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

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=== Week 1: September 26 ===
 
=== Week 1: September 26 ===
[[HCDS_(Fall_2019)/Day_1_plan|Day 1 plan]]
 
 
<!--
 
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[[:File:HCDS_2018_week_1_slides.pdf|Day 1 slides]]
 
[[:File:HCDS_2018_week_1_slides.pdf|Day 1 slides]]
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;Assignments due
 
;Assignments due
* fill out the pre-course survey
+
* Fill out the [https://docs.google.com/forms/d/e/1FAIpQLSffoC-Dd2eYtiWr00ZoRcaTc9eeaK_lySaAVDTX2ZTj_lHIFA/viewform?usp=sf_link 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. ('''no reading reflection required''')
+
* Read ('''not graded'''): 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
 
;Agenda
{{:HCDS (Fall 2019)/Day 1 plan}}
+
* Syllabus review
 
+
* Pre-course survey results
;Readings assigned
+
* What do we mean by ''data science?''
* Read: 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.
+
* 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
 
;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]]
 
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A1:_Data_curation|A1: Data curation]]
  
 
;Resources
 
;Resources
 +
* Princeton Dialogues on AI & Ethics: ''[https://aiethics.princeton.edu/case-studies/ Case studies]''
 
* Aragon, C. et al. (2016). [https://cscw2016hcds.files.wordpress.com/2015/10/cscw_2016_human-centered-data-science_workshop.pdf ''Developing a Research Agenda for Human-Centered Data Science.''] Human Centered Data Science workshop, CSCW 2016.
 
* Aragon, C. et al. (2016). [https://cscw2016hcds.files.wordpress.com/2015/10/cscw_2016_human-centered-data-science_workshop.pdf ''Developing a Research Agenda for Human-Centered Data Science.''] Human Centered Data Science workshop, CSCW 2016.
 
* Kling, Rob and Star, Susan Leigh. [https://scholarworks.iu.edu/dspace/bitstream/handle/2022/1798/wp97-04B.html ''Human Centered Systems in the Perspective of Organizational and Social Informatics.''] 1997.
 
* Kling, Rob and Star, Susan Leigh. [https://scholarworks.iu.edu/dspace/bitstream/handle/2022/1798/wp97-04B.html ''Human Centered Systems in the Perspective of Organizational and Social Informatics.''] 1997.
 
* Harford, T. (2014). ''[http://doi.org/10.1111/j.1740-9713.2014.00778.x Big data: A big mistake?]'' Significance, 11(5), 14–19.
 
* Harford, T. (2014). ''[http://doi.org/10.1111/j.1740-9713.2014.00778.x Big data: A big mistake?]'' Significance, 11(5), 14–19.
<!-- * Ideo.org [http://www.designkit.org/ ''The Field Guide to Human-Centered Design.''] 2015. -->
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* Ideo.org [http://www.designkit.org/ ''The Field Guide to Human-Centered Design.''] 2015.
 
 
 
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=== Week 2: October 3 ===
 
=== Week 2: October 3 ===
[[HCDS_(Fall_2019)/Day_2_plan|Day 2 plan]]
 
 
 
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[[:File:HCDS_2019_week_2_slides.pdf|Day 2 slides]]
 
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;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 2 plan}}
+
* Reading reflection discussion
 
+
* Assignment 1 review & reflection
;Readings assigned
+
* A primer on copyright, licensing, and hosting for code and data
* Read: Duarte, N., Llanso, E., & Loup, A. (2018). ''[https://cdt.org/files/2017/12/FAT-conference-draft-2018.pdf Mixed Messages? The Limits of Automated Social Media Content Analysis].'' Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 106.
+
* Introduction to replicability, reproducibility, and open research
 +
* In-class activity
 +
* Intro to assignment 2: Bias in data
  
 
;Homework assigned
 
;Homework assigned
* Reading reflection
+
* Read and reflect: Duarte, N., Llanso, E., & Loup, A. (2018). ''[https://cdt.org/files/2017/12/FAT-conference-draft-2018.pdf Mixed Messages? The Limits of Automated Social Media Content Analysis].'' Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 106.
* A2: Bias in data
+
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A2:_Bias_in_data|A2: Bias in data]]
  
 
;Resources
 
;Resources
* 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.
 
 
* Hickey, Walt. [https://fivethirtyeight.com/features/the-bechdel-test-checking-our-work/ ''The Bechdel Test: Checking Our Work'']. FiveThirtyEight, 2014.
 
* Hickey, Walt. [https://fivethirtyeight.com/features/the-bechdel-test-checking-our-work/ ''The Bechdel Test: Checking Our Work'']. FiveThirtyEight, 2014.
 +
* GroupLens, ''[https://grouplens.org/datasets/movielens/ MovieLens datasets]''
 
* J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), ''[http://altmetrics.org/manifesto Altmetrics: A manifesto]'', 26 October 2010.
 
* J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), ''[http://altmetrics.org/manifesto Altmetrics: A manifesto]'', 26 October 2010.
 
