Editing User:Groceryheist/drafts/Data Science Syllabus

;Data Science and Organizational Communication:
;Principal instructor: [[User:Groceryheist|Nate TeBlunthuis]]
;Course Catalog Description: Fundamental principles of data science and its implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness and accountability; data provenance, curation, preservation, and reproducibility; human computation; data communication and visualization; the role of data science in organizational context and the societal impacts of data science. 

== Course Description ==
The rise of "data science" reflects a broad and ongoing shift in how many teams, organizational leaders,  communities of practice, and entire industries create and use knowledge. This class teaches "data science" as practiced by data-intensive knowledge workers but also as it is positioned in historical, organizational, institutional, and societal contexts.  Students will gain an appriciation for the technical and intellectual aspects of data science, consider critical questions about how data science is often practiced, and envision ethical and effective science practice in their current and future organiational roles.  The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments.

The course is designed around two high-stakes projects. In the first stage of the students will attend the Community Data Science Workshop (CDSC). I am one of the organizers and instructors of this three week intensive workshop on basic programming and data analysis skills. The first course project is to apply these skills together with the conceptual material from this course we have covered so far to conduct an original data analysis on a topic of the student's interest. The second high-stakes project is a critical analysis of an organization or work team. For this project students will serve as consultants to an organizational unit involved in data science. Through interviews and workplace observations they will gain an understanding of the socio-technical and organizational context of their team. They will then synthesize this understanding with the knowledge they gained from the course material to compose a report offering actionable insights to their team.

This version of the syllabus is designed around a weekly schedule. 

== Learning Objectives ==

By the end of this course, students will be able to: 
* Understand what it means to analyze large and complex data effectively and ethically with an understanding of human, societal, organizational, and socio-technical contexts. 
* Consider the account ethical, social, organizational, and legal considerations of data science in organizational and institutional contexts.
* Combine quantitative and qualitative data to generate critical insights into human behavior.
* Discuss and evaluate ethical, social, organizational and legal trade-offs of different data analysis, testing, curation, and sharing methods.

== Schedule ==

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Course schedule (click to expand)
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=== Week 1 ===
<!-- [[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
* Attend week 1 of CDSW
* Read: Provost, Foster, and Tom Fawcett. [http://online.liebertpub.com/doi/pdf/10.1089/big.2013.1508 ''Data science and its relationship to big data and data-driven decision making.''] Big Data 1.1 (2013): 51-59.

<!-- ;Agenda -->
<!-- {{:HCDS (Fall 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
* Attend week 2 of CDSW

<!-- ;Resources -->
* 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

<!-- * 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. -->
<!-- * 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 ===
<!-- [[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
* Attend week 2 of CDSW
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|Assignment 1: 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 ===
<!-- [[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
* Attend week 2 of CDSW

<!-- ;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
* Attend week 3 of CDSW

<!-- ;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. -->

<!-- <\!--  -->
<!-- * 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. -->
<!-- -\->  -->


<!-- *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 ===
<!-- [[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)
* Barley, S. R. (1986). Technology as an occasion for structuring: evidence from observations of ct scanners and the social order of radiology departments. Administrative Science Quarterly, 31(1), 78–108.
* Orlikowski, W. J., & Barley, S. R. (2001). Technology and institutions: what can research on information technology and research on organizations learn from each other? MIS Q., 25(2), 145–165. https://doi.org/10.2307/3250927

;Homework assigned
* Reading reflection



<!-- ;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 ===
; Technology and Organizing

; Assignments due
* Week 4 reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A1:_Data_curation|A1: Data curation]]


; Readings assigned
* Passi, S., & Jackson, S. J. (2018). Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects. Proc. ACM Hum.-Comput. Interact., 2(CSCW), 136:1–136:28. https://doi.org/10.1145/3274405

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

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=== Week 6 ===
; Data science in Organizational Contexts

; Assignments due
* Week 5 reading reflection
* [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A2:_Bias_in_data|A2: Bias in data]]

;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)

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=== Week 7 ===
<!-- [[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).
* 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
<!-- * [[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 8 ===
<!-- [[HCDS_(Fall_2018)/Day_6_plan|Day 6 plan]] -->

<!-- [[:File:HCDS 2018 week 6 slides.pdf|Day 6 slides]] -->

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

;Assignments due
* Reading reflection
* A4: Final Project Plan

<!-- ;Agenda -->
<!-- {{:HCDS (Fall 2018)/Day 6 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. -->
<!-- * 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. -->
<!-- * 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. -->
<!-- * 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] -->


<br/>
<hr/>
<br/>

<!-- === Week 7 === -->
<!-- <\!-- [[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 -->
<!-- <\!-- * [[Human_Centered_Data_Science_(Fall_2018)/Assignments#A3:_Crowdwork_ethnography|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. -->
<!-- ;Readings assigned -->
<!-- ;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. -->
<!-- <br/> -->
<!-- <hr/> -->
<!-- <br/> -->

=== Week 9 ===
<!-- [[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.  


