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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00233 |
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| _version_ | 1866910977916665856 |
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| author | Sharma, Aasish Kumar Kyosev, Dimitar Kunkel, Julian |
| author_facet | Sharma, Aasish Kumar Kyosev, Dimitar Kunkel, Julian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ethical AI: Towards Defining a Collective Evaluation Framework Sharma, Aasish Kumar Kyosev, Dimitar Kunkel, Julian Artificial Intelligence 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. |
| title | Ethical AI: Towards Defining a Collective Evaluation Framework |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00233 |