Vollständiger Abstract
Worum geht es in dieser Arbeit?
From AI-powered diagnostic tools in hospitals to autonomous drones in military operations, Artificial Intelligence is rapidly shaping decisions that affect millions of lives every day. Imagine a medical AI misdiagnosing a patient due to faulty training data, or a semi-autonomous weapon acting unpredictably on the battlefield — who is held accountable when such high-risk technologies fail?This paper explores how India, the European Union (EU), and the United States (US) address the legal accountability of AI systems, particularly in public health and military applications, where the consequences of errors or misuse can be catastrophic. Instead of relying on statistics or coding frameworks, this study uses a qualitative comparative approach to examine laws, policy documents, ethical guidelines, and case studies.Through a detailed review of legislative texts (such as the EU AI Act, US Executive Orders, and India's evolving AI strategy), court decisions, and international instruments like the OECD AI Principles and UNESCO Recommendation on AI Ethics, the paper identifies common legal gaps and unique regulatory approaches. It highlights how the EU has adopted a more structured regulatory path, the US follows a sector-based approach, and India is at a formative stage, relying heavily on ethical guidelines rather than binding rules.The paper concludes by proposing a layered accountability framework that blends legal responsibility, ethical oversight, and international cooperation to address the fast-evolving challenges of AI in these critical sectors.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Lakshmi Walia, Namrata Yadav
- Quelle
- ShodhAI: Journal of Artificial Intelligence
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 3048-9245, 3108-1940
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Zitierfähiger Nachweis
Lakshmi Walia, Namrata Yadav (2026). EXAMINING LEGAL ACCOUNTABILITY FRAMEWORKS FOR AI IN HEALTHCARE. ShodhAI: Journal of Artificial Intelligence. https://doi.org/10.29121/shodhai.v3.i2.2026.106