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Ethics & AI

Overview

Ethical considerations for developing and deploying AI systems in healthcare and beyond.


Core Principles

Beneficence

  • Create positive impact
  • Improve outcomes
  • Reduce suffering
  • Enable human flourishing

Non-Maleficence

  • Avoid harm
  • Consider unintended consequences
  • Fail safely
  • Protect vulnerable

Autonomy

  • Respect human agency
  • Maintain transparency
  • Enable informed decisions
  • Preserve choice

Justice

  • Fair distribution of benefits
  • Equitable access
  • Avoid bias
  • Consider all stakeholders

AI-Specific Considerations

Transparency

  • Explainable decisions
  • Clear limitations
  • Honest capabilities
  • Open about AI use

Bias & Fairness

  • Diverse training data
  • Regular audits
  • Bias mitigation
  • Fair outcomes

Privacy

  • Data minimization
  • Consent
  • Security
  • De-identification

Accountability

  • Clear responsibility
  • Human oversight
  • Audit trails
  • Feedback mechanisms

Healthcare AI Ethics

Patient Safety

  • Thorough testing
  • Clinical validation
  • Continuous monitoring
  • Clear failure modes

Clinical Integration

  • Augment, don't replace
  • Appropriate trust
  • Clinical judgment
  • Shared decision-making

Decision Framework

  1. Who is affected?
  2. What are the risks?
  3. What are the benefits?
  4. Are we being transparent?
  5. Can we justify this decision?


Last updated: January 2025