Drafts with AI – Part 3

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For the final section of the chapter on AI and privacy, I wrote the following prompt to elicit Perplexity Pro’s suggestions:

Write the next section of the textbook chapter on AI and privacy. This section must focus on the integration of AI into medicine, STEM, education, and government. The emphasis in this section should focus on the United States, although specific and relevant international events and accomplishments. In the final section of the chapter, please share what the potential dangers of AI are with specific reference to privacy as well as other potential issues. Conclude with AI best practices as well as examples of current ethical frameworks being suggested by scholars and the industry.

Integration of AI Across Key Sectors in the United States

Medicine and Healthcare

AI is revolutionizing diagnostics, treatment, and medical education in the U.S.:

  • Precision Diagnostics: AI-powered imaging tools, such as Aidoc’s FDA-cleared system for detecting collapsed lungs on X-rays, enable faster and more accurate diagnoses12. Wearables like the Apple Watch now include AI-driven atrial fibrillation detection, expanding preventive care12.
  • Personalized Medicine: Genomics and AI are merging to create tailored therapies. For example, Bloomer Tech uses AI to analyze cardiovascular data from wearable fabrics, targeting diseases that disproportionately affect women12.
  • Medical Education: The UK’s Data Science in the Medical Curriculum report (adopted by U.S. institutions like Harvard) mandates training in health informatics, AI ethics, and data governance to prepare graduates for data-driven healthcare4.

Internationally, the NHS’s investment in AI-driven disease tracking mirrors U.S. efforts to optimize resource management through large-scale health datasets4.

STEM Education

AI is addressing systemic challenges in U.S. education:

  • Personalized Learning: Platforms like Carnegie Learning’s MATHia adapt to individual student needs, improving pass rates in STEM courses from 73% to 91% in some cases13.
  • Bridging the Rural-Urban Gap: AI tools provide remote students access to advanced robotics and engineering curricula, mitigating resource disparities in rural schools13.
  • Administrative Efficiency: Universities leverage AI for admissions and scheduling, freeing resources for strategic initiatives2.

Government Operations

The U.S. federal government is prioritizing AI adoption:

  • Policy Reforms: The 2025 OMB memos (M-25-21 and M-25-22) streamline AI procurement and remove bureaucratic barriers, aiming to close the technology gap between agencies and the private sector3.
  • Public Trust Measures: The Trump administration’s Executive Order emphasizes balancing innovation with privacy and civil rights protections, reflecting global trends like the EU’s GDPR15.

Risks and Ethical Challenges of AI

Privacy Threats

  • Data Exploitation: AI systems often ingest sensitive data (e.g., biometrics, health records) without explicit consent, risking exposure through breaches or adversarial attacks56. For example, voice-cloning scams have surged, with criminals impersonating individuals to commit fraud8.
  • Surveillance: Predictive policing algorithms in cities like Los Angeles disproportionately target Black communities, perpetuating systemic bias8. China’s facial recognition networks highlight risks of mass surveillance8.

Broader Societal Risks

  • Bias in Healthcare: Racial disparities persist in diagnostic algorithms; for instance, dermatology AI trained on lighter skin tones misdiagnoses conditions in darker-skinned patients19.
  • Security Vulnerabilities: Prompt injection attacks can manipulate AI systems into leaking private data, such as medical records6.
  • Economic Inequality: Overinvestment in AI risks neglecting other critical sectors, potentially destabilizing job markets and widening economic gaps8.

Best Practices and Ethical Frameworks

Industry Standards

  • Data Minimization: Collect only essential data (e.g., omitting demographics in hiring algorithms to reduce bias)20.
  • Transparency: AWS mandates human oversight for AI systems impacting health, safety, or civil rights11.
  • Security: Encryption and adversarial testing are prioritized by firms like IBM to thwart data exfiltration5.

