When Do Data-Driven Systems Exhibit the Capability to Infer?
Quick Answer
The European AI Act introduces obligations for AI systems, particularly regarding inference capabilities, which remain undefined, creating ambiguity for data-driven systems like credit scoring.
Quick Take
This study proposes a grading framework for inference capability and highlights the need for regulatory clarity, particularly in workflows involving human experts.
Key Points
- The AI Act lacks a clear definition of inference, affecting high-risk AI systems.
- Credit scoring systems are highlighted as ambiguous under the AI Act's guidelines.
- A framework for grading inference capability was developed based on statistical learning theory.
- Human expert involvement significantly influences the inference capability in AI workflows.
- Two realistic credit scoring workflows were analyzed to illustrate inference occurrences.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 11769v1 Announce Type: new Abstract: The European AI Act is the first comprehensive regulation of artificial intelligence (AI), setting out extensive obligations, particularly for so-called high-risk and general-purpose AI systems. A key distinguishing feature of AI systems under the AI Act is the capability to infer. Since the AI Act does not clearly define what inference is, there is a gray area for certain data-driven systems.
A specific example is credit scoring systems, which are listed by Annex III of the AI Act. At the same time, however, these are often implemented using statistical models for which it is unclear whether they have the capability to infer and thus fall under the AI definition of the AI Act at all. Motivated by statistical learning theory, this work develops a framework for grading different levels of the capability to infer.
Based on the AI Act and the Commission Guidelines on the definition of an artificial intelligence system, we analyze which levels constitute sufficient capability to infer within the meaning of the AI Act and where further regulatory clarity is needed. We illustrate the framework by creating two realistic credit scoring workflows and show whether and where inference occurs in them. Our analysis illustrates that not only individual models but the entire data processing workflow must be considered.
It also shows that the involvement of human experts during development can have significant influence on the capability to infer. Code can be found at https://github. com/fraunhofer-iais/inference-framework-creditscorecards.
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