# Ethical AI: Towards Defining a Collective Evaluation Framework

arXiv: https://arxiv.org/abs/2506.00233 · HTML: https://mlsanity.com/paper/2506.00233/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 10 of 10)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME](https://mlsanity.com/paper/2305.02012/) (2023)
- [Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applications](https://mlsanity.com/paper/2408.10215/) (2024)
- [Building Ethically Bounded AI](https://mlsanity.com/paper/1812.03980/) (2018)
- [Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning](https://mlsanity.com/paper/2407.00324/) (2024)
- [Machine Ethics and Automated Vehicles](https://mlsanity.com/paper/2010.15665/) (2020)
- [An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI](https://mlsanity.com/paper/2310.14455/) (2023)
- [Towards a framework for understanding societal and ethical implications of Artificial Intelligence](https://mlsanity.com/paper/2001.09750/) (2020)
- [Ethics of Artificial Intelligence Demarcations](https://mlsanity.com/paper/1904.10239/) (2019)

## Built on by (most cited first, 3 of 3)

- [Auditing Training Data in Domain-adapted LLMs: LoRA-MINT](https://mlsanity.com/paper/2606.06946/) (2026)
- [Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems](https://mlsanity.com/paper/2605.23297/) (2026)
- [Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review](https://mlsanity.com/paper/2608.14562/) (2026)

## Abstract (arXiv)

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.