+
* Chapter 2 [https://www.practicereproducibleresearch.org/core-chapters/2-assessment.html "Assessing Reproducibility"] and Chapter 3 [https://www.practicereproducibleresearch.org/core-chapters/3-basic.html "The Basic Reproducible Workflow Template"] from ''The Practice of Reproducible Research'' University of California Press, 2018.
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+
* Halfaker, A., Geiger, R. S., Morgan, J. T., & Riedl, J. (2013). ''[https://www-users.cs.umn.edu/~halfaker/publications/The_Rise_and_Decline/halfaker13rise-preprint.pdf The rise and decline of an open collaboration system: How Wikipedia’s reaction to popularity is causing its decline].'' American Behavioral Scientist, 57(5), 664-688
 
* TeBlunthuis, N., Shaw, A., and Hill, B.M. (2018). Revisiting "The rise and decline" in a population of peer production projects. In ''Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems (CHI '18)''. https://doi.org/10.1145/3173574.3173929
 
* TeBlunthuis, N., Shaw, A., and Hill, B.M. (2018). Revisiting "The rise and decline" in a population of peer production projects. In ''Proceedings of the 2018 ACM Conference on Human Factors in Computing Systems (CHI '18)''. https://doi.org/10.1145/3173574.3173929
 
* Press, Gil. [https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2608257f6f63 ''Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says.''] Forbes, 2016.
 
* Press, Gil. [https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#2608257f6f63 ''Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says.''] Forbes, 2016.
 
* Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
 
* Christensen, Garret. [https://github.com/garretchristensen/BestPracticesManual/blob/master/Manual.pdf ''Manual of Best Practices in Transparent Social Science Research.''] 2016.
 +
<!--
 
* Aschwanden, Christie. [https://fivethirtyeight.com/features/science-isnt-broken/ ''Science Isn't Broken''] FiveThirtyEight, 2015.
 
* Aschwanden, Christie. [https://fivethirtyeight.com/features/science-isnt-broken/ ''Science Isn't Broken''] FiveThirtyEight, 2015.
-->
 
 
;Assignment 1 [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A1:_Data_curation|Data curation]] resources:
 
 
*Chapter 2 [https://www.practicereproducibleresearch.org/core-chapters/2-assessment.html "Assessing Reproducibility"] and Chapter 3 [https://www.practicereproducibleresearch.org/core-chapters/3-basic.html "The Basic Reproducible Workflow Template"] from ''The Practice of Reproducible Research'' University of California Press, 2018.
 
 
* 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]).
 
* 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.''
 
*''See [[Human_Centered_Data_Science/Datasets#Dataset_documentation_examples|the datasets page]] for examples of well-documented and not-so-well documented open datasets.''
 
+
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=== Week 3: October 10 ===
 
=== Week 3: October 10 ===
[[HCDS_(Fall_2019)/Day_3_plan|Day 3 plan]]
 
 
<!--
 
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[[:File:HCDS 2019 week 3 slides.pdf|Day 3 slides]]
 
[[:File:HCDS 2019 week 3 slides.pdf|Day 3 slides]]
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;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 3 plan}}
+
* Reading reflection review
 
+
* Sources and consequences of bias in data collection, processing, and re-use
;Readings assigned (Read both, reflect on one)
+
* In-class activity
* Wang, Tricia. ''[https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7 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. ''[http://www-users.cs.umn.edu/~bhecht/publications/goldstandards_CSCW2015.pdf 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
 
;Homework assigned
* Reading reflection
+
* Read both, reflect on one:
 +
:* Wang, Tricia. ''[https://medium.com/ethnography-matters/why-big-data-needs-thick-data-b4b3e75e3d7 Why Big Data Needs Thick Data]''. Ethnography Matters, 2016.
 +
:* Kery, M. B., Radensky, M., Arya, M., John, B. E., & Myers, B. A. (2018). ''[https://marybethkery.com/projects/Verdant/Kery-The-Story-in-the-Notebook-Exploratory-Data-Science-using-a-Literate-Programming-Tool.pdf The Story in the Notebook: Exploratory Data Science using a Literate Programming Tool]''. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI’18, 1–11. https://doi.org/10.1145/3173574.3173748
  
 
;Resources
 
;Resources
* Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016). ''[http://kiciman.org/wp-content/uploads/2017/08/SSRN-id2886526.pdf Social data: Biases, methodological pitfalls, and ethical boundaries].
 
* Brian N Larson. 2017. ''[http://www.ethicsinnlp.org/workshop/pdf/EthNLP04.pdf Gender as a Variable in Natural-Language Processing: Ethical Considerations]. EthNLP, 3: 30–40.
 
 
* Bender, E. M., & Friedman, B. (2018). [https://openreview.net/forum?id=By4oPeX9f Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science]. To appear in Transactions of the ACL.
 
