Human Centered Data Science (Fall 2018)/Schedule: Difference between revisions

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* Nissenbaum, Helen, [https://crypto.stanford.edu/portia/papers/RevnissenbaumDTP31.pdf Privacy as Contextual Integrity]
* Nissenbaum, Helen, [https://crypto.stanford.edu/portia/papers/RevnissenbaumDTP31.pdf Privacy as Contextual Integrity]
* National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. [https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html ''The Belmont Report.''] U.S. Department of Health and Human Services, 1979.
* National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. [https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html ''The Belmont Report.''] U.S. Department of Health and Human Services, 1979.
* Bethan Cantrell, Javier Salido, and Mark Van Hollebeke (2016). ''[http://datworkshop.org/papers/dat16-final38.pdf Industry needs to embrace data ethics: Here's how it could be done]''. Workshop on Data and Algorithmic Transparency (DAT'16). http://datworkshop.org/
* Javier Salido (2012). ''[http://download.microsoft.com/download/D/1/F/D1F0DFF5-8BA9-4BDF-8924-7816932F6825/Differential_Privacy_for_Everyone.pdf Differential Privacy for Everyone].'' Microsoft Corporation Whitepaper.
* Markham, Annette and Buchanan, Elizabeth. [https://aoir.org/reports/ethics2.pdf ''Ethical Decision-Making and Internet Researchers.''] Association for Internet Research, 2012.
* Markham, Annette and Buchanan, Elizabeth. [https://aoir.org/reports/ethics2.pdf ''Ethical Decision-Making and Internet Researchers.''] Association for Internet Research, 2012.
* Hill, Kashmir. [https://www.forbes.com/sites/kashmirhill/2014/06/28/facebook-manipulated-689003-users-emotions-for-science/#6a01653e197c ''Facebook Manipulated 689,003 Users' Emotions For Science.''] Forbes, 2014.
* Hill, Kashmir. [https://www.forbes.com/sites/kashmirhill/2014/06/28/facebook-manipulated-689003-users-emotions-for-science/#6a01653e197c ''Facebook Manipulated 689,003 Users' Emotions For Science.''] Forbes, 2014.
* Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. [http://www.pnas.org/content/111/24/8788.full ''Experimental evidence of massive-scale emotional contagion through social networks.''] PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
* Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock [http://www.pnas.org/content/111/24/8788.full ''Experimental evidence of massive-scale emotional contagion through social networks.''] PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
* Barbaro, Michael and Zeller, Tom. [http://query.nytimes.com/gst/abstract.html?res=9E0CE3DD1F3FF93AA3575BC0A9609C8B63&legacy=true ''A Face Is Exposed for AOL Searcher No. 4417749.''] New York Times, 2008.
* Barbaro, Michael and Zeller, Tom. [http://query.nytimes.com/gst/abstract.html?res=9E0CE3DD1F3FF93AA3575BC0A9609C8B63&legacy=true ''A Face Is Exposed for AOL Searcher No. 4417749.''] New York Times, 2008.
* Zetter, Kim. [https://www.wired.com/2012/06/wmw-arvind-narayanan/ ''Arvind Narayanan Isn’t Anonymous, and Neither Are You.''] WIRED, 2012.
* Zetter, Kim. [https://www.wired.com/2012/06/wmw-arvind-narayanan/ ''Arvind Narayanan Isn’t Anonymous, and Neither Are You.''] WIRED, 2012.

Revision as of 23:38, 30 September 2018

This page is a work in progress.


Week 1: September 27

Day 1 plan

Day 1 slides

Introduction to Human Centered Data Science
What is data science? What is human centered? What is human centered data science?
Assignments due
Agenda
  • Syllabus review
  • Pre-course survey results
  • What do we mean by data science?
  • What do we mean by human centered?
  • How does human centered design relate to data science?
  • Looking ahead: Week 2 assignments and topics


Readings assigned
Homework assigned
  • Reading reflection
Resources




Week 2: October 4

Day 2 plan


Ethical considerations
privacy, informed consent and user treatment


Assignments due
  • Week 1 reading reflection
Agenda
  • Intro to assignment 1: Data Curation
  • A brief history of research ethics
  • Guest lecture: Javier Salido and Mark van Hollebeke, "A Practitioners View of Privacy & Data Protection"
  • Guest lecture: Javier Salido, "Differential Privacy"
  • Contextual Integrity in data science
  • Week 2 reading reflection


Readings assigned


Homework assigned
Resources




Week 3: October 11

Day 3 plan


Reproducibility and Accountability
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 2 reading reflection
Agenda
  • Six Provocations for Big Data: Review & Reflections
  • A primer on copyright, licensing, and hosting for code and data
  • Introduction to replicability, reproducibility, and open research
  • Reproducibility case study: fivethirtyeight.com
  • Group activity: assessing reproducibility in data journalism
  • Overview of Assignment 1: Data curation


