Difference between revisions of "Human Centered Data Science (Fall 2019)/Schedule"

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* 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.
 
* 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., Friedler, S., Arenas, M., Barocas, S., Hay, M., Howe, B., … Zevenbergen, B. (2018). ''[http://www.fatml.org/resources/principles-for-accountable-algorithms Principles for Accountable Algorithms and a Social Impact Statement for Algorithms].'' Fatml.Org 2018.  
 
* Diakopoulos, N., Friedler, S., Arenas, M., Barocas, S., Hay, M., Howe, B., … Zevenbergen, B. (2018). ''[http://www.fatml.org/resources/principles-for-accountable-algorithms Principles for Accountable Algorithms and a Social Impact Statement for Algorithms].'' Fatml.Org 2018.  
* Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
 
*Fabien Girardin. ''[https://medium.com/@girardin/experience-design-in-the-machine-learning-era-e16c87f4f2e2 Experience design in the machine learning era].'' Medium, 2016.
 
* Xavier Amatriain and Justin Basilico. ''[https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 Netflix Recommendations: Beyond the 5 stars].'' Netflix Tech Blog, 2012.
 
* Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. ''[https://pure.tue.nl/ws/files/3484177/724656348730405.pdf Explaining the user experience of recommender systems].'' User Modeling and User-Adapted Interaction 22, 4-5 (October 2012), 441-504. DOI=http://dx.doi.org/10.1007/s11257-011-9118-4
 
* Patrick Austin, ''[https://gizmodo.com/facebook-google-and-microsoft-use-design-to-trick-you-1827168534 Facebook, Google, and Microsoft Use Design to Trick You Into Handing Over Your Data, New Report Warns].'' Gizmodo, 6/18/2018
 
* Cremonesi, P., Elahi, M., & Garzotto, F. (2017). ''[https://core.ac.uk/download/pdf/74313597.pdf User interface patterns in recommendation-empowered content intensive multimedia applications].'' Multimedia Tools and Applications, 76(4), 5275-5309.
 
 
* Morgan, J. 2016. ''[https://meta.wikimedia.org/wiki/Research:Evaluating_RelatedArticles_recommendations Evaluating Related Articles recommendations]''. Wikimedia Research.
 
* Morgan, J. 2016. ''[https://meta.wikimedia.org/wiki/Research:Evaluating_RelatedArticles_recommendations Evaluating Related Articles recommendations]''. Wikimedia Research.
 
* Morgan, J. 2017. ''[https://meta.wikimedia.org/wiki/Research:Comparing_most_read_and_trending_edits_for_Top_Articles_feature Comparing most read and trending edits for the top articles feature]''. Wikimedia Research.
 
* Morgan, J. 2017. ''[https://meta.wikimedia.org/wiki/Research:Comparing_most_read_and_trending_edits_for_Top_Articles_feature Comparing most read and trending edits for the top articles feature]''. Wikimedia Research.
 
*Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. ''[https://md.ekstrandom.net/research/pubs/listcmp/listcmp.pdf User perception of differences in recommender algorithms].'' In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14).
 
*Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. ''[https://md.ekstrandom.net/research/pubs/listcmp/listcmp.pdf User perception of differences in recommender algorithms].'' In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14).
* Michael D. Ekstrand and Martijn C. Willemsen. 2016. ''[https://md.ekstrandom.net/research/pubs/behaviorism/BehaviorismIsNotEnough.pdf Behaviorism is Not Enough: Better Recommendations through Listening to Users].'' In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16).
 
 
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Revision as of 03:50, 14 November 2019

This page is a work in progress.


Week 1: September 26

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?
  • In-class activity
  • Intro to assignment 1: Data Curation
Homework assigned
  • Read and reflect on both:
Resources




Week 2: October 3

Reproducibility and Accountability
data curation, preservation, documentation, and archiving; best practices for open scientific research
Assignments due
  • Week 1 reading reflection
  • A1: Data curation
Agenda
  • Reading reflection discussion
  • Assignment 1 review & reflection
  • A primer on copyright, licensing, and hosting for code and data
  • Introduction to replicability, reproducibility, and open research
  • In-class activity
  • Intro to assignment 2: Bias in data
Homework assigned
Resources




Week 3: October 10

Interrogating datasets
causes and consequences of bias in data; best practices for selecting, describing, and implementing training data
Assignments due
  • Week 2 reading reflection
Agenda
  • Reading reflection review
  • Sources and consequences of bias in data collection, processing, and re-use
  • In-class activity
Homework assigned
  • Read both, reflect on one:
Resources




Week 4: October 17

Introduction to qualitative and mixed-methods research
Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing
Assignments due
  • Reading reflection
  • A2: Bias in data
Agenda
  • Reading reflection reflection
  • Overview of qualitative research
  • Introduction to ethnography
  • In-class activity: explaining art to aliens
  • Mixed methods research and data science
  • An introduction to crowdwork
  • Overview of assignment 3: Crowdwork ethnography
Homework assigned
Resources





Week 5: October 24

Research ethics for big data
privacy, informed consent and user treatment
Assignments due
  • Reading reflection
Agenda
  • Reading reflection review
  • Guest lecture
  • A2 retrospective
  • Final project deliverables and timeline
  • A brief history of research ethics in the United States


Homework assigned
  • Read and reflect: Gray, M. L., & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Eamon Dolan Books. (PDF available on Canvas)
Resources




Week 6: October 31

Data science and society
power, data, and society; ethics of crowdwork
Assignments due
  • Reading reflection
  • A3: Crowdwork ethnography
Agenda
  • Reading reflections
  • Assignment 3 review
  • Guest lecture: Stefania Druga
  • In-class activity
  • Introduction to assignment 4: Final project proposal
Homework assigned
  • Read both, reflect on one:
Resources




Week 7: November 7

Human centered machine learning
algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits
Assignments due
  • Reading reflection
  • A4: Project proposal
Agenda
  • Reading reflection review
  • Algorithmic transparency, interpretability, and accountability
  • Auditing algorithms
  • In-class activity
  • Introduction to assignment 5: Final project proposal
Homework assigned
Resources




Week 8: November 14

User experience and data science
algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems
Assignments due
  • Reading reflection
  • A5: Final project plan
Agenda
  • coming soon
Homework assigned
Resources




Week 9: November 21

Data science in context
Doing human centered datascience in product organizations; communicating and collaborating across roles and disciplines; HCDS industry trends and trajectories
Assignments due
  • Reading reflection
Agenda
  • coming soon
Homework assigned
Resources




Week 10: November 28 (No Class Session)

Assignments due
  • Reading reflection
Homework assigned
Resources




Week 11: December 5

Final presentations
presentation of student projects, course wrap up
Assignments due
  • Reading reflection
  • A5: Final presentation
Readings assigned
  • NONE
Homework assigned
  • NONE
Resources
  • NONE




Week 12: Finals Week (No Class Session)

  • NO CLASS
  • A7: FINAL PROJECT REPORT DUE BY 5:00PM on Tuesday, December 10 via Canvas
  • LATE PROJECT SUBMISSIONS NOT ACCEPTED.