Ethical Frameworks

  • The Belmont Principles: Adapted for AI, these emphasize beneficence (minimizing harm), justice (equitable benefits), and respect for persons (informed consent)10.
  • MIT’s AI Policy Brief: Advocates for extending existing privacy laws to AI and implementing third-party audits to ensure fairness16.
  • EU-U.S. Collaboration: The GDPR and California’s CPRA grant users rights to opt out of automated profiling, inspiring global standards15.

Conclusion

While AI drives innovation in medicine, education, and governance, its risks-particularly to privacy-demand proactive regulation. By integrating ethical frameworks like the Belmont Principles and adopting technical safeguards, the U.S. can harness AI’s potential while upholding civil liberties. Global cooperation, as seen in the EU’s AI Act and U.S. Executive Orders, will be critical to navigating this transformative era responsibly1015.

Citations:

  1. https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
  2. https://www.norc.org/research/library/artificial-intelligence-in-stem-higher-education.html
  3. https://www.whitehouse.gov/articles/2025/04/white-house-releases-new-policies-on-federal-agency-ai-use-and-procurement/
  4. https://www.hdruk.ac.uk/news/medical-schools-to-integrate-ai-and-data-science-skills-into-education/
  5. https://www.ibm.com/think/insights/ai-privacy
  6. https://www.trigyn.com/insights/ai-and-privacy-risks-challenges-and-solutions
  7. https://www.reuters.com/legal/legalindustry/privacy-paradox-with-ai-2023-10-31/
  8. https://builtin.com/artificial-intelligence/risks-of-artificial-intelligence
  9. https://dialzara.com/blog/ai-ethics-and-data-privacy-2024-guide/
  10. https://onlinedegrees.sandiego.edu/ethics-in-ai/
  11. https://aws.amazon.com/ai/responsible-ai/policy/
  12. https://www.worldhealthexpo.com/insights/ai-automation/top-5-ai-driven-medical-innovations-in-the-united-states
  13. https://mrccedtech.com/ai-is-transforming-stem-education/
  14. https://transcend.io/blog/ai-and-privacy
  15. https://www.bdo.com/insights/advisory/ethical-ai-and-privacy-series-article-2-the-regulations
  16. https://www.alvarezandmarsal.com/insights/ai-ethics-part-two-ai-framework-best-practices
  17. https://allenai.org/responsible-use
  18. https://www.sgu.edu/blog/medical/ai-in-medicine-and-healthcare/
  19. https://hai.stanford.edu/news/privacy-ai-era-how-do-we-protect-our-personal-information
  20. https://community.trustcloud.ai/docs/grc-launchpad/grc-101/governance/data-privacy-and-ai-ethical-considerations-and-best-practices/
  21. https://iapp.org/news/a/shaping-the-future-a-dynamic-taxonomy-for-ai-privacy-risks
  22. https://www.ituonline.com/blogs/best-practices-for-ethical-ai-data-privacy/
  23. https://www.csis.org/analysis/protecting-data-privacy-baseline-responsible-ai
  24. https://www.ama-assn.org/practice-management/digital/augmented-intelligence-medicine
  25. https://www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care/
  26. https://www.lapu.edu/post/ai-health-care-industry
  27. https://pmc.ncbi.nlm.nih.gov/articles/PMC10755136/
  28. https://pmc.ncbi.nlm.nih.gov/articles/PMC5819974/
  29. https://www.nsf.gov/funding/initiatives/ige/updates/empowering-tomorrow-nsfs-role-advancing-ai-driven-stem
  30. https://www.brookings.edu/articles/for-ai-to-make-government-work-better-reduce-risk-and-increase-transparency/
  31. https://academic.oup.com/pnasnexus/article/2/12/pgad410/7477225
  32. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
  33. https://www.princetonreview.com/ai-education/ai-and-stem
  34. https://www.ncsl.org/technology-and-communication/artificial-intelligence-in-government-the-federal-and-state-landscape
  35. https://ai.gov/wp-content/uploads/2024/06/FINDINGS_Enhancing-AIs-Positive-Impact-on-Science-and-Medicine.pdf
  36. https://iapp.org/resources/article/consumer-perspectives-of-privacy-and-ai/
  37. https://www.dataguard.com/blog/growing-data-privacy-concerns-ai/
  38. https://economictimes.com/news/how-to/ai-and-privacy-the-privacy-concerns-surrounding-ai-its-potential-impact-on-personal-data/articleshow/99738234.cms
  39. https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2025/01/ai-risk-consumer-harm
  40. https://www.americanbar.org/groups/gpsolo/resources/magazine/2025-mar-apr/privacy-risks-ai-your-data-their-knowledge/
  41. https://ovic.vic.gov.au/privacy/resources-for-organisations/artificial-intelligence-and-privacy-issues-and-challenges/
  42. https://news.harvard.edu/gazette/story/2020/10/ethical-concerns-mount-as-ai-takes-bigger-decision-making-role/
  43. https://www.vktr.com/ai-upskilling/data-mongering-is-the-silent-ai-threat-to-privacy-and-personal-autonomy/
  44. https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2021/beware-the-privacy-violations-in-artificial-intelligence-applications
  45. https://coalfire.com/the-coalfire-blog/the-dark-side-of-ai-data-privacy
  46. https://vtmit.vt.edu/academics/student-experience/blog/ethics-in-ai.html
  47. https://www.nist.gov/blogs/cybersecurity-insights/managing-cybersecurity-and-privacy-risks-age-artificial-intelligence
  48. https://hai.stanford.edu/news/privacy-ai-era-how-do-we-protect-our-personal-information
  49. https://www.dhs.gov/ai/ensuring-ai-is-used-responsibly
  50. https://iapp.org/news/a/privacy-and-responsible-ai
  51. https://www.intelligence.gov/artificial-intelligence-ethics-framework-for-the-intelligence-community
  52. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
  53. https://www.diu.mil/responsible-ai-guidelines
  54. https://legal.thomsonreuters.com/blog/how-to-responsibly-use-ai-to-address-ethical-and-risk-challenges/
  55. https://hdsr.mitpress.mit.edu/pub/l0jsh9d1
  56. https://itsecurity.uiowa.edu/guidelines-secure-and-ethical-use-artificial-intelligence
  57. https://www.thedigitalspeaker.com/privacy-age-ai-risks-challenges-solutions/
  58. https://techpolicy.press/doge-plan-to-push-ai-across-the-us-federal-government-is-wildly-dangerous

