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

Hickey, Walt. The Bechdel Test: Checking Our Work. FiveThirtyEight, 2014.
J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), Altmetrics: A manifesto, 26 October 2010.

Assignment 1 Data curation resources

Chapter 2 "Assessing Reproducibility" and Chapter 3 "The Basic Reproducible Workflow Template" from The Practice of Reproducible Research University of California Press, 2018.
sample code for API calls (view the notebook, download the notebook).
See the datasets page for examples of well-documented and not-so-well documented open datasets.

Week 3: October 10

Day 3 plan

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

HCDS (Fall 2019)/Day 3 plan

Readings assigned (Read both, reflect on one)

Wang, Tricia. Why Big Data Needs Thick Data. Ethnography Matters, 2016.
Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao (Ken) Wang, and Brent Hecht. 2015. Turkers, Scholars, "Arafat" and "Peace": Cultural Communities and Algorithmic Gold Standards. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '15)

Homework assigned

Reading reflection

Resources

Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016). Social data: Biases, methodological pitfalls, and ethical boundaries.
Brian N Larson. 2017. Gender as a Variable in Natural-Language Processing: Ethical Considerations. EthNLP, 3: 30–40.
Bender, E. M., & Friedman, B. (2018). Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science. To appear in Transactions of the ACL.
Isaac L. Johnson, Yilun Lin, Toby Jia-Jun Li, Andrew Hall, Aaron Halfaker, Johannes Schöning, and Brent Hecht. 2016. Not at Home on the Range: Peer Production and the Urban/Rural Divide. CHI '16. DOI: https://doi.org/10.1145/2858036.2858123
Leo Graiden Stewart, Ahmer Arif, A. Conrad Nied, Emma S. Spiro, and Kate Starbird. 2017. Drawing the Lines of Contention: Networked Frame Contests Within #BlackLivesMatter Discourse. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 96 (December 2017), 23 pages. DOI: https://doi.org/10.1145/3134920
Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. No country for old members: user lifecycle and linguistic change in online communities. In Proceedings of the 22nd international conference on World Wide Web (WWW '13). ACM, New York, NY, USA, 307-318. DOI: https://doi.org/10.1145/2488388.2488416

Week 4: October 17

Day 4 plan

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

Assignments due

Week 3 reading reflection
A2: Bias in data

Agenda

HCDS (Fall 2019)/Day 4 plan

Homework assigned

Read and reflect: Barocas, Solan and Nissenbaum, Helen. Big Data's End Run around Anonymity and Consent. In Privacy, Big Data, and the Public Good. 2014.
A3: Crowdwork ethnography

Qualitative research methods resources

Ladner, S. (2016). Practical ethnography: A guide to doing ethnography in the private sector. Routledge.
Spradley, J. P. (2016). The ethnographic interview. Waveland Press.
Spradley Participant Observation FIXME
Eriksson, P., & Kovalainen, A. (2015). Ch 12: Ethnographic Research. In Qualitative methods in business research: A practical guide to social research. Sage.
Usability.gov, System usability scale.
Nielsen, Jakob (2000). Why you only need to test with five users. nngroup.com.

Wikipedia gender gap research resources

Crowdwork research resources

WeArDynamo contributors. How to be a good requester and Guidelines for Academic Requesters. Wearedynamo.org

Week 5: October 24

Day 5 plan

Research ethics for big data: privacy, informed consent and user treatment

Assignments due

Week 4 reading reflection

Agenda

HCDS (Fall 2019)/Day 5 plan

Homework assigned

Read and reflect: Mary Gray, Ghost Work FIXME
Final project proposal FIXME

Resources

National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report. U.S. Department of Health and Human Services, 1979.
Bethan Cantrell, Javier Salido, and Mark Van Hollebeke (2016). 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). Differential Privacy for Everyone. Microsoft Corporation Whitepaper.
Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
Hill, Kashmir. Facebook Manipulated 689,003 Users' Emotions For Science. Forbes, 2014.
Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock 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. A Face Is Exposed for AOL Searcher No. 4417749. New York Times, 2008.
Zetter, Kim. Arvind Narayanan Isn’t Anonymous, and Neither Are You. WIRED, 2012.
Gray, Mary. When Science, Customer Service, and Human Subjects Research Collide. Now What? Culture Digitally, 2014.
Tene, Omer and Polonetsky, Jules. Privacy in the Age of Big Data. Stanford Law Review, 2012.
Dwork, Cynthia. Differential Privacy: A survey of results. Theory and Applications of Models of Computation , 2008.
Hsu, Danny. Techniques to Anonymize Human Data. Data Sift, 2015.

Week 6: October 31

Day 6 plan

Data science and society: power, data, and society; ethics of crowdwork

Assignments due

Reading reflection
A3: Crowdwork ethnography

Agenda

HCDS (Fall 2019)/Day 7 plan

Homework assigned

Read both, reflect on one:

Baumer, E. P. S. (2017). Toward human-centered algorithm design. Big Data & Society.
Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the People: The Role of Humans in Interactive Machine Learning. AI Magazine, 35(4), 105.

