When Do Data-Driven Systems Exhibit the Capability to Infer?

• URL: http://arxiv.org/abs/2606.11769v1
• Date: Wed, 10 Jun 2026 07:49:34 GMT
• Title: When Do Data-Driven Systems Exhibit the Capability to Infer?
• Abstract summary: Key distinguishing feature of AI systems under the AI Act is the capability to infer.<n>A specific example is credit scoring systems, which are listed by Annex III of the AI Act.<n>Motivated by statistical learning theory, this work develops a framework for grading different levels of the capability to infer.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution [80.98492754957466]
  Core trustworthy AI objectives, such as fairness, robustness, privacy, and explainability, are hard to achieve simultaneously.<n>This paper argues that causality is necessary to understand and balance trade-offs in performance and multiple objectives of trustworthy AI.
  arXiv  Detail & Related papers  (2026-05-04T14:26:28Z)
• AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
  This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
  arXiv  Detail & Related papers  (2025-11-27T16:56:04Z)
• General Scales Unlock AI Evaluation with Explanatory and Predictive Power [57.7995945974989]
  benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems.<n>We introduce general scales for AI evaluation that can explain what common AI benchmarks really measure.<n>Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate.
  arXiv  Detail & Related papers  (2025-03-09T01:13:56Z)
• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• How VADER is your AI? Towards a definition of artificial intelligence systems appropriate for regulation [39.58317527488534]
  Recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI.<n>We propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is.
  arXiv  Detail & Related papers  (2024-02-07T17:41:15Z)
• Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination [1.4305544869388402]
  Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making. The integration of KGs with neuronal learning is currently a topic of active research. This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination.
  arXiv  Detail & Related papers  (2023-10-30T12:51:52Z)
• General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance [16.030931070783637]
  General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations.
  arXiv  Detail & Related papers  (2023-07-26T16:35:48Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Knowledge-intensive Language Understanding for Explainable AI [9.541228711585886]
  How AI-led decisions are made and what determining factors were included are crucial to understand. It is critical to have human-centered explanations that are directly related to decision-making. It is necessary to involve explicit domain knowledge that humans understand and use.
  arXiv  Detail & Related papers  (2021-08-02T21:12:30Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.