Readings assigned
  • Read: Duarte, N., Llanso, E., & Loup, A. (2018). Mixed Messages? The Limits of Automated Social Media Content Analysis. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 106. PDF: http://proceedings.mlr.press/v81/duarte18a.html
Homework assigned
  • Reading reflection
Resources
Examples of well-documented open research projects
Examples of not-so-well documented open research projects





Week 4: October 18

Day 4 plan


Interrogating datasets
bias in data; best practices for selecting, describing, and implementing training data


Assignments due
  • Reading reflection
  • A1: Data curation
Agenda
  • Final project: Goal, timeline, and deliverables.
  • Overview of assignment 2: Bias in data
  • Reading reflections review
  • Sources of bias in datasets
  • Introduction to assignment 2: Bias in data
  • Sources of bias in data collection and processing
  • In-class exercise: assessing bias in training data


Readings assigned
Homework assigned
  • Reading reflection
  • A2: Bias in data


Resources




Week 5: October 25

Day 5 plan


Introduction to mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice


Assignments due
  • Reading reflection


Agenda
  • Assignment 1 review & reflection
  • Week 4 reading reflection discussion
  • Survey of qualitative research methods
  • Mixed-methods case study #1: The Wikipedia Gender Gap: causes & consequences
  • In-class activity: Automated Gender Recognition scenarios
  • Introduction to ethnography
  • Ethnographic research case study: Structured data on Wikimedia Commons
  • Introduction to crowdwork
  • Overview of Assignment 3: Crowdwork ethnography


Readings assigned
Homework assigned


Resources





Week 6: November 1

Day 6 plan


Interrogating algorithms
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection
  • A2: Bias in data
Agenda
  • Reading reflections
  • Ethical implications of crowdwork
  • Algorithmic transparency, interpretability, and accountability
  • Auditing algorithms
  • In-class activity: auditing the Perspective API


Readings assigned


Homework assigned
  • Reading reflection


Resources





Week 7: November 8

Day 7 plan

Critical approaches to data science
power, data, and society; ethics of crowdwork


Assignments due
  • Reading reflection
  • A3: Crowdwork ethnography


Agenda
  • Guest lecture: Rochelle LaPlante


Readings assigned (read both, reflect on one)
  • TBD
Homework assigned


Resources





Week 8: November 15

Day 8 plan


Human-centered algorithm design
algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems


Assignments due
  • Reading reflection


Agenda
  • Final project overview & examples
  • Guest Lecture: Kelly Franznick, Blink UX
  • Reading reflections
  • Human-centered algorithm design
  • design process
  • user-driven evaluation
  • design patterns & anti-patterns


Readings assigned
Homework assigned
  • Reading reflection
Resources





Week 9: November 22 (No Class Session)

Day 9 plan

Data science for social good
Community-based and participatory approaches to data science; Using data science for society's benefit
Assignments due
  • Reading reflection
  • A4: Final project plan
Agenda
  • Reading reflections discussion
  • Feedback on Final Project Plans
  • Guest lecture: Steven Drucker (Microsoft Research)
  • UI patterns & UX considerations for ML/data-driven applications
  • Final project presentation: what to expect
  • In-class activity: final project peer review


Readings assigned (Read and reflect on one only)
  • Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). Democratizing Data Science: The Community Data Science Workshops and Classes. In N. Jullien, S. A. Matei, & S. P. Goggins (Eds.), Big Data Factories: Scientific Collaborative approaches for virtual community data collection, repurposing, recombining, and dissemination. New York, New York: Springer Nature. [Preprint/Draft PDF]
  • Berney, Rachel, Bernease Herman, Gundula Proksch, Hillary Dawkins, Jacob Kovacs, Yahui Ma, Jacob Rich, and Amanda Tan. Visualizing Equity: A Data Science for Social Good Tool and Model for Seattle. Data Science for Social Good Conference, September 2017, Chicago, Illinois USA (2017).
Homework assigned
  • Reading reflection
Resources





Week 10: November 29

Day 10 plan


User experience and big data
Design considerations for machine learning applications; human centered data visualization; data storytelling


Assignments due
  • Reading reflection


Agenda
  • Reading reflections discussion
  • Feedback on Final Project Plans
  • Guest lecture: Steven Drucker (Microsoft Research)
  • UI patterns & UX considerations for ML/data-driven applications
  • Final project presentation: what to expect
  • In-class activity: final project peer review


Readings assigned


Homework assigned
  • Reading reflection
  • A5: Final presentation
Resources




Week 11: December 6

Day 11 plan

Final presentations
course wrap up, presentation of student projects


Assignments due
  • Reading reflection
  • A5: Final presentation


Agenda
  • Student final presentations
  • Course wrap-up


Readings assigned
  • none!
Homework assigned
  • none!
Resources
  • one




Week 12: Finals Week (No Class Session)

  • NO CLASS
  • A6: FINAL PROJECT REPORT DUE BY 11:59PM on Sunday, December 9
  • LATE PROJECT SUBMISSIONS NOT ACCEPTED.