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Mason Marks, "Algorithmic Disability Discrimination: How Corporate Mining of Emergent Medical Data Promotes Inequality"

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June 1, 2018

Historically and across societies people with disabilities have been stigmatized and excluded from social opportunities on a variety of culturally specific grounds. These justifications include assertions that people with disabilities are biologically defective, less than capable, costly, suffering, or fundamentally inappropriate for social inclusion. Rethinking the idea of disability so as to detach being disabled from inescapable disadvantage has been considered a key to twenty-first century reconstruction of how disablement is best understood.

Such ‘destigmatizing’ has prompted hot contestation about disability. Bioethicists in the ‘destigmatizing’ camp have lined up to present non-normative accounts, ranging from modest to audacious, that characterize disablement as “mere difference” or in other neutral terms. The arguments for their approach range from applications of standards for epistemic justice to insights provided by evolutionary biology. Conversely, other bioethicists vehemently reject such non-normative or “mere difference” accounts, arguing instead for a “bad difference” stance. “Bad difference” proponents contend that our strongest intuitions make us weigh disability negatively. Furthermore, they warn, destigmatizing disability could be dangerous because social support for medical programs that prevent or cure disability is predicated on disability’s being a condition that it is rational to avoid. Construing disability as normatively neutral thus could undermine the premises for resource support, access priorities, and cultural mores on which the practice of medicine depends.

The “mere difference” vs. “bad difference” debate can have serious implications for legal and policy treatment of disability, and shape strategies for allocating and accessing health care. For example, the framing of disability impacts the implementation of the Americans with Disabilities Act, Section 1557 of the Affordable Care Act, and other legal tools designed to address discrimination. The characterization of disability also has health care allocation and accessibility ramifications, such as the treatment of preexisting condition preclusions in health insurance. The aim of this conference was to construct a twenty-first century conception of disablement that resolves the tension about whether being disabled is merely neutral or must be bad, examines and articulates the clinical, philosophical, and practical implications of that determination, and attempts to integrate these conclusions into medical and legal practices.

Learn more: http://petrieflom.law.harvard.edu/events/details/2018-petrie-flom-center-annual-conference

Published in: Health & Medicine
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Mason Marks, "Algorithmic Disability Discrimination: How Corporate Mining of Emergent Medical Data Promotes Inequality"

  1. 1. Algorithmic Disability Discrimination: How Corporate Mining of Emergent Medical Data Promotes Inequality Mason Marks, MD, JD Visiting Fellow, Yale Law School Information Society Project Copyright © 2018 Mason Marks
  2. 2. Digital Traces • Credit card purchases • Search history • Browser history and click streams • Social media posts and likes • Private messages on social media or messaging apps • Path traveled while walking, driving, or using Uber or Lyft • Videos streamed on YouTube, Netflix, Amazon, and Facebook • Music streamed on Pandora, Spotify, iTunes • Data gathered by wearables such as FitBits and Apple Watches • Data gathered by digital assistants such as Siri and Alexa Copyright © 2018 Mason Marks
  3. 3. credit card purchases search histories click streams Machine Learning Health Categorization social media posts & likes data gathered by wearables private messages on social media videos streamed music streamed data gathered by digital assistants Raw User Data (Collected) Emergent Medical Data (Inferred) Substance Use Disorder Copyright © 2018 Mason Marks Depression
  4. 4. credit card purchases search histories click streams Machine Learning Health Categorization social media posts & likes data gathered by wearables private messages on social media videos streamed music streamed data gathered by digital assistants Raw User Data (Collected) Emergent Medical Data (Inferred) Targeted Advertising Automated Decision-Making Scoring Copyright © 2018 Mason Marks Substance Use Disorder Depression
  5. 5. Targeted Advertising Mobility Impairment Walkers, Scooters, Wheelchairs Substance Use Disorder Drug Treatment Programs Eating Disorder Diet Pills or Stimulants Gambling Disorder Discounted Las Vegas Vacation Chronic Pain Illegal Opioids Disability or Medical Condition Advertised Product Copyright © 2018 Mason Marks
  6. 6. Scoring • China’s Social Credit Score • US FICO Credit Score • Employee Flight-Risk Score Copyright © 2018 Mason Marks
  7. 7. Automated Decision-Making Score Al Decision Maker Outcome Copyright © 2018 Mason Marks Flight Risk Hiring Algorithm Don’t Hire
  8. 8. Use of Technology by People with Disabilities • Sensor-based devices for the visually impaired • Augmented and Alternative Communication (AAC) devices • Internet-enabled “smart” hearing aids • Assistive smartphone apps • Assistive neurotechnologies
  9. 9. Disruption of Traditional Flow of Medical Information Copyright © 2018 Mason Marks Patient Doctor Hospital/Pharmacy/Insurance Company Corporation/Advertiser “de-identified”EMD Traditional Flow Disrupted Flow
  10. 10. credit card purchases search histories click streams Machine Learning Health Categorization social media posts & likes data gathered by wearables videos streamed music streamed Raw User Data (Collected) Emergent Medical Data (Inferred) Diagnosis Copyright © 2018 Mason Marks Substance Use Disorder Depression
  11. 11. EMD Mining as a Breach of Fiduciary Duties 1. Duty of Care 2. Duty of Confidentiality 3. Duty of Loyalty Copyright © 2018 Mason Marks Don’t Diagnose Don’t Share Don’t Exploit
  12. 12. Conceptualizing EMD Mining 1. Diversion of the traditional flow of medical information. 2. Analogous to medical diagnosis. 3. A breach of fiduciary duties. Copyright © 2018 Mason Marks
  13. 13. Recommendations 1. Notify users that health data may be inferred and allow opt-out. 2. Expand HIPAA definition of covered entities. 3. Create new regulation for health data not covered by HIPAA. 4. Protect privacy generally (emulate EU and GDPR). 5. Establish fiduciary duties (through legislation or litigation). Copyright © 2018 Mason Marks
  14. 14. Algorithmic Disability Discrimination: How Corporate Mining of Emergent Medical Data Promotes Inequality Mason Marks, MD, JD Visiting Fellow, Yale Law School Information Society Project Copyright © 2018 Mason Marks

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