AI Ethics in Healthcare
Challenges
- Biased Data: The data used to train AI may be biased, generating misleading or inaccurate information that could pose risks to health, equity and inclusiveness;
- Incorrect Data: Large Language Model (LLMs) tools generate responses that can appear authoritative and plausible to an end user; however, these responses may be completely incorrect or contain serious errors, especially for health-related responses.
- Integration Issues: Ethical issues w.r.t., machines self-operating humans, plus, reluctance among medical practitioners to adopt AI and fear of AI replacing humans are some of the common concerns.
- Data Privacy and Security: Mobile health applications and devices are now using AI and ....
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Ethics, Integrity & Aptitude
- 1 Impact of Probity in Governance on Administrative Effectiveness and Public Trust
- 2 AI in Decision Making: Impact on Administration
- 3 Ethical Issues and Challenges in Social Media
- 4 Moral Values and Ethical Leadership
- 5 Moral Relativism vs. Moral Universalism
- 6 Ethical Dimensions of Celebrity Endorsements
- 7 Bioethics and its Significance
- 8 Foundational Values of Civil Services: Measures to Ensure their Effectiveness
- 9 Relevance of Swami Vivekananda’s Moral Philosophy in Contemporary Society
- 10 Role of Impartiality and Non-Partisanship in Building Ethical Integrity of Public Service
