June 16th, 2018

Phantom Perspective

ROLL CALL 

A new report from Fordham CLIP sheds light on the market for student list data from higher education institutions

From the paper authored by N. CAMERON RUSSELL, JOEL R. REIDENBERG, ELIZABETH MARTIN, and THOMAS NORTON of the FORDHAM CENTER ON LAW AND INFORMATION POLICY: 

“Student lists are commercially available for purchase on the basis of ethnicity, affluence, religion, lifestyle, awkwardness, and even a perceived or predicted need for family planning services.

This information is being collected, marketed, and sold about individuals because they are students."

Drawing from publicly-available sources, public records requests from educational institutions, and marketing materials sent to high school students gathered over several years, the study paints an unsettling portrait of the murky market for student list data, and makes recommendations for regulatory response: 

  1. The commercial marketplace for student information should not be a subterranean market. Parents, students, and the general public should be able to reasonably know (i) the identities of student data brokers, (ii) what lists and selects they are selling, and (iii) where the data for student lists and selects derives. A model like the Fair Credit Reporting Act (FCRA) should apply to compilation, sale, and use of student data once outside of schools and FERPA protections. If data brokers are selling information on students based on stereotypes, this should be transparent and subject to parental and public scrutiny.
  2. Brokers of student data should be required to follow reasonable procedures to assure maximum possible accuracy of student data. Parents and emancipated students should be able to gain access to their student data and correct inaccuracies. Student data brokers should be obligated to notify purchasers and other downstream users when previously-transferred data is proven inaccurate and these data recipients should be required to correct the inaccuracy.
  3. Parents and emancipated students should be able to opt out of uses of student data for commercial purposes unrelated to education or military recruitment.
  4. When surveys are administered to students through schools, data practices should be transparent, students and families should be informed as to any commercial purposes of surveys before they are administered, and there should be compliance with other obligations under the Protection of Pupil Rights Amendment (PPRA)."
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June 9th, 2018

Ego

PAVEMENT, NURSING, MISSILES

Algorithm Tips, a compilation of "potentially newsworthy algorithms" for journalists and researchers

DANIEL TRIELLI, JENNIFER STARK, and NICK DIAKOPOLOUS and Northwestern’s Computational Journalism Lab created this searchable, non-comprehensive list of algorithms in use at the federal, state, and local levels. The “Methodology” page explains the data-scraping process, then the criteria for inclusion:

“We formulated questions to evaluate the potential newsworthiness of each algorithm:

Can this algorithm have a negative impact if used inappropriately?
Can this algorithm raise controversy if adopted?
Is the application of this algorithm surprising?
Does this algorithm privilege or harm a specific subset of people?
Does the algorithm have the potential of affecting a large population or section of the economy?

If the answers for any of these questions were 'yes', the algorithm could be included on the list."

Link popup: yes. The list includes a huge range of applications, from a Forest Service algorithmic ranking of invasive plants, to an intelligence project meant to discover “significant societal events” from public data—and pavement, nursing, and missiles too.

  • Nick Diakopolous also wrote a guide for journalists on investigating algorithms: “Auditing algorithms is not for the faint of heart. Information deficits limit an auditor’s ability to sometimes even know where to start, what to ask for, how to interpret results, and how to explain the patterns they’re seeing in an algorithm’s behavior. There is also the challenge of knowing and defining what’s expected of an algorithm, and how those expectations may vary across contexts.” Link popup: yes.
  • The guide is a chapter from the upcoming Data Journalism Handbook popup: yes. One of the partner organizations behind the guide has a website of advice and stories popup: yes from the data-reporting trenches, such as this popup: yes on trying to figure out prescription drug deaths: “The FDA literally found three different ways to spell ASCII. This was a sign of future surprises.”
 Full Article