* Bender, E. M., & Friedman, B. (2018). [https://openreview.net/forum?id=By4oPeX9f 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. ''[http://delivery.acm.org/10.1145/2860000/2858123/p13-johnson.pdf?ip=209.166.92.236&id=2858123&acc=CHORUS&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1539880715_eb477907771cea4ecaabc953094c3080 Not at Home on the Range: Peer Production and the Urban/Rural Divide].'' CHI '16. DOI: https://doi.org/10.1145/2858036.2858123
+
* Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumeé III, H., & Crawford, K. (2018). [https://www.fatml.org/media/documents/datasheets_for_datasets.pdf Datasheets for datasets]. arXiv preprint arXiv:1803.09010.
* Leo Graiden Stewart, Ahmer Arif, A. Conrad Nied, Emma S. Spiro, and Kate Starbird. 2017. ''[https://faculty.washington.edu/kstarbi/Stewart_Starbird_Drawing_the_Lines_of_Contention-final.pdf 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
+
* Olteanu, A., Castillo, C., Diaz, F., Kıcıman, E., & Kiciman, E. (2019). ''[https://www.frontiersin.org/articles/10.3389/fdata.2019.00013/pdf Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries].'' Frontiers in Big Data, 2, 13. https://doi.org/10.3389/fdata.2019.00013
* Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. ''[https://web.stanford.edu/~jurafsky/pubs/linguistic_change_lifecycle.pdf 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  
+
* Rose Eveleth ''[https://www.vox.com/the-highlight/2019/10/1/20887003/tech-technology-evolution-natural-inevitable-ethics The biggest lie tech people tell themselves — and the rest of us].'' October 8, 2019, Vox.com.
<!-- * Astrid Mager. 2012. Algorithmic ideology: How capitalist society shapes search engines. Information, Communication & Society 15, 5: 769–787. http://doi.org/10.1080/1369118X.2012.676056 (in Canvas) -->
+
* Rani Molla ''[https://www.vox.com/2019/2/8/18211794/government-data-internet The government is using the wrong data to make crucial decisions about the internet].'' February 8, 2019, Vox.com.
 +
* 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. 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. CSCW 2017. DOI: https://doi.org/10.1145/3134920
 +
* Lada A. Adamic and Natalie Glance. 2005. The political blogosphere and the 2004 U.S. election: divided they blog. (LinkKDD '05). DOI=http://dx.doi.org/10.1145/1134271.1134277
 +
* 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. (WWW '13). DOI: https://doi.org/10.1145/2488388.2488416  
 
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=== Week 4: October 17 ===
 
=== Week 4: October 17 ===
[[HCDS_(Fall_2019)/Day_4_plan|Day 4 plan]]
 
 
 
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;Introduction to mixed-methods research: ''Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing''
+
;Introduction to qualitative and mixed-methods research: ''Big data vs thick data; integrating qualitative research methods into data science practice; crowdsourcing''
 
 
  
 
;Assignments due
 
;Assignments due
* Week 3 reading reflection
+
* Reading reflection
 
* A2: Bias in data
 
* A2: Bias in data
 
  
 
;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 4 plan}}
+
* Reading reflection reflection
 
+
* Overview of qualitative research
 
+
* Introduction to ethnography
;Readings assigned (Read both, reflect on one)
+
* In-class activity: explaining art to aliens
* Donovan, J., Caplan, R., Matthews, J., & Hanson, L. (2018). ''[https://datasociety.net/wp-content/uploads/2018/04/Data_Society_Algorithmic_Accountability_Primer_FINAL.pdf Algorithmic accountability: A primer]''. Data & Society, 501(c).
+
* Mixed methods research and data science
 
+
* An introduction to crowdwork
 +
* Overview of assignment 3: Crowdwork ethnography
  
 
;Homework assigned
 
;Homework assigned
* Reading reflection
+
* 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. ([https://canvas.uw.edu/courses/1319253/files/folder/Readings PDF available on Canvas])
 
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A3:_Crowdwork_ethnography|A3: Crowdwork ethnography]]
 
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A3:_Crowdwork_ethnography|A3: Crowdwork ethnography]]
  
 
+
;Resources
;Qualitative research methods resources
+
* Singer, P., Lemmerich, F., West, R., Zia, L., Wulczyn, E., Strohmaier, M., & Leskovec, J. (2017, April). ''[https://arxiv.org/pdf/1702.05379.pdf Why we read wikipedia]''. In Proceedings of the 26th International Conference on World Wide Web.
 +
* [https://meta.wikimedia.org/wiki/Research:The_role_of_citations_in_how_readers_evaluate_Wikipedia_articles/Trust_taxonomy Taxonomy of reasons why people trust/distrust Wikipedia], Jonathan Morgan, Wikimedia Research report, May 2019.
 
* Ladner, S. (2016). ''[http://www.practicalethnography.com/ Practical ethnography: A guide to doing ethnography in the private sector]''. Routledge.
 
* Ladner, S. (2016). ''[http://www.practicalethnography.com/ Practical ethnography: A guide to doing ethnography in the private sector]''. Routledge.
 
* Spradley, J. P. (2016). ''[https://www.waveland.com/browse.php?t=688 The ethnographic interview]''. Waveland Press.
 
* Spradley, J. P. (2016). ''[https://www.waveland.com/browse.php?t=688 The ethnographic interview]''. Waveland Press.
 +
* Spradley, J. P. (2016) ''[https://www.waveland.com/browse.php?t=689 Participant Observation]''. Waveland Press
 
* Eriksson, P., & Kovalainen, A. (2015). ''[http://study.sagepub.com/sites/default/files/Eriksson%20and%20Kovalainen.pdf Ch 12: Ethnographic Research]''. In Qualitative methods in business research: A practical guide to social research. Sage.
 
