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Brent Mittelstadt, "From Protecting Individuals to Groups in Biomedical Big Data"

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Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.

This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.

The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.

Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.

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Brent Mittelstadt, "From Protecting Individuals to Groups in Biomedical Big Data"

  1. 1. From  Individuals  to  Groups  in   Biomedical  Big  Data Brent  Daniel  Mittelstadt
  2. 2. Aim § Individualistic  conceptions  of  privacy  insufficiently  protect   individuals  against  the  invasive  effects  of  Big  Data  analytics   that  involve  classification  of  people  (or  data  describing  people) § Group  privacy  is  proposed  as  a  third  interest  to  balance   alongside  individual  privacy  and  social,  commercial  and   epistemic  benefits  when  assessing  the  ethical  acceptability  of   automated  knowledge  work  in  general,  and  algorithmic   classification  systems  in  particular.   6/14/16Problems  of  Context  and   Abstraction  in  Big  Data Page  2
  3. 3. Types  of  groups § Collectives  – A  group  intentionally  joined  due  to  collective   interests,  shared  background  or  other  explicit  common  traits   and  purposes.   § Examples:  patient  advocacy  group,  labour  unions § Ascriptive groups  – A  group  whose  membership  is   determined  by  inherited  or  incidentally  developed   characteristic.   § Examples:  genetic  groups,  patient  cohorts § Ad  hoc  groups  – A  group  whose  membership  is  assembled   for  a  third  party  interest  according  to  perceived  links  between   members § Examples:  market  segments,  profiling  groups 6/14/16Problems  of  Context  and   Abstraction  in  Big  Data Page  3
  4. 4. § European  Data  Protection  Directive/Regulation  and  Common  Rule   both  protect  privacy  of  identifiable  individuals. § “Privacy  laws  apply  only  to  identified  or  identifiable  persons;;  one  is   not  a  ‘person’  in  the  absence  of  identifiability.”  (Knoppers  and   Saginur  2005,  925). Privacy  for  Identifiable  Individuals 6/14/16Two  Ethical  Challenges   for  the  IoT Page  4
  5. 5. Big data analytics treat individuals as types. Within analytics, Alice’s identity is shared with other data subjects. It is constituted from shared behavioural identity tokens Alice
  6. 6. Profiling  Identity
  7. 7. Privacy  for  groups:  the  right  to  inviolate   personality § Based  on  concept  of  informational  identity  (Floridi) § Privacy  as  identity-­constitutive § Privacy  violations  as  attacks  on  self-­defined  identity § Right  to  immunity  from  unknown,  undesired,  or  unintentional   changes  in  one’s  own  identity  (Warren  and  Brandeis) § Analytics  as  attack  on  shared  group  identity 6/14/16Problems  of  Context  and   Abstraction  in  Big  Data Page  9
  8. 8. Who  should  hold  a  right  to  group  privacy? § Individual  right  vs.  group  right § Group  rights   § Precedent:  Rights  of  collectives  (e.g.  national  sovereignty,  union’s   rights  to  assemble) § Requirements:  Collective  identity  and  collective  agency § Ad  hoc  groups  have  neither,  but  can  be  considered  moral   patients § Ad  hoc  groups  deserve  to  be  rights-­holders  due  to  shared   ownership  of  behavioural  identity  tokens 6/14/16Problems  of  Context  and   Abstraction  in  Big  Data Page  10
  9. 9. A  strong  or  weak  right § Strong § Valid  claims  (e.g.  control)  can  be  made  on  processes  that  create   identity-­constituting  information § Weak § A  claim  to  be  educated  and  empowered  about  identity-­constitutive   processes  so  as  to  make  more  informed  decisions  with  one’s  data § Moderate § Oversight,  e.g.  rights-­holders  kept  ‘in-­the-­loop’  by  data  processors 6/14/16Problems  of  Context  and   Abstraction  in  Big  Data Page  11
  10. 10. Proactive  or  reactive  protections § Proactive § Prevention  of  construction  of  certain  types  of  profiles  through   Big  Data  analytics § Prevention  of  certain  forms  of  knowledge  generation  about  a   group § Reactive § A duty  to  inform  individuals  of  group  membership  and  new   knowledge  about  the  group § A  right  to  control  external  identities 6/14/16Two  Ethical  Challenges   for  the  IoT Page  12
  11. 11. Group  privacy  in  biomedical  Big  Data § Applicable  in  principle  to  any  type  of  analytics § Commercial  Big  Data  analytics  (e.g.  hiring,  wellness  programmes,  health   insurance) § Redress  information  asymmetry  between  data  subjects  and  commercial   processors § Digital  epidemiology § Lack  of  existing  social  contract  for  research § Risk  stratification/personalized  medicine § Group  privacy  to  be  balanced  with  individual  privacy  rights  and  the   social,  commercial  and  epistemic  benefits  of  medical  data   processing § Group  privacy  as  theoretical  framework  for  consent  reforms 6/14/16Problems  of  Context  and   Abstraction  in  Big  Data Page  13
  12. 12. Open  questions § What  new  types  of  vulnerable  groups  will  Big  Data  analytics   create? § Which  new  attributes/classes  require  protection? § How  can  an  ad  hoc  group’s  right  to  group  privacy  be  enforced   without  collective  agency? § Stewardship § Auditing 6/14/16Problems  of  Context  and   Abstraction  in  Big  Data Page  14
  13. 13. ACKNOWLEDGEMENTS This  research  is  supported  by  a  John  Fell  Fund   Major  Grant.   brent.mittelstadt@oii.ox.ac.uk COPYRIGHT  DISCLAIMER.  Texts,  marks,  logos,  names,  graphics,  images,  photographs,   illustrations,  artwork,  audio  clips,  video  clips,  and  software  copyrighted  by  their  respective   owners  are  used  on  these  slides  for  non-­commercial,  educational  and  personal  purposes  only.   Use  of  any  copyrighted  material  is  not  authorized  without  the  written  consent  of  the  copyright   holder.    Every  effort  has  been  made  to  respect  the  copyrights  of  other  parties.  If  you  believe  that   your  copyright  has  been  misused,  please  direct  your  correspondence  to:   brent.mittelstadt@oii.ox.ac.uk   stating  your  position  and  I  shall  endeavour to  correct  any  misuse.

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