Tom Slee (SAP); Algorithmic Accountability: The Big Problems; Their Blog; 2017-10.
tl;dr → You have problems, SAP has expertise in this practice area. Call now.
Yvonne Baur, Brenda Reid, Steve Hunt, Fawn Fitter (SAP); How AI Can End Bias; In Their Other Blog, entitled The D!gitalist; 2017-01-16.
Teaser: Harmful human bias—both intentional and unconscious—can be avoided with the help of artificial intelligence, but only if we teach it to play fair and constantly question the results.
- The Canon is rehearsed.
- General Data Protection Regulation (GDPR)
- “in effect in” 2018 (2018-05-28).
Anti-patterns, Negative (Worst) Practices
- Bad statistics
- Ill-defined scales
- Bad Incentives
- Lack of transparency
Five Axes of Unfairness
Unfairness ↔ Disparate Impact
- Target variables
- Training data
- Feature selection
- Solon Barocas, self [Princeton]
- Cynthia Dwork, self [Microsoft]
Trade: pioneer [theorist]..
- Seth Flaxman, staff, Oxford University.
- Bryce Goodman, staff, Oxford University.
- Cathy O’Neil, self.
data scientiststatistician who works on a Macintosh Computer and lives in San Francisco.
- Frank Pasquale, professor, law [Maryland]
- Andrew Selbst, self [U.S. .Court of Appeals]
- Solon Barocas (Princeton University), Andrew D. Selbst (U.S. Court of Appeals); Big Data’s Disparate Impact; California Law Review, Vol. 104, 2016; 62 pages; ssrn:2477899; 2015-08-14; previously filled.
- Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, Rich Zemel; Fairness Through Awareness; arXiv:1104.3913; 2011-04-29→ 2011-11-20; 24 pages.
- Bryce Goodman, Seth Flaxman (Oxford University) Some Opinion/Editorial
- Frank Pasquale; The Black Box Society: The Secret Algorithms That Control Money and Information; Harvard University Press; 2016-08-29; 320 pages; ASIN:0674970845: Kindle: $10, paper: $11+SHT; separately filled.
- Cathy O’Neil; Weapons of Math Destruction; Broadway Books; 2017-09-06; 288 pages; ASIN:0553418831: Kindle: $12, paper: $8+SHT.
tl;dr → the opposition is elucidated and juxtaposed; the domain is problematized.
and → “Big Data,” by definition, is opportunistic and unsupervisable; it collects everything and identifies something later in the backend. Else it is not “Big Data” (it is “little data,” which is known, familiar, boring, and of course has settled law surrounding its operational envelope).
After years of drafting and negotiations, the EU finally passed the General Data Protection Regulation (GDPR). The GDPR’s impact will, most likely, be profound. Among the challenges data protection law faces in the digital age, the emergence of Big Data is perhaps the greatest. Indeed, Big Data analysis carries both hope and potential harm to the individuals whose data is analyzed, as well as other individuals indirectly affected by such analyses. These novel developments call for both conceptual and practical changes in the current legal setting.
Unfortunately, the GDPR fails to properly address the surge in Big Data practices. The GDPR’s provisions are — to borrow a key term used throughout EU data protection regulation — incompatible with the data environment that the availability of Big Data generates. Such incompatibility is destined to render many of the GDPR’s provisions quickly irrelevant. Alternatively, the GDPR’s enactment could substantially alter the way Big Data analysis is conducted, transferring it to one that is suboptimal and inefficient. It will do so while stalling innovation in Europe and limiting utility to European citizens, while not necessarily providing such citizens with greater privacy protection.
After a brief introduction (Part I), Part II quickly defines Big Data and its relevance to EU data protection law. Part III addresses four central concepts of EU data protection law as manifested in the GDPR: Purpose Specification, Data Minimization, Automated Decisions and Special Categories. It thereafter proceeds to demonstrate that the treatment of every one of these concepts in the GDPR is lacking and in fact incompatible with the prospects of Big Data analysis. Part IV concludes by discussing the aggregated effect of such incompatibilities on regulated entities, the EU, and society in general.
<snide>Apparently this was not known before the activists captured the legislature and affected their ends with the force of law. Now we know. Yet we all must obey the law, as it stands and as it is written. And why was this not published in an EU-located law journal, perhaps one located in … Brussels?</snide>
I. INTRODUCTION AND ROAD MAP
II. A BRIEF PRIMER ON BIG DATA AND THE LAW
III. THE GDPR’S INCOMPATIBILITY – FOUR EXAMPLES
A. Purpose Limitation
B. Data Minimization
C. Special Categories
D. Automated Decisions
IV. CONCLUSION: WHAT’S NEXT FOR EUROPE
There are 123 references, manifested as footnotes in the legal style.
Behavioral advertising industry slams ePrivacy plans; Jennifer Baker (IAPP); In Their Blog; 2017-09-07.
tl;dr → Filler. Quotes for color, background & verisimilitude…
- The Future Media Lounge event,
- sponsored by the European Interactive Digital Advertising Alliance (EDAA)
- General Data Protection Regulation (GDPR)
- European Interactive Digital Advertising Alliance (EDAA)
- ePrivacy Regulation
- European Parliament
- Marju Lauristin, ePrivacy Rapporteur
- Xavier Bouckaert, CEO, Roularta Media Group
- Angela Mills Wade,
- board member, EDAA
- director of EPC
- Despina Spanou, director for digital society, trust and cybersecurity, European Commission (EC)
- Sophie In’t Veld, MEP, NE
- Anna Maria Corazza Bildt, MEP, SE
- Dan Dalton, MEP, UK (GB?)