* Eriksson, P., & Kovalainen, A. (2015). ''[http://study.sagepub.com/sites/default/files/Eriksson%20and%20Kovalainen.pdf Ch 12: Ethnographic Research]''. In Qualitative methods in business research: A practical guide to social research. Sage.
 +
* ''[http://www.wou.edu/~girodm/library/zork.pdf Qualitative research activity: categorizing student responses].'' Mark Girod, Western Oregon University
 +
* ''[https://cmci.colorado.edu/~palen/EmpiricalEpistemologiesforHCC-7.pdf Empirical    Epistemologies Applied to Human-­‐Centered Computing Research]'' Leysia Palen, University of Colorado Boulder, November 16 2014.
 +
 +
<!--
 
* Usability.gov, ''[https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html System usability scale]''.  
 
* Usability.gov, ''[https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html System usability scale]''.  
 
* Nielsen, Jakob (2000). ''[https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/ Why you only need to test with five users]''. nngroup.com.
 
* Nielsen, Jakob (2000). ''[https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/ Why you only need to test with five users]''. nngroup.com.
 +
-->
  
;Wikipedia gender gap research resources
 
* Hill, B. M., & Shaw, A. (2013). ''[journals.plos.org/plosone/article?id=10.1371/journal.pone.0065782 The Wikipedia gender gap revisited: Characterizing survey response bias with propensity score estimation]''. PloS one, 8(6), e65782
 
* Shyong (Tony) K. Lam, Anuradha Uduwage, Zhenhua Dong, Shilad Sen, David R. Musicant, Loren Terveen, and John Riedl. 2011. ''[http://files.grouplens.org/papers/wp-gender-wikisym2011.pdf WP:clubhouse?: an exploration of Wikipedia's gender imbalance.]'' In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym '11). ACM, New York, NY, USA, 1-10. DOI=http://dx.doi.org/10.1145/2038558.2038560
 
* Maximillian Klein. ''[http://whgi.wmflabs.org/gender-by-language.html Gender by Wikipedia Language]''. Wikidata Human Gender Indicators (WHGI), 2017.
 
* Source: Wagner, C., Garcia, D., Jadidi, M., & Strohmaier, M. (2015, April). ''[https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10585/10528 It's a Man's Wikipedia? Assessing Gender Inequality in an Online Encyclopedia]''. In ICWSM (pp. 454-463).
 
 
* Benjamin Collier and Julia Bear. ''[https://static1.squarespace.com/static/521c8817e4b0dca2590b4591/t/523745abe4b05150ff027a6e/1379354027662/2012+-+Collier%2C+Bear+-+Conflict%2C+confidence%2C+or+criticism+an+empirical+examination+of+the+gender+gap+in+Wikipedia.pdf Conflict, criticism, or confidence: an empirical examination of the gender gap in wikipedia contributions]''. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW '12). DOI: https://doi.org/10.1145/2145204.2145265
 
* Christina Shane-Simpson, Kristen Gillespie-Lynch, Examining potential mechanisms underlying the Wikipedia gender gap through a collaborative editing task, In Computers in Human Behavior, Volume 66, 2017, https://doi.org/10.1016/j.chb.2016.09.043. (PDF on Canvas)
 
* Amanda Menking and Ingrid Erickson. 2015. ''[https://upload.wikimedia.org/wikipedia/commons/7/77/The_Heart_Work_of_Wikipedia_Gendered,_Emotional_Labor_in_the_World%27s_Largest_Online_Encyclopedia.pdf The Heart Work of Wikipedia: Gendered, Emotional Labor in the World's Largest Online Encyclopedia]''. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). https://doi.org/10.1145/2702123.2702514
 
 
;Crowdwork research resources
 
* WeArDynamo contributors. ''[http://wiki.wearedynamo.org/index.php?title=Basics_of_how_to_be_a_good_requester How to be a good requester]'' and ''[http://wiki.wearedynamo.org/index.php?title=Guidelines_for_Academic_Requesters Guidelines for Academic Requesters]''. Wearedynamo.org
 
 
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=== Week 5: October 24 ===
 
=== Week 5: October 24 ===
[[HCDS_(Fall_2019)/Day_5_plan|Day 5 plan]]
 
 
<!--
 
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[[:File:HCDS 2019 week 5 slides.pdf|Day 5 slides]]
 
[[:File:HCDS 2019 week 5 slides.pdf|Day 5 slides]]
 
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;Ethical considerations: ''privacy, informed consent and user treatment''
+
;Research ethics for big data: ''privacy, informed consent and user treatment''
 
 
  
 
;Assignments due
 
;Assignments due
*Week 4 reading reflection
+
*Reading reflection
  
 
;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 5 plan}}
+
* Reading reflection review
 
+
* Guest lecture
 
+
* A2 retrospective
;Readings assigned
+
* Final project deliverables and timeline
* Read:  boyd, danah and Crawford, Kate, Six Provocations for Big Data (September 21, 2011). A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011. Available at SSRN: https://ssrn.com/abstract=1926431 or http://dx.doi.org/10.2139/ssrn.1926431
+
* A brief history of research ethics in the United States
  
  
 
;Homework assigned
 
;Homework assigned
* Reading reflection
+
* 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. ([https://canvas.uw.edu/courses/1319253/files/folder/Readings PDF available on Canvas])
 
 
  
 
;Resources
 
;Resources
* 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/
 
* 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.
 