Answer from Perplexity: pplx.ai/share

While I can see the starting points for topics I would want to cover, I find there is a lack of critical assessment of the evident concerns for the wide adoption of AI. To push the conversation a bit further, I issued a request for a rewrite using this prompt:

Please rewrite the response with the following parameters: use a traditional textbook style so that is accessible to freshmen and sophomore students at a public university in the United States of America, include what specific practices have already taken place under the issues of data exploitation and surveillance by American corporations and the US federal government, expand how privacy in healthcare can be violated due to the use of large medical data sets, provide a in-depth analysis of the 2025 OMB memos explaining potential positive and negative outcomes, address how the digital divide may complicate the goal of bridging the rural-urban gap. Finally, provide a unified list of ethical principles pulling from the Belmont principles, MIT’s AI Policy Brief, and the standards for EU and the GDPR.

AI, Privacy, and Societal Impact: A Comprehensive Overview

Data Exploitation and Surveillance in the U.S.

Corporate Practices:

  • Facebook/Cambridge Analytica Scandal (2018): Over 87 million users’ data were harvested without consent to influence political campaigns, highlighting risks of unregulated data monetization3.
  • Genetic Testing Firms: Companies like 23andMe revised privacy policies to expand data sharing with third parties, often without clear user consent1.
  • Wearable Tech: Devices like the Apple Watch collect health data, which companies may use for AI training under broad terms of service18.

Federal Surveillance:

  • DHS Social Media Monitoring: The Department of Homeland Security uses AI to scan social media posts of visa applicants for “extremist” rhetoric, raising concerns about overreach and biased definitions2.
  • EPA Employee Monitoring: Under the Trump administration, AI tools allegedly tracked federal employees for “hostile language” toward political figures2.

