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

From CommunityData
Line 247: Line 247:
* Christian Sandvig, Kevin Hamilton, Karrie Karahalios, Cedric Langbort (2014/05/22) ''[http://www-personal.umich.edu/~csandvig/research/Auditing%20Algorithms%20--%20Sandvig%20--%20ICA%202014%20Data%20and%20Discrimination%20Preconference.pdf Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms].'' Paper presented to "Data and Discrimination: Converting Critical Concerns into Productive Inquiry," a preconference at the 64th Annual Meeting of the International Communication Association. May 22, 2014; Seattle, WA, USA.  
* Christian Sandvig, Kevin Hamilton, Karrie Karahalios, Cedric Langbort (2014/05/22) ''[http://www-personal.umich.edu/~csandvig/research/Auditing%20Algorithms%20--%20Sandvig%20--%20ICA%202014%20Data%20and%20Discrimination%20Preconference.pdf Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms].'' Paper presented to "Data and Discrimination: Converting Critical Concerns into Productive Inquiry," a preconference at the 64th Annual Meeting of the International Communication Association. May 22, 2014; Seattle, WA, USA.  
* Shahriari, K., & Shahriari, M. (2017). ''[https://ethicsinaction.ieee.org/ IEEE standard review - Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems].'' Institute of Electrical and Electronics Engineers  
* Shahriari, K., & Shahriari, M. (2017). ''[https://ethicsinaction.ieee.org/ IEEE standard review - Ethically aligned design: A vision for prioritizing human wellbeing with artificial intelligence and autonomous systems].'' Institute of Electrical and Electronics Engineers  
* ACM US Policy Council ''[https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf Statement on Algorithmic Transparency and Accountability].'' January 2017.
* ''[https://futureoflife.org/ai-principles/ Asilomar AI Principles].'' Future of Life Institute, 2017.
* Friedman, B., & Nissenbaum, H. (1996). ''[https://www.vsdesign.org/publications/pdf/64_friedman.pdf Bias in Computer Systems]''. ACM Trans. Inf. Syst., 14(3), 330–347.
* Friedman, B., & Nissenbaum, H. (1996). ''[https://www.vsdesign.org/publications/pdf/64_friedman.pdf Bias in Computer Systems]''. ACM Trans. Inf. Syst., 14(3), 330–347.
* ACM US Policy Council ''https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf Statement on Algorithmic Transparency and Accountability].'' January 2017.
* ''[https://futureoflife.org/ai-principles/ Asilomar AI Principles].'' Future of Life Institute, 2017.
* Diakopoulos, N. (2014). Algorithmic accountability reporting: On the investigation of black boxes. Tow Center for Digital Journalism, 1–33. https://doi.org/10.1002/ejoc.201200111
* Diakopoulos, N. (2014). Algorithmic accountability reporting: On the investigation of black boxes. Tow Center for Digital Journalism, 1–33. https://doi.org/10.1002/ejoc.201200111
* Hill, Kashmir. ''[https://gizmodo.com/facebook-figured-out-my-family-secrets-and-it-wont-tel-1797696163 Facebook figured out my family secrets, and it won't tell me how].'' Engadget, 2017.
* Hill, Kashmir. ''[https://gizmodo.com/facebook-figured-out-my-family-secrets-and-it-wont-tel-1797696163 Facebook figured out my family secrets, and it won't tell me how].'' Engadget, 2017.

Revision as of 23:52, 31 October 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

Day 3 slides

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
Homework assigned
  • Reading reflection
Resources


Assignment 1 Data curation resources





Week 4: October 18

Day 4 plan

Day 4 slides

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


Assignments due
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 (Read both, reflect on one)
Homework assigned


Resources




Week 5: October 25

Day 5 plan

Day 5 slides

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


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 (Read both, reflect on one)


Homework assigned


Qualitative research methods resources
Wikipedia gender gap research resources
Crowdwork research resources





Week 6: November 1

Day 6 plan


Interrogating algorithms
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
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)
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
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.