* 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.
 +
* Kelley, P. G., Bresee, J., Cranor, L. F., & Reeder, R. W. (2009). ''[http://cups.cs.cmu.edu/soups/2009/proceedings/a4-kelley.pdf A “nutrition label” for privacy.]'' Proceedings of the 5th Symposium on Usable Privacy and Security - SOUPS ’09, 1990, 1. https://doi.org/10.1145/1572532.1572538
 +
* Warncke-Wang, M., Cosley, D., & Riedl, J. (2013). ''[https://opensym.org/wsos2013/proceedings/p0202-warncke.pdf Tell me more: An actionable quality model for wikipedia].'' Proceedings of the 9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013. https://doi.org/10.1145/2491055.2491063
 +
<!--
 
* 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. 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.
 
* 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.
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* 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.
 
* Gray, Mary. [http://culturedigitally.org/2014/07/when-science-customer-service-and-human-subjects-research-collide-now-what/ ''When Science, Customer Service, and Human Subjects Research Collide. Now What?''] Culture Digitally, 2014.
 
* Gray, Mary. [http://culturedigitally.org/2014/07/when-science-customer-service-and-human-subjects-research-collide-now-what/ ''When Science, Customer Service, and Human Subjects Research Collide. Now What?''] Culture Digitally, 2014.
* Tene, Omer and Polonetsky, Jules. [https://www.stanfordlawreview.org/online/privacy-paradox-privacy-and-big-data/ ''Privacy in the Age of Big Data.''] Stanford Law Review, 2012.
 
 
* Dwork, Cynthia. [https://www.microsoft.com/en-us/research/wp-content/uploads/2008/04/dwork_tamc.pdf ''Differential Privacy: A survey of results'']. Theory and Applications of Models of Computation , 2008.
 
* Dwork, Cynthia. [https://www.microsoft.com/en-us/research/wp-content/uploads/2008/04/dwork_tamc.pdf ''Differential Privacy: A survey of results'']. Theory and Applications of Models of Computation , 2008.
 
* Hsu, Danny. [http://blog.datasift.com/2015/04/09/techniques-to-anonymize-human-data/ ''Techniques to Anonymize Human Data.''] Data Sift, 2015.
 
* Hsu, Danny. [http://blog.datasift.com/2015/04/09/techniques-to-anonymize-human-data/ ''Techniques to Anonymize Human Data.''] Data Sift, 2015.
* Metcalf, Jacob. [http://ethicalresolve.com/twelve-principles-of-data-ethics/ ''Twelve principles of data ethics'']. Ethical Resolve, 2016.
+
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 +
 
 
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<br/>
 
<hr/>
 
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=== Week 6: October 31 ===
 
=== Week 6: October 31 ===
[[HCDS_(Fall_2019)/Day_6_plan|Day 6 plan]]
 
 
<!--
 
<!--
 
[[:File:HCDS 2019 week 6 slides.pdf|Day 6 slides]]
 
[[:File:HCDS 2019 week 6 slides.pdf|Day 6 slides]]
 
-->
 
-->
;Interrogating algorithms: ''algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits''
+
;Data science and society: ''power, data, and society; ethics of crowdwork''
  
 
;Assignments due
 
;Assignments due
 
* Reading reflection
 
* Reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A2:_Bias_in_data|A2: Bias in data]]
+
* A3: Crowdwork ethnography
  
 
;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 6 plan}}
+
* Reading reflections
 
+
* Assignment 3 review
;Readings assigned
+
* Guest lecture: Stefania Druga
* Astrid Mager. 2012. ''[https://computingeverywhere.soc.northwestern.edu/wp-content/uploads/2017/07/Mager-Algorithmic-Ideology-Required.pdf Algorithmic ideology: How capitalist society shapes search engines]''. Information, Communication & Society 15, 5: 769–787. http://doi.org/10.1080/1369118X.2012.676056
+
* In-class activity
 
+
* Introduction to assignment 4: Final project proposal
 
 
  
 
;Homework assigned
 
;Homework assigned
* Reading reflection
+
* Read both, reflect on one:
 
+
:* Baumer, E. P. S. (2017). ''[http://journals.sagepub.com/doi/pdf/10.1177/2053951717718854 Toward human-centered algorithm design].'' Big Data & Society.
 +
:* Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). ''[http://www.aaai.org/ojs/index.php/aimagazine/article/download/2513/2456 Power to the People: The Role of Humans in Interactive Machine Learning].'' AI Magazine, 35(4), 105.
 +
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A4:_Final project proposal|A4: Final project proposal]]
  
 
;Resources
 
;Resources
* 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.
+
* Lilly C. Irani and M. Six Silberman. 2013. ''[https://escholarship.org/content/qt10c125z3/qt10c125z3.pdf Turkopticon: interrupting worker invisibility in amazon mechanical turk]''. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). DOI: https://doi.org/10.1145/2470654.2470742
* 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
+
* Salehi, Niloufar, Lilly C. Irani, Michael S. Bernstein, Ali Alkhatib, Eva Ogbe, and Kristy Milland. ''[https://hci.stanford.edu/publications/2015/dynamo/DynamoCHI2015.pdf We are dynamo: Overcoming stalling and friction in collective action for crowd workers]''. In Proceedings of the 33rd annual ACM conference on human factors in computing systems, pp. 1621-1630. ACM, 2015.
* 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.
+
* Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). ''[https://mako.cc/academic/hill_etal-cdsw_chapter-DRAFT.pdf 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. https://doi.org/10.1007/978-3-319-59186-5_9
* ''[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.
 