Privacy Risks in Healthcare AI

Large Medical Datasets:

  • Data Breaches: The 2024 Change Healthcare breach exposed records of 100 million patients, the largest healthcare data breach in U.S. history7.
  • Third-Party Apps: Fitness and mental health apps (e.g., BetterHelp, Premom) share user data with advertisers, bypassing HIPAA protections68.
  • Algorithmic Bias: Dermatology AI tools trained predominantly on lighter skin tones misdiagnose conditions in darker-skinned patients, exacerbating health disparities56.

Regulatory Gaps:

  • HIPAA Limitations: Apps not classified as “covered entities” under HIPAA can legally sell user health data, creating loopholes for exploitation68.

The 2025 OMB Memos: Balancing Innovation and Risk

Memorandum M-25-21 (AI Adoption):

  • Positive Outcomes:
    • Requires federal agencies to appoint Chief AI Officers to oversee ethical AI deployment.
    • Promotes “American AI” to reduce reliance on foreign technology.
    • Establishes a “high-impact AI” category for systems affecting civil rights or safety, mandating transparency reports4911.
  • Negative Risks:
    • Rushed implementation (deadlines as short as 60 days) may compromise thorough risk assessments.
    • Overemphasis on “pro-innovation” could sideline privacy safeguards911.

Memorandum M-25-22 (AI Procurement):

  • Positive Outcomes:
    • Bans vendors from using government data to train public AI models without consent.
    • Requires “model portability” to prevent vendor lock-in49.
  • Negative Risks:
    • Limited enforcement mechanisms for privacy compliance.
    • Excludes common commercial AI tools (e.g., chatbots), leaving gaps in oversight911.

The Digital Divide and Rural-Urban AI Gaps

Challenges:

  • Infrastructure: Only 65% of rural households have broadband access, limiting AI-driven education tools like Carnegie Learning’s MATHia1214.
  • Educational Disparities: Urban schools adopt AI grading and tutoring systems faster, while rural institutions lack funding for AI training programs1314.
  • Healthcare Access: Rural clinics often lack AI diagnostic tools due to high costs and connectivity barriers, worsening regional health inequities814.

Mitigation Efforts:

  • Starlink Satellites: Provide low-cost internet to remote areas, enabling telemedicine and online learning14.
  • Federal Grants: Programs like the USDA’s ReConnect Fund aim to expand rural broadband access by 202614.

Unified Ethical Principles for AI

Drawing from the Belmont Report, MIT Policy Brief, and EU GDPR:

  1. Respect for Autonomy (Belmont):
    • Users must consent to data collection and understand how AI systems affect them16.
  2. Beneficence (Belmont):
    • Prioritize AI systems that improve well-being (e.g., reducing diagnostic errors) while minimizing harm16.
  3. Justice (Belmont):
    • Ensure equitable access to AI benefits and prevent biased outcomes in hiring, healthcare, and policing16.
  4. Transparency (MIT/GDPR):
    • Disclose data sources, algorithmic decision-making, and error rates (e.g., FDA requirements for medical AI)1718.
  5. Data Minimization (GDPR):
    • Collect only essential data (e.g., omitting race in hiring algorithms unless legally required)1819.
  6. Accountability (MIT):
    • Assign liability for AI errors (e.g., hospitals held responsible for misdiagnoses by unvalidated tools)1719.
  7. Security (EU AI Act):
    • Encrypt health data and conduct adversarial testing to prevent breaches1819.

Conclusion

AI’s integration into medicine, education, and government offers transformative potential but requires vigilant oversight. The 2025 OMB memos reflect a push for innovation, yet their success hinges on addressing rural infrastructure gaps and enforcing ethical standards. By adopting principles like transparency, justice, and data minimization, the U.S. can harness AI’s benefits while safeguarding privacy and equity.

Citations:

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