who owns faculty data?: fairness and transparency in ucla's new academic hr system
TRANSCRIPT
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Who Owns Faculty Data? Fairness & Transparency in
UCLA’s New Academic HR System
C H LO E R E Y N O L D S & H E AT H E R S M A L L
U C L A , I T S E R V I C E S
I CO N F E R E N C E
3 . 2 6 . 1 5
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Topics
History of the project Issues a) Data Ownership b) Data Access/Privacy c) Data & Truth d) Data Representa@on
Q & A
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Opus History Our Role ◦ Role – analysts on IT team building a new faculty informa@on system (Opus)
System Features ◦ Academic Personnel (AP) workflow, Curriculum Vitae data, Repor@ng
History ◦ Faculty have called for improvements to the AP review process since 1988.
◦ The AP process has been es@mated at $10M/year and takes up to 300 days.
◦ In 2010 a joint Academic Senate/Administra@on taskforce report, provided the impetus to build an electronic academic review system.
◦ Beta release in Dec. 2014; mul@ple major releases over the next year.
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A. Data Ownership
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Data Ownership Problem
◦ The value of the Opus data depends on a consistent set of expecta@ons about data fidelity, security, and access.
Mi@ga@on
◦ Iden@fy data stewards for each data element
◦ Display data steward informa@on to Opus users.
◦ Error correc@on begins with the authorita@ve source.
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Data Ownership
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But defining ownership is hard A tale of two salaries…
…And what about
publica@ons,
degrees,
community
service ac@vi@es?
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Lesson # 1
In aggrega@ng data from various sources, you need to understand the story of the data in each context Who is “authoritative” is context-specific, rather than enterprise-‐specific
All of this needs to be factored in
for every single data element.
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B. Data Access / Privacy
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Access & Use Problem ◦ Balancing the business needs of the organiza@on, the public’s right
to know, and faculty privacy & security.
Mi@ga@on ◦ Granular visibility sedngs & transparency about usage
◦ Public visibility for minimal set of data
◦ Private visibility for data about works in progress ◦ Access to detailed data limited to those with a business need
◦ Review process for reques@ng data for new uses
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PrioriCzing access & use
…when does faculty privacy trump the public’s right to
know?
…when does business need trump faculty privacy?
What does the public have the right to
know?
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Access & Use
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Lesson #2
Fear of change occurs at every level of projects and organizations
Pudng things ‘under the microscope’ and scrutinizing practices and data can create a sense of exposure and vulnerability. Stakeholders often have overlapping and/or competing interests and incentives around how data are collected, used, and interpreted (Borgman, 2013).
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C. Data & Truth
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Case Reviewer
Candidate
Researcher
Chair, Dean or other Administrator
Committee Member
Opus will be used by people in several different roles
External Reviewers Staff Public
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Data Sources Data will come from many sources ◦ Internal (campus) systems ◦ External systems ◦ Data entry
For example ◦ From the student registrar system: course level, course @tle, number of instructors, term, enrollment
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MulCple NarraCves Problem: Mul@ple narra@ves ◦ Data elements comes in from different sources ◦ Updated and augmented by different par@es ◦ Viewed by various user groups ◦ Viewed for different purposes than they were collected for
Mi@ga@on: Transparency, Annota@ons, Educa@on ◦ Data provenance transparency, annota@ons, data literacy educa@on
Example ◦ Enrollment: as indicator of level of faculty work, as a financial metric, as a measure of student body size
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RepresenCng MulCple Truths: AnnotaCons and DescripCons
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RepresenCng MulCple Truths: AnnotaCons and DescripCons
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D. Data RepresentaCon
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RepresentaCon Issues Problem: Reducing Informa@on to Data points ◦ Workload reduced to course size, student evalua@on ra@ngs, number of publica@ons, amount of grant money, etc.
Mi@ga@on: How to represent mul@ple truths ◦ Annota@on and descrip@ons ◦ Data literacy educa@on Examples ◦ Enrollment -‐ co-‐teaching/seniority, cross-‐lis@ng, exchange & extension students, theater ◦ Publica@ons -‐ publica@on pakern variance by discipline, early-‐cited ar@cles emphasis, etc.
◦ Gray lines disadvantage the modest, but seem “fair”
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RepresentaCon Issues Categories ◦ Publica@on Types ◦ Obituaries, Interviews (about me, by me, or of me)
◦ Degree Types ◦ translate?, when to merge, maintenance, crosswalks
Terminology ◦ Awards, etc.
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Lesson # 3 SemanCcs maRer
Seman@cs are @ed to iden@ty and cultural associa@on Who decides what things are called? How do you come to a compromise when stakeholders disagree?
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Overarching Lessons Learned
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Lesson # 4 Data projects can expose & exacerbate
…policy gaps, inconsistencies in prac@ce, long-‐standing disagreements, old habits.
One of the main reasons this project was ini@ated was because campus iden@fied the AP process to be in need of re-‐engineering. We were akemp@ng to resolve transac@onal inefficiencies, but those proved to be a symptom of larger, more complex issues.
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“Can’t you just go build the system?”
Why do technologists find themselves wrangling with what are essen@ally policy/legal/ideological issues? It’s incumbent on the technical team to educate stakeholders about the complexity.
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Q & A Live System h"ps://opus.ucla.edu
Opus FAQ h"ps://opus.ucla.edu/public/FAQ.shtml
Opus Privacy h"ps://opus.ucla.edu/public/privacy.shtml
Original Charge h"ps://www.apo.ucla.edu/ini<a<ves/opus/charge
Heather Small [email protected] . Chloe Reynolds [email protected]