* 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.
 
* 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
 
* Nate Matias, 2017. ''[https://medium.com/@natematias/how-anyone-can-audit-facebooks-newsfeed-b879c3e29015 How Anyone Can Audit Facebook's Newsfeed].'' Medium.com
 
* 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.
 
* Blue, Violet. ''[https://www.engadget.com/2017/09/01/google-perspective-comment-ranking-system/ Google’s comment-ranking system will be a hit with the alt-right].'' Engadget, 2017.
 
 
* Ingold, David and Soper, Spencer. ''[https://www.bloomberg.com/graphics/2016-amazon-same-day/ Amazon Doesn’t Consider the Race of Its Customers. Should It?].'' Bloomberg, 2016.
 
* Ingold, David and Soper, Spencer. ''[https://www.bloomberg.com/graphics/2016-amazon-same-day/ Amazon Doesn’t Consider the Race of Its Customers. Should It?].'' Bloomberg, 2016.
* Paul Lamere. ''[https://musicmachinery.com/2011/05/14/how-good-is-googles-instant-mix/ How good is Google's Instant Mix?].'' Music Machinery, 2011.
 
 
* Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. ''[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias: Risk Assessment in Criminal Sentencing]. Propublica, May 2018.
 
* Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. ''[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias: Risk Assessment in Criminal Sentencing]. Propublica, May 2018.
* [https://www.perspectiveapi.com/#/ Google's Perspective API]
 
 
 
 
 
<br/>
 
<br/>
 
<hr/>
 
<hr/>
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=== Week 7: November 7 ===
 
=== Week 7: November 7 ===
[[HCDS_(Fall_2019)/Day_7_plan|Day 7 plan]]
 
 
<!--
 
<!--
 
[[:File:HCDS 2019 week 7 slides.pdf|Day 7 slides]]
 
[[:File:HCDS 2019 week 7 slides.pdf|Day 7 slides]]
 
-->
 
-->
;Critical approaches to data science: ''power, data, and society; ethics of crowdwork''
+
;Human centered machine learning: ''algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits''
 
 
  
 
;Assignments due
 
;Assignments due
 
* Reading reflection
 
* Reading reflection
* A3: Crowdwork ethnography
+
* A4: Project proposal
 
 
  
 
;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 7 plan}}
+
* Reading reflection review
 
+
* Algorithmic transparency, interpretability, and accountability
;Readings assigned (read both, reflect on one)
+
* Auditing algorithms
* Read: Baumer, E. P. S. (2017). ''[http://journals.sagepub.com/doi/pdf/10.1177/2053951717718854 Toward human-centered algorithm design].'' Big Data & Society.
+
* In-class activity
* Read: Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). ''[http://www.aaai.org/ojs/index.php/aimagazine/article/download/2513/2456 Power to the People: The Role of Humans in Interactive Machine Learning].'' AI Magazine, 35(4), 105.
+
* Introduction to assignment 5: Final project proposal
  
 
;Homework assigned
 
;Homework assigned
* Reading reflection
+
* Read and reflect: Kocielnik, R., Amershi, S., & Bennett, P. N. (2019). ''[http://saleemaamershi.com/papers/chi2019.AI.Expectations.pdf Will You Accept an Imperfect AI?]'' Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems  - CHI ’19, 1–14. https://doi.org/10.1145/3290605.3300641
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A4:_Final_project_plan|A4: Final project plan]]
+
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A5:_Final_project_plan|A5: Final project plan]]
 
 
  
 
;Resources
 
;Resources
* Neff, G., Tanweer, A., Fiore-Gartland, B., & Osburn, L. (2017). Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big Data, 5(2), 85–97. https://doi.org/10.1089/big.2016.0050
+
* 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.  
* Lilly C. Irani and M. Six Silberman. 2013. ''[https://escholarship.org/content/qt10c125z3/qt10c125z3.pdf Turkopticon: interrupting worker invisibility in amazon mechanical turk]''. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13). DOI: https://doi.org/10.1145/2470654.2470742
+
* 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.
* Bivens, R. and Haimson, O.L. 2016. ''[http://journals.sagepub.com/doi/pdf/10.1177/2056305116672486 Baking Gender Into Social Media Design: How Platforms Shape Categories for Users and Advertisers]''. Social Media + Society. 2, 4 (2016), 205630511667248. DOI:https://doi.org/10.1177/2056305116672486.  
+
* Nate Matias, 2017. ''[https://medium.com/@natematias/how-anyone-can-audit-facebooks-newsfeed-b879c3e29015 How Anyone Can Audit Facebook's Newsfeed].'' Medium.com
* Schlesinger, A. et al. 2017. ''[http://arischlesinger.com/wp-content/uploads/2017/03/chi2017-schlesinger-intersectionality.pdf Intersectional HCI: Engaging Identity through Gender, Race, and Class].'' Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI ’17. (2017), 5412–5427. DOI:https://doi.org/10.1145/3025453.3025766.
+
* 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.
 
