Editing Human Centered Data Science (Fall 2018)/Schedule

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=== Week 1: September 27 ===
[[HCDS_(Fall_2018)/Day_1_plan|Day 1 plan]]

[[:File:HCDS_2018_week_1_slides.pdf|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
* 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. ('''no reading reflection required''')

;Agenda
{{:HCDS (Fall 2018)/Day 1 plan}}

;Readings assigned
* 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.

;Homework assigned
* Reading reflection

;Resources
* 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.
* 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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=== Week 2: October 4 ===
[[HCDS_(Fall_2018)/Day_2_plan|Day 2 plan]]

<!-- [[:File:HCDS Week 2 slides.pdf|Day 2 slides]] -->

;Ethical considerations: ''privacy, informed consent and user treatment''


;Assignments due
*Week 1 reading reflection

;Agenda
{{:HCDS (Fall 2018)/Day 2 plan}}


;Readings assigned
* 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 


;Homework assigned
* Reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|A1: Data curation]]

;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.
* 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.
* Markham, Annette and Buchanan, Elizabeth. [https://aoir.org/reports/ethics2.pdf ''Ethical Decision-Making and Internet Researchers.''] Association for Internet Research, 2012.
* 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.
* Barbaro, Michael and Zeller, Tom. [http://query.nytimes.com/gst/abstract.html?res=9E0CE3DD1F3FF93AA3575BC0A9609C8B63&legacy=true ''A Face Is Exposed for AOL Searcher No. 4417749.''] New York Times, 2008.
* 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.
* 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.
* 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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=== Week 3: October 11 ===
[[HCDS_(Fall_2018)/Day_3_plan|Day 3 plan]]

[[:File:HCDS_2018_week_3_slides.pdf|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
{{:HCDS (Fall 2018)/Day 3 plan}}

;Readings assigned 
* 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.

;Homework assigned
* Reading reflection

;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.
* J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), ''[http://altmetrics.org/manifesto Altmetrics: A manifesto]'', 26 October 2010.

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* 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.
* 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.
--> 

;Assignment 1 [[Human_Centered_Data_Science_(Fall_2018)/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]).
*''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 4: October 18 ===
[[HCDS_(Fall_2018)/Day_4_plan|Day 4 plan]]

[[:File:HCDS 2018 week 4 slides.pdf|Day 4 slides]]

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


;Assignments due
* Reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|A1: Data curation]]

;Agenda
{{:HCDS (Fall 2018)/Day 4 plan}}

;Readings assigned (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.
* 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
* Reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A2:_Bias_in_data|A2: Bias in data]]


;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.
* 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
* 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
* 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   
<!-- * 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) -->
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=== Week 5: October 25 ===
[[HCDS_(Fall_2018)/Day_5_plan|Day 5 plan]]

[[:File:HCDS 2018 week 5 slides.pdf|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
{{:HCDS (Fall 2018)/Day 5 plan}}


;Readings assigned (Read both, reflect on one)
* 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).


;Homework assigned
* Reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A3:_Crowdwork_ethnography|A3: Crowdwork ethnography]]


;Qualitative research methods resources
* 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.
* 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.
* 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.

;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 6: November 1 ===
[[HCDS_(Fall_2018)/Day_6_plan|Day 6 plan]]

[[:File:HCDS 2018 week 6 slides.pdf|Day 6 slides]]

;Interrogating algorithms: ''algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits''

;Assignments due
* Reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A2:_Bias_in_data|A2: Bias in data]]

;Agenda
{{:HCDS (Fall 2018)/Day 6 plan}}

;Readings assigned
* 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



;Homework assigned
* Reading reflection


;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. 
* 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.
* 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.
* 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.
* [https://www.perspectiveapi.com/#/ Google's Perspective API]



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=== Week 7: November 8 ===
[[HCDS_(Fall_2018)/Day_7_plan|Day 7 plan]]

[[:File:HCDS 2018 week 7 slides.pdf|Day 7 slides]]

;Critical approaches to data science: ''power, data, and society; ethics of crowdwork''


;Assignments due
* Reading reflection
* A3: Crowdwork ethnography


;Agenda
{{:HCDS (Fall 2018)/Day 7 plan}}

;Readings assigned (read both, reflect on one)
* Read: Baumer, E. P. S. (2017). ''[http://journals.sagepub.com/doi/pdf/10.1177/2053951717718854 Toward human-centered algorithm design].'' Big Data & Society.
* 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.

;Homework assigned
* Reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A4:_Final_project_plan|A4: Final project plan]]


;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
* 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
* 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. 
* 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.


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=== Week 8: November 15 ===
[[HCDS_(Fall_2018)/Day_8_plan|Day 8 plan]]

[[:File:HCDS 2018 week 8 slides.pdf|Day 8 slides]]

;Human-centered algorithm design: ''algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems''


;Assignments due
* Reading reflection


;Agenda
{{:HCDS (Fall 2018)/Day 8 plan}}

;Readings assigned
* 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.

;Homework assigned
* Reading reflection

;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.
* 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.
*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.


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=== Week 9: November 22 (No Class Session)===
[[HCDS_(Fall_2018)/Day_8_plan|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
{{:HCDS (Fall 2018)/Day 9 plan}}

;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
* Reading reflection

;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.  


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=== Week 10: November 29 ===
[[HCDS_(Fall_2018)/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
* Reading reflection


;Agenda
{{:HCDS (Fall 2018)/Day 10 plan}}

;Readings assigned
* NONE

;Homework assigned
* A5: Final presentation

;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.
* 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/2016/08/10/communicating-data-science-why-and-some-of-the-how-to-visualize-information/ Communicating data science: Why and how to visualize information].'' 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.



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=== Week 11: December 6 ===
[[HCDS_(Fall_2018)/Day_11_plan|Day 11 plan]]

;Final presentations: course wrap up, presentation of student projects''


;Assignments due
* A5: Final presentation


;Agenda
{{:HCDS (Fall 2018)/Day 11 plan}}

;Readings assigned
* none!

;Homework assigned
* A6: Final project report (due 12/9 by 11:59pm)

;Resources
* ''one''

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=== 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.


[[Category:HCDS (Fall 2018)]]