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

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;Agenda
 
;Agenda
* ''coming soon''
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* Filling out course evaluation
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* Week 8 in-class activity report out
* Reading reflections discussion
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* End of quarter logistics
* Feedback on Final Project Plans
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* Final project presentations and reports
* UI patterns & UX considerations for ML/data-driven applications
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* Guest lecture: Rich Caruana, Microsoft Research
* Final project presentation: what to expect
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* In-class activity (InterpretML): Harsha Nori, Microsoft
* In-class activity: final project peer review
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;Homework assigned
 
;Homework assigned
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;Resources
 
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* Rich Caruana, Harsha Nori, Samuel Jenkins, Paul Koch, Ester de Nicolas. 2019. ''InterpretML software toolkit'' ([https://github.com/interpretml/interpret github repo], [https://www.microsoft.com/en-us/research/blog/creating-ai-glass-boxes-open-sourcing-a-library-to-enable-intelligibility-in-machine-learning/ blog post])
* Daniela Aiello, Lisa Bates, et al. [https://shelterforce.org/2018/08/22/eviction-lab-misses-the-mark/ Eviction Lab Misses the Mark], ShelterForce, August 2018. 
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* Partnership on AI, 2019 ''[https://www.partnershiponai.org/report-on-machine-learning-in-risk-assessment-tools-in-the-u-s-criminal-justice-system/ Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System].''  
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*Ethical OS ''[https://ethicalos.org/wp-content/uploads/2018/08/Ethical-OS-Toolkit-2.pdf Toolkit]'' and ''[https://ethicalos.org/wp-content/uploads/2018/08/EthicalOS_Check-List_080618.pdf Risk Mitigation Checklist]''. EthicalOS.org.
 
 
* Morgan, J. T., 2019. ''[https://figshare.com/articles/Ethical_Human_Centered_AI/8044553 Ethical and Human-centered AI at Wikimedia]''. Wikimedia Research 2030​.
 
* Morgan, J. T., 2019. ''[https://figshare.com/articles/Ethical_Human_Centered_AI/8044553 Ethical and Human-centered AI at Wikimedia]''. Wikimedia Research 2030​.
  

Revision as of 23:20, 21 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
  • Filling out course evaluation
  • Week 8 in-class activity report out
  • End of quarter logistics
  • Final project presentations and reports
  • Guest lecture: Rich Caruana, Microsoft Research
  • In-class activity (InterpretML): Harsha Nori, Microsoft


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.