+
* Blue, Violet. ''[https://www.engadget.com/2017/09/01/google-perspective-comment-ranking-system/ Google’s comment-ranking system will be a hit with the alt-right].'' Engadget, 2017.
 +
* Anderson, Carl. ''[https://medium.com/@leapingllamas/the-role-of-model-interpretability-in-data-science-703918f64330 The role of model interpretability in data science].'' Medium, 2016.
 +
* Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. ''[https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Machine Bias: Risk Assessment in Criminal Sentencing]. Propublica, May 2018.
 +
* Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., … Gebru, T. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10.1145/3287560.3287596
 +
* Hosseini, H., Kannan, S., Zhang, B., & Poovendran, R. (2017). Deceiving Google’s Perspective API Built for Detecting Toxic Comments. ArXiv:1702.08138 [Cs]. Retrieved from http://arxiv.org/abs/1702.08138
 +
* Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2017). Like trainer, like bot? Inheritance of bias in algorithmic content moderation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10540 LNCS, 405–415. https://doi.org/10.1007/978-3-319-67256-4_32
 +
* Borkan, D., Dixon, L., Sorensen, J., Thain, N., & Vasserman, L. (2019). Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification. 2, 491–500. https://doi.org/10.1145/3308560.3317593
 +
* Zhang, J., Chang, J., Danescu-Niculescu-Mizil, C., Dixon, L., Hua, Y., Taraborelli, D., & Thain, N. (2019). Conversations Gone Awry: Detecting Early Signs of Conversational Failure. 1350–1361. https://doi.org/10.18653/v1/p18-1125
 +
* Miriam Redi, Besnik Fetahu, Jonathan T. Morgan, and Dario Taraborelli. 2019. ''[https://arxiv.org/pdf/1902.11116.pdf Citation Needed a Taxonomy and Algorithmic Assessment of Wikipedia’s Verifiability].'' The Web Conference.
 +
*[https://www.perspectiveapi.com/#/ Google's Perspective API]
  
 
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=== Week 8: November 14 ===
 
=== Week 8: November 14 ===
[[HCDS_(Fall_2019)/Day_8_plan|Day 8 plan]]
 
 
<!--
 
<!--
 
[[:File:HCDS 2019 week 8 slides.pdf|Day 8 slides]]
 
[[:File:HCDS 2019 week 8 slides.pdf|Day 8 slides]]
 
-->
 
-->
;Human-centered algorithm design: ''algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems''
+
;User experience and data science: ''algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems''
 
 
  
 
;Assignments due
 
;Assignments due
 
* Reading reflection
 
* Reading reflection
 
+
* A5: Final project plan
  
 
;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 8 plan}}
+
* ''coming soon''
 
+
<!--
;Readings assigned
+
* Final project overview & examples
* Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). ''[https://mako.cc/academic/hill_etal-cdsw_chapter-DRAFT.pdf 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.
+
* Reading reflections
 +
* Human-centered algorithm design
 +
:* design process
 +
:* user-driven evaluation
 +
:* design patterns & anti-patterns
 +
-->
  
 
;Homework assigned
 
;Homework assigned
* Reading reflection
+
* Reading and reflect: Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, III, Miro Dudik, and Hanna Wallach. 2019. ''[https://arxiv.org/pdf/1812.05239.pdf Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?]''. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). ACM, New York, NY, USA, Paper 600, 16 pages. DOI: https://doi.org/10.1145/3290605.3300830
 +
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A6:_Final project presentation|A6: Final project presentation]]
  
 
;Resources
 
;Resources
* 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.
+
* Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. ''[http://files.grouplens.org/papers/mcnee-chi06-hri.pdf 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. ''[http://files.grouplens.org/papers/p171-mcnee.pdf 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).
 +
* 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.
 +
* 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.  
 
* 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).
* Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. ''[http://files.grouplens.org/papers/mcnee-chi06-hri.pdf 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. ''[http://files.grouplens.org/papers/p171-mcnee.pdf 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. ''[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).
 
* Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
 
* Anderson, Carl. ''[https://medium.com/@leapingllamas/the-role-of-model-interpretability-in-data-science-703918f64330 The role of model interpretability in data science].'' Medium, 2016.
 
 
 
 
<br/>
 
<br/>
 
<hr/>
 
<hr/>
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=== Week 9: November 21 ===
 
=== Week 9: November 21 ===
[[HCDS_(Fall_2019)/Day_9_plan|Day 9 plan]]
 
  
;Data science for social good: ''Community-based and participatory approaches to data science; Using data science for society's benefit''
+
;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
 
;Assignments due
 
* Reading reflection
 
* Reading reflection
* A4: Final project plan
 
  
 
;Agenda
 
;Agenda
{{:HCDS (Fall 2019)/Day 9 plan}}
+
* 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
  
;Readings assigned
 
* Berney, Rachel, Bernease Herman, Gundula Proksch, Hillary Dawkins, Jacob Kovacs, Yahui Ma, Jacob Rich, and Amanda Tan. ''[https://dssg.uchicago.edu/wp-content/uploads/2017/09/berney.pdf 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
 
;Homework assigned
* Reading reflection
+
* Read and reflect: Passi, S., & Jackson, S. J. (2018). ''[https://dl.acm.org/citation.cfm?doid=3290265.3274405 Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects].'' Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–28. https://doi.org/10.1145/3274405 ([https://sjackson.infosci.cornell.edu/Passi&Jackson_TrustinDataScience(CSCW2018).pdf ACCESS PDF HERE])
 +
* [[Human_Centered_Data_Science_(Fall_2019)/Assignments#A7:_Final_project_report|A7: Final project report]]
  
 
;Resources
 
;Resources
* 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.
+
* 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])
 
+
* 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].''
 +
* Morgan, J. T., 2019. ''[https://figshare.com/articles/Ethical_Human_Centered_AI/8044553 Ethical and Human-centered AI at Wikimedia]''. Wikimedia Research 2030​.
  
