Ethics & Society
Ethical Concerns
With power comes responsibility. As AI becomes embedded in medicine, data privacy and algorithmic bias are critical issues. Patient data must be protected under regulations like HIPAA and GDPR. Additionally, biased datasets can lead to unequal treatment outcomes.
Examples of Real-World Ethical Issues:
- In 2019, a major healthcare algorithm used in U.S. hospitals was found to have racial bias, assigning lower risk scores to Black patients than to white patients with the same health status.
- Facial recognition algorithms, when applied in medical imaging, have shown higher error rates in underrepresented populations due to unbalanced training datasets.
- Genetic data sharing platforms have raised concerns about consent and future use—cases have arisen where individuals’ genetic data were used for research they had not explicitly agreed to.
Balancing Innovation and Ethics
We must ensure that innovation doesn’t outpace regulation. Transparent algorithms, inclusive datasets, and interdisciplinary ethics committees are essential. Ethical AI must consider long-term societal impacts while encouraging growth.
Examples of How to Balance Innovation and Ethics:
- Incorporating AI ethics training and cross-disciplinary ethics boards in biotech companies.
- Requiring independent audits of AI models before deployment in clinical environments.
- Using diverse, representative training data to reduce algorithmic bias.
- Implementing dynamic consent models for genetic data usage, allowing participants more control over how their data is shared.
- Enforcing clear, patient-centered guidelines for the use of predictive AI tools in diagnostics and treatment.