outcomes are often conditioned by the processes used to generate labels. This paper does not seek to improve classification performance. Instead, it examines the validity of performance measurement under differing label-authority regimes. This issue is particularly relevant in large-scale metadata-driven systems, where labels are often incomplete, inconsistent, or weakly supervised. We introduce evaluation
The proliferation of large-scale AI systems, particularly those relying on vast and inconsistently labeled datasets, makes the validity of their foundational evaluation metrics a critical and timely concern.
A strategic reader must understand that perceived AI performance, especially in critical applications, can be skewed by the underlying label generation processes, impacting trust and investment decisions.
This research shifts the focus from solely improving AI classification performance to scrutinizing the integrity of the evaluation process itself, particularly in weakly supervised, metadata-driven systems.
- · Organizations prioritizing robust AI auditing
- · Developers of meta-evaluation frameworks
- · Ethical AI researchers
- · Regulatory bodies
- · AI systems with unchecked evaluation processes
- · Developers neglecting label validity
- · Companies relying on opaque performance metrics
Increased scrutiny on data labeling practices and their impact on reported AI performance will emerge.
New standards and best practices for 'evaluation sovereignty' in AI systems could be developed and enforced, potentially creating barriers to entry for some models.
Public and governmental trust in AI systems will increasingly hinge on transparent and verifiable evaluation methodologies, influencing adoption and regulatory landscapes.
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Read at arXiv cs.AI