 
<br/>
 
<br/>
Line 370: Line 357:
  
 
=== Week 10: November 28 (No Class Session) ===
 
=== Week 10: November 28 (No Class Session) ===
<!--
 
[[HCDS_(Fall_2019)/Day_10_plan|Day 10 plan]]
 
 
[[:File:HCDS 2018 week 10 slides.pdf|Day 10 slides]]
 
 
;User experience and big data: ''Design considerations for machine learning applications; human centered data visualization; data storytelling''
 
 
  
 
;Assignments due
 
;Assignments due
 
* Reading reflection
 
* Reading reflection
 
 
;Agenda
 
{{:HCDS (Fall 2019)/Day 10 plan}}
 
-->
 
 
;Readings assigned
 
* NONE
 
  
 
;Homework assigned
 
;Homework assigned
* A5: Final presentation
+
* Read and reflect: Barocas, S., & Boyd, D. (2017). ''Engaging the ethics of data science in practice.'' Communications of the ACM, 60(11), 23–25. https://doi.org/10.1145/3144172 ([https://canvas.uw.edu/courses/1319253/files/folder/Readings PDF available on Canvas])
  
 
;Resources
 
;Resources
*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.
 
* Jess Holbrook. ''[https://medium.com/google-design/human-centered-machine-learning-a770d10562cd Human Centered Machine Learning].'' Google Design Blog. 2017.
 
* 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
 
* Brown, A., Tuor, A., Hutchinson, B., & Nichols, N. (2018). ''[[https://arxiv.org/abs/1803.04967 Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection].'' arXiv preprint arXiv:1803.04967.
 
* 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.
 
 
* Marilynn Larkin, ''[https://www.elsevier.com/connect/how-to-give-a-dynamic-scientific-presentation How to give a dynamic scientific presentation].'' Elsevier Connect, 2015.
 
* Marilynn Larkin, ''[https://www.elsevier.com/connect/how-to-give-a-dynamic-scientific-presentation How to give a dynamic scientific presentation].'' Elsevier Connect, 2015.
 
* Megan Risdal, ''[http://blog.kaggle.com/2016/06/29/communicating-data-science-a-guide-to-presenting-your-work/ Communicating data science: a guide to presenting your work].'' Kaggle blog, 2016.
 
* Megan Risdal, ''[http://blog.kaggle.com/2016/06/29/communicating-data-science-a-guide-to-presenting-your-work/ Communicating data science: a guide to presenting your work].'' Kaggle blog, 2016.
Line 405: Line 370:
 
* Megan Risdal, ''[http://blog.kaggle.com/2016/06/13/communicating-data-science-an-interview-with-a-storytelling-expert-tyler-byers/ Communicating data science: an interview with a storytelling expert].'' Kaggle blog, 2016.
 
* Megan Risdal, ''[http://blog.kaggle.com/2016/06/13/communicating-data-science-an-interview-with-a-storytelling-expert-tyler-byers/ Communicating data science: an interview with a storytelling expert].'' Kaggle blog, 2016.
 
* 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.
 
* 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.
 
 
 
 
<br/>
 
<br/>
 
<hr/>
 
<hr/>
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=== Week 11: December 5 ===
 
=== Week 11: December 5 ===
[[HCDS_(Fall_2019)/Day_11_plan|Day 11 plan]]
 
 
;Final presentations: course wrap up, presentation of student projects''
 
  
 +
;Final presentations: presentation of student projects, course wrap up''
  
 
;Assignments due
 
;Assignments due
 +
* Reading reflection
 
* A5: Final presentation
 
* A5: Final presentation
 
 
;Agenda
 
{{:HCDS (Fall 2019)/Day 11 plan}}
 
  
 
;Readings assigned
 
;Readings assigned
* none!
+
* NONE
  
 
;Homework assigned
 
;Homework assigned
* A6: Final project report (due 12/9 by 11:59pm)
+
* NONE
  
 
;Resources
 
;Resources
* ''one''
+
* NONE
  
 
<br/>
 
<br/>
Line 440: Line 397:
 
=== Week 12: Finals Week (No Class Session) ===
 
=== Week 12: Finals Week (No Class Session) ===
 
* NO CLASS
 
* NO CLASS
* A6: FINAL PROJECT REPORT DUE BY 5:00PM on Tuesday, December 10
+
* A7: FINAL PROJECT REPORT DUE BY 5:00PM on Tuesday, December 10 via Canvas
 
* LATE PROJECT SUBMISSIONS NOT ACCEPTED.
 
* LATE PROJECT SUBMISSIONS NOT ACCEPTED.
  
  
 
[[Category:HCDS (Fall 2019)]]
 
[[Category:HCDS (Fall 2019)]]

Latest revision as of 21:22, 27 November 2019

This page is a work in progress.


Week 1: September 26[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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[edit]

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)[edit]

Assignments due
  • Reading reflection
Homework assigned
Resources




Week 11: December 5[edit]

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)[edit]

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