Establishing an Ethics Framework for Predictive Analytics in Higher EducationCyber Summit 2016, Banff

Stephen Childs, Institutional AnalystOctober 27, 2016

Disclamer

The content of this presentation represents my views only. and not that of my employer, the University of Calgary.

I am not qualified to accurately describe University of Calgary policy in the areas discussed in this talk.

Please contact the University if you have policy questions.

Data Abundance in Higher Education

3

Big Data, Big Problems

Advancing technology— Better data collection— Handle more data— Apply algorithms to data

We know more about our students Can make predictions about their behavior Very few guidelines about this practice

Solutions

Develop an ethics framework around student data. Build on existing guidelines. Build on the norms of service to students Do this now while these practices are new.

Outline

Introduction Students and Student Data Predictive Analytics Existing Frameworks Next Steps

About Me

About My Office

Office of Institutional Analysis https://oia.ucalgary.ca/

What OIA Does

About the University

Students

Student Data

Application Student Information System LMS Unicard Surveys Residence Facilities Awarding Degrees

Grades USRI IT usage Others…

Student Data

Students can opt out of some data collection, but not all Student give us their data because they trust us We need to deserve that trust!

— Respect student privacy— Transparency about how data is used— Accountability— Consultation— Consider the Consequences

Privacy

Access to Data

Transparency and Accountability

Internalize norms is not enough! How Universities use data should be known

— We aren’t corporations with competitive secrets— We need to set up ways to report and share

We need to be able describe what happened! Log events Version control your software Develop reporting methods

Consultation

Consider the Consequences

Moving from institutional decision making to acting on individual data

Lathe of Heaven – a mad social scientist

Predictive Analytics

Best Practices using Predictive Analytics

Have to carefully present information to students— Present a positive outlook— Don’t personalize it – talk about a group of similar

students. The factors in the model may be less deterministic than

unobserved factors. Difference between causality and correlation. Beware the self-fulfilling prophecy

Cathy O’Neil

@mathbabe, mathbabe.org Mathematician, former hedge-fund

quant

Weapons of Math Destruction

Three factors make a model a WMD:— Is the participant aware of the model? Is the model

opaque or invisible?— Does the model work against the participant’s interest? Is

it unfair? Does it create feedback loops?— Can the model scale?

Student Data Principles

http://studentdataprinciples.org/ Purpose and use of student data Timely access to data Data should not replace professional judgement. Data governance, security, breach notification

Student Data Pledge

http://www.edtechmagazine.com/k12/article/2015/03/protect-personal-student-information-pair-organizations-recommends-commitment

Don’t sell student data, use data to target ads, or profile students for non-educational purposes

Don’t collect more information or retain information longer than necessary.

Do disclose how, what and why

uCalgary Data Rules

Freedom of Information and Privacy Act (1999)— Students must be able to correct own info— University must provide own info upon confirmation of ID

Categories of Data Confidentiality Research Ethics Boards

— Data collection for University operations does not generally fall under REB jurisdiction.

Financial Modeler’s Manifesto

https://www.wilmott.com/financial-modelers-manifesto/ Emanuel Derman and Paul Wilmott – January 7, 2009 The Modelers’ Hippocratic Oath

— I will remember that I didn’t make the world, and it doesn’t satisfy my equations.— Though I will use models boldly to estimate value, I will not be overly impressed

by mathematics.— I will never sacrifice reality for elegance without explaining why I have done so.— Nor will I give the people who use my model false comfort about its accuracy.

Instead, I will make explicit its assumptions and oversights.— I understand that my work may have enormous effects on society and the

economy, many of them beyond my comprehension.

Responsible Use of Student Data in Higher Education

http://gsd.su.domains/ Opportunity to understand student learning and enhance

educational attainment. New questions about the ethical collection, use, and sharing

of information. Commitments to honor the integrity, discretion, and humanity

of students. Improve practice in light of accumulating information and

knowledge.

Maciej Cegłowski

https://pinboard.in and @pinboard http://idlewords.com/talks/ Two talks on Data in particular:

— http://idlewords.com/talks/deep_fried_data.htm— http://idlewords.com/talks/haunted_by_data.htm

Basic Framework

Safeguard Student Privacy— Vendors; Monetizing Data

Strong internal norms around data Consider and Measure Outcomes Work with Data Owners and Stewards Responsibility to Educate Consult with Students and Stakeholders Data should have a clear purpose

Next Steps

Write down your norms/expectations for working with Student data

Set up a discussion with your co-workers about it. Seek out others who perform a similar role and discuss it. Discuss with the Student Data Steward at your institution. Send me your comments!

Continue the Conversation

Follow me on twitter: @sechilds Stephen.Childs@ucalgary.ca or sechilds@gmail.com https://oia.ucalgary.ca/Contact https://www.meetup.com/PyData-Calgary/