Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance

Sylvie Delacroix, Neil D. Lawrence
International Data Privacy Law, Oxford Academic 9(4):236-252, 2019.

Abstract

From the friends we make to the foods we like, via our shopping and sleeping habits, most aspects of our quotidian lives can now be turned into machine-readable data points. For those able to turn these data points into models predicting what we will do next, this data can be a source of wealth. For those keen to replace biased, fickle human decisions, this data—sometimes misleadingly—offers the promise of automated, increased accuracy. For those intent on modifying our behaviour, this data can help build a puppeteer's strings. As we move from one way of framing data governance challenges to another, salient answers change accordingly. Just like the wealth redistribution way of framing those challenges tends to be met with a property-based, 'it's *our* data' answer, when one frames the problem in terms of manipulation potential, dignity-based, human rights answers rightly prevail (via fairness and transparency-based answers to contestability concerns). Positive data-sharing aspirations tend to be raised within altogether different conversations from those aimed at addressing the above concerns. Our data Trusts proposal challenges these boundaries.

Cite this Paper

BibTeX
@Article{Delacroix-trusts19, title = {Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance}, author = {Delacroix, Sylvie and Lawrence, Neil D.}, journal = {International Data Privacy Law}, pages = {236--252}, year = {2019}, volume = {9}, number = {4}, address = {Oxford, UK}, publisher = {Oxford Academic}, doi = {10.1093/idpl/ipz014}, pdf = {https://academic.oup.com/idpl/article-pdf/9/4/236/32744015/ipz014.pdf}, url = {http://inverseprobability.com/publications/bottom-up-data-trusts-disturbing-the-one-size-fits-all-approach-to-data-governance.html}, abstract = {From the friends we make to the foods we like, via our shopping and sleeping habits, most aspects of our quotidian lives can now be turned into machine-readable data points. For those able to turn these data points into models predicting what we will do next, this data can be a source of wealth. For those keen to replace biased, fickle human decisions, this data—sometimes misleadingly—offers the promise of automated, increased accuracy. For those intent on modifying our behaviour, this data can help build a puppeteer's strings. As we move from one way of framing data governance challenges to another, salient answers change accordingly. Just like the wealth redistribution way of framing those challenges tends to be met with a property-based, 'it's *our* data' answer, when one frames the problem in terms of manipulation potential, dignity-based, human rights answers rightly prevail (via fairness and transparency-based answers to contestability concerns). Positive data-sharing aspirations tend to be raised within altogether different conversations from those aimed at addressing the above concerns. Our data Trusts proposal challenges these boundaries. } }
Endnote
%0 Journal Article %T Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance %A Sylvie Delacroix %A Neil D. Lawrence %J International Data Privacy Law %D 2019 %F Delacroix-trusts19 %I Oxford Academic %P 236--252 %R 10.1093/idpl/ipz014 %U http://inverseprobability.com/publications/bottom-up-data-trusts-disturbing-the-one-size-fits-all-approach-to-data-governance.html %V 9 %N 4 %X From the friends we make to the foods we like, via our shopping and sleeping habits, most aspects of our quotidian lives can now be turned into machine-readable data points. For those able to turn these data points into models predicting what we will do next, this data can be a source of wealth. For those keen to replace biased, fickle human decisions, this data—sometimes misleadingly—offers the promise of automated, increased accuracy. For those intent on modifying our behaviour, this data can help build a puppeteer's strings. As we move from one way of framing data governance challenges to another, salient answers change accordingly. Just like the wealth redistribution way of framing those challenges tends to be met with a property-based, 'it's *our* data' answer, when one frames the problem in terms of manipulation potential, dignity-based, human rights answers rightly prevail (via fairness and transparency-based answers to contestability concerns). Positive data-sharing aspirations tend to be raised within altogether different conversations from those aimed at addressing the above concerns. Our data Trusts proposal challenges these boundaries.
RIS
TY - JOUR TI - Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance AU - Sylvie Delacroix AU - Neil D. Lawrence DA - 2019/10/01 ID - Delacroix-trusts19 PB - Oxford Academic VL - 9 IS - 4 SP - 236 EP - 252 DO - 10.1093/idpl/ipz014 L1 - https://academic.oup.com/idpl/article-pdf/9/4/236/32744015/ipz014.pdf UR - http://inverseprobability.com/publications/bottom-up-data-trusts-disturbing-the-one-size-fits-all-approach-to-data-governance.html AB - From the friends we make to the foods we like, via our shopping and sleeping habits, most aspects of our quotidian lives can now be turned into machine-readable data points. For those able to turn these data points into models predicting what we will do next, this data can be a source of wealth. For those keen to replace biased, fickle human decisions, this data—sometimes misleadingly—offers the promise of automated, increased accuracy. For those intent on modifying our behaviour, this data can help build a puppeteer's strings. As we move from one way of framing data governance challenges to another, salient answers change accordingly. Just like the wealth redistribution way of framing those challenges tends to be met with a property-based, 'it's *our* data' answer, when one frames the problem in terms of manipulation potential, dignity-based, human rights answers rightly prevail (via fairness and transparency-based answers to contestability concerns). Positive data-sharing aspirations tend to be raised within altogether different conversations from those aimed at addressing the above concerns. Our data Trusts proposal challenges these boundaries. ER -
APA
Delacroix, S. & Lawrence, N.D.. (2019). Bottom-up Data Trusts: Disturbing the 'One Size Fits All' Approach to Data Governance. International Data Privacy Law 9(4):236-252 doi:10.1093/idpl/ipz014 Available from http://inverseprobability.com/publications/bottom-up-data-trusts-disturbing-the-one-size-fits-all-approach-to-data-governance.html.

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