A4: Final project plan

Resources

Week 7: November 7

Day 7 plan

Human centered machine learning: algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits

Assignments due

Reading reflection
A4: Project proposal

Agenda

HCDS (Fall 2019)/Day 6 plan

Homework assigned

Read and reflect: TBD
A5: Final project plan

Resources

Week 8: November 14

Day 8 plan

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

HCDS (Fall 2019)/Day 8 plan

Homework assigned

Reading and reflect: TBD (data science ethics survey paper)
A6: Final project presentation

Resources

Ethical OS Toolkit and Risk Mitigation Checklist. EthicalOS.org.
Morgan, J. 2016. Evaluating Related Articles recommendations. Wikimedia Research.
Morgan, J. 2017. 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. User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14).
Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Making recommendations better: an analytic model for human-recommender interaction. In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06).
Sean M. McNee, Nishikant Kapoor, and Joseph A. Konstan. 2006. Don't look stupid: avoiding pitfalls when recommending research papers. In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work (CSCW '06).
Michael D. Ekstrand and Martijn C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16).
Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
Anderson, Carl. The role of model interpretability in data science. Medium, 2016.

Week 9: November 21

Day 9 plan

Data science in organizations: TBD

Assignments due

Reading reflection

Agenda

HCDS (Fall 2019)/Day 9 plan

Homework assigned

Read and reflect: TBD
A6: Final project presentation
A7: Final project report

Resources

Week 10: November 28 (No Class Session)

Assignments due

Reading reflection

Readings assigned

NONE

Homework assigned

NONE

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.

@@ Line 28: / Line 28: @@
 ;Assignments due
-* fill out the pre-course survey
+* Fill out the pre-course survey
 * Read: Provost, Foster, and Tom Fawcett. [http://online.liebertpub.com/doi/pdf/10.1089/big.2013.1508 ''Data science and its relationship to big data and data-driven decision making.''] Big Data 1.1 (2013): 51-59.
 ;Agenda
 {{:HCDS (Fall 2019)/Day 1 plan}}
-;Readings assigned
-* Hickey, Walt. [https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/ ''The Dollars and Cents Case Against Hollywood's Exclusion of Women.''] FiveThirtyEight, 2014.
-* Keegan, Brian. [https://github.com/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb ''The Need for Openness in Data Journalism.''] 2014.
 ;Homework assigned
-* Reading reflection
+* Read and reflect on both:
+:*Hickey, Walt. [https://fivethirtyeight.com/features/the-dollar-and-cents-case-against-hollywoods-exclusion-of-women/ ''The Dollars and Cents Case Against Hollywood's Exclusion of Women.''] FiveThirtyEight, 2014.
+:* Keegan, Brian. [https://github.com/brianckeegan/Bechdel/blob/master/Bechdel_test.ipynb ''The Need for Openness in Data Journalism.''] 2014.
 * [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A1:_Data_curation|A1: Data curation]]
@@ Line 67: / Line 65: @@
 ;Agenda
 {{:HCDS (Fall 2019)/Day 2 plan}}
-;Readings assigned
 ;Homework assigned
@@ Line 76: / Line 71: @@
 ;Resources
 * Hickey, Walt. [https://fivethirtyeight.com/features/the-bechdel-test-checking-our-work/ ''The Bechdel Test: Checking Our Work'']. FiveThirtyEight, 2014.
 * J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), ''[http://altmetrics.org/manifesto Altmetrics: A manifesto]'', 26 October 2010.
@@ Line 92: / Line 86: @@
 * sample code for API calls ([http://paws-public.wmflabs.org/paws-public/User:Jtmorgan/data512_a1_example.ipynb view the notebook], [http://paws-public.wmflabs.org/paws-public/User:Jtmorgan/data512_a1_example.ipynb?format=raw download the notebook]).
 *''See [[Human_Centered_Data_Science/Datasets#Dataset_documentation_examples|the datasets page]] for examples of well-documented and not-so-well documented open datasets.''
 <br/>
@@ Line 139: / Line 132: @@
 ;Introduction to mixed-methods research: ''Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing''
 ;Assignments due
 * Week 3 reading reflection
 * A2: Bias in data
 ;Agenda
@@ Line 152: / Line 143: @@
 * Read and reflect: Barocas, Solan and Nissenbaum, Helen. [https://www.nyu.edu/projects/nissenbaum/papers/BigDatasEndRun.pdf ''Big Data's End Run around Anonymity and Consent'']. In ''Privacy, Big Data, and the Public Good''. 2014.
 * [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A3:_Crowdwork_ethnography|A3: Crowdwork ethnography]]
 ;Qualitative research methods resources
@@ Line 187: / Line 177: @@
 ;Research ethics for big data: ''privacy, informed consent and user treatment''
 ;Assignments due
@@ Line 372: / Line 361: @@
 * Brent Dykes, ''[https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/ Data Storytelling: The Essential Data Science Skill Everyone Needs].'' Forbes, 2016.
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