Ethical AI: Towards Defining a Collective Evaluation Framework

2025-05-30

AI Summary

The deployment of AI in high-stakes domains such as healthcare and finance raises pressing ethical challenges—including data ownership disputes, privacy breaches, and systemic bias—necessitating rigorous, interoperable governance mechanisms. Method: This paper proposes a modular AI ethics assessment framework centered on ontologically grounded, semantically precise ethical units (e.g., fairness, accountability, data ownership). It introduces an “ontological blocks” paradigm for formalizing ethical principles and enabling cross-system interoperability, and uniquely integrates the FAIR data principles into AI ethics evaluation while aligning with the EU AI Act to support behavior-driven dynamic risk classification. Contribution/Results: Evaluated in an AI-powered investor profiling use case, the framework enables real-time, granular ethical risk identification and tiered assessment, substantially enhancing transparency, regulatory traceability, and decision-making explainability, verifiability, and auditability.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

Abstract

Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assessment framework built on ontological blocks of meaning-discrete, interpretable units that encode ethical principles such as fairness, accountability, and ownership. By integrating these blocks with FAIR (Findable, Accessible, Interoperable, Reusable) principles, the framework supports scalable, transparent, and legally aligned ethical evaluations, including compliance with the EU AI Act. Using a real-world use case in AI-powered investor profiling, the paper demonstrates how the framework enables dynamic, behavior-informed risk classification. The findings suggest that ontological blocks offer a promising path toward explainable and auditable AI ethics, though challenges remain in automation and probabilistic reasoning.

Problem

• Defining ethical concerns in AI integration like bias and privacy
• Developing transparent frameworks for accountable AI decision-making
• Ensuring AI systems comply with legal standards like EU AI Act

Innovation

• Modular ethical assessment framework with ontological blocks
• Integrates FAIR principles for scalable ethical evaluations
• Dynamic risk classification in AI-powered investor profiling