Legal and Governance Issues in Non-Medical Diagnostic AI Systems: From Expert Systems to LLMs
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901659799519232 |
|---|---|
| author | Laczkovich, Roman R. |
| author_facet | Laczkovich, Roman R. |
| contents | <p class="MsoNormal">Diagnostic expert systems (DXPS) have been used for decades to emulate human problem-solving in specialized domains. This survey examines the evolution of non-medical DXPS from early rule-based <strong>expert systems</strong> to contemporary <strong>artificial intelligence</strong> approaches, including machine learning models and large language models (LLMs) for diagnostic tasks. We analyze how the shift from deterministic expert systems to data-driven AI has introduced new <strong>legal</strong> and <strong>governance</strong> challenges. Key issues reviewed include accountability and <strong>liability</strong> for automated decisions, regulatory and compliance frameworks for high-stakes diagnostics in fields like engineering and finance, and ethical considerations such as transparency, bias, and user trust. By comparing historical precedents (e.g., how legacy expert systems were validated and regulated) with emerging concerns around modern AI-driven diagnostics, the survey highlights gaps in current governance. The paper also discusses proposed policy responses and frameworks for responsible deployment of diagnostic AI outside the medical domain. Our findings underscore the need for updated governance structures to ensure <strong>accountability</strong> and <strong>safety</strong> in the next generation of diagnostic systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19596405 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Legal and Governance Issues in Non-Medical Diagnostic AI Systems: From Expert Systems to LLMs Laczkovich, Roman R. AI governance diagnostic expert systems non-medical AI legal liability accountability regulatory policy expert systems large language models <p class="MsoNormal">Diagnostic expert systems (DXPS) have been used for decades to emulate human problem-solving in specialized domains. This survey examines the evolution of non-medical DXPS from early rule-based <strong>expert systems</strong> to contemporary <strong>artificial intelligence</strong> approaches, including machine learning models and large language models (LLMs) for diagnostic tasks. We analyze how the shift from deterministic expert systems to data-driven AI has introduced new <strong>legal</strong> and <strong>governance</strong> challenges. Key issues reviewed include accountability and <strong>liability</strong> for automated decisions, regulatory and compliance frameworks for high-stakes diagnostics in fields like engineering and finance, and ethical considerations such as transparency, bias, and user trust. By comparing historical precedents (e.g., how legacy expert systems were validated and regulated) with emerging concerns around modern AI-driven diagnostics, the survey highlights gaps in current governance. The paper also discusses proposed policy responses and frameworks for responsible deployment of diagnostic AI outside the medical domain. Our findings underscore the need for updated governance structures to ensure <strong>accountability</strong> and <strong>safety</strong> in the next generation of diagnostic systems.</p> |
| title | Legal and Governance Issues in Non-Medical Diagnostic AI Systems: From Expert Systems to LLMs |
| topic | AI governance diagnostic expert systems non-medical AI legal liability accountability regulatory policy expert systems large language models |
| url | https://doi.org/10.5281/zenodo.19596405 |