<br/>
<hr/>
<br/>


=== Week 10 ===
<!-- [[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. -->

<br/>
<hr/>
<br/>

=== Week 11 ===
<!-- [[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 (by 11:59pm)

<!-- ;Resources -->
<!-- * ''one'' -->

<br/>
<hr/>
<br/>

=== Week 12: Finals Week (No Class Session) ===
* NO CLASS
* A6: FINAL PROJECT REPORT DUE BY 11:59PM
<!-- * LATE PROJECT SUBMISSIONS NOT ACCEPTED. -->

</div>
</div>



== Assignments ==

Your grade in this class will be assigned through:

* 9 Reading reflections (25%)
* 6 Project assignments (50%)
* Participation (25%)

Assignments are comprised of weekly in-class activities, weekly reading reflections, written assignments, and programming/data analysis assignments. Weekly in-class reading groups will discuss the assigned readings from the course and students are expected to have read the material in advance. In class activities each week are posted to Canvas and may require time outside of class to complete.

Project Assignments 1 and 2 are extensions of exercies from the Community Data Science Workshop and will get you started on y Project   

Unless otherwise noted, all assignments are due before 5pm on the following week's class.

Unless otherwise noted, all assignments are individual assignments.

=== Weekly reading reflections ===
This course will introduce you to cutting edge research and opinion from major thinkers in the domain of human centered data science. By reading and writing about this material, you will have an opportunity to explore the complex intersections of technology, methodology, ethics, and social thought that characterize this budding field of research and practice. 

As a participant in the course, you are responsible for intellectually engaging with ''all assigned readings'' and developing an understanding of the ideas discussed in them.

The weekly reading reflections assignment is designed to encourage you to reflect on these works and make connections during our class discussions. To this end, you will be responsible for posting reflections on the previous week's assigned reading before the next class session.

There will generally be multiple readings assigned each week. You are responsible for reading ''all of them.'' However, you only need to write a reflection on '''one reading per week.''' Unless your instructor specifies otherwise, you can choose which reading you would like to reflect on. 

These reflections are meant to be succinct but meaningful. Follow the instructions below, demonstrate that you engaged with the material, and turn the reflection in on time, and you will receive full credit. Late reading reflections will never be accepted.

;Instructions
# Read all assigned readings.
# Select a reading to reflect on.
# In at least 2-3 full sentences, answer the question "How does this reading inform your understanding of human centered data science?"
# Using full sentences, list ''at least 1 question'' that this reading raised in your mind, and say ''why'' the reading caused you to ask this question.
# Post your reflection to Canvas before the next class session.

You are encouraged, but not required, to make connections between different readings (from the current week, from previous weeks, or other relevant material you've read/listened to/watched) in your reflections.


=== Project Assignments ===
This section provides basic descriptions of all scheduled course assignments. 

=== A1: Project proposal and data aquisition ===

For this assignment you will propose a project for your midterm project and use the skills you have learned in the CDSW to collect or present a dataset.  

==== Required deliverables ====
A directory in your GitHub repository called <tt>data-512-a1</tt> that contains the following files:
:# 5 source data files in JSON format that follow the specified naming convention.
:# 1 final data file in CSV format that follows the specified naming convention.
:# 1 Jupyter notebook named <tt>hcds-a1-data-curation</tt> that contains all code as well as information necessary to understand each programming step.
:# 1 README file in .txt or .md format that contains information to reproduce the analysis, including data descriptions, attributions and provenance information, and descriptions of all relevant resources and documentation (inside and outside the repo) and hyperlinks to those resources.
:# 1 LICENSE file that contains an [https://opensource.org/licenses/MIT MIT LICENSE] for your code.
:# 1 .png or .jpeg image of your visualization.

==== Helpful tips ====
* Read all instructions carefully before you begin
* Read all API documentation carefully before you begin
* Experiment with queries in the sandbox of the technical documentation  for each API to familiarize yourself with the schema and the data
* Ask questions on Slack if you're unsure about anything
* When documenting/describing your project, think: "If I found this GitHub repo, and wanted to fully reproduce the analysis, what information would I want? What information would I need?"

=== A2: Bias in data ===
The goal of this assignment is to explore the concept of bias through data on Wikipedia articles - specifically, articles on political figures from a variety of countries. For this assignment, you will combine a dataset of Wikipedia articles with a dataset of country populations, and use a machine learning service called ORES to estimate the quality of each article.

You are expected to perform an analysis of how the ''coverage'' of politicians on Wikipedia and the ''quality'' of articles about politicians varies between countries. Your analysis will consist of a series of tables that show:
# the countries with the greatest and least coverage of politicians on Wikipedia compared to their population. 
# the countries with the highest and lowest proportion of high quality articles about politicians.

You are also expected to write a short reflection on the project, that describes how this assignment helps you understand the causes and consequences of bias on Wikipedia.

'''A repository with a README framework and examples of querying the ORES datastore in R and Python can be found [https://github.com/Ironholds/data-512-a2 here]'''

==== Getting the article and population data ====

The first step is getting the data, which lives in several different places. The wikipedia dataset can be found [https://figshare.com/articles/Untitled_Item/5513449 on Figshare]. Read through the documentation for this repository, then download and unzip it. 

The population data is on [https://www.dropbox.com/s/5u7sy1xt7g0oi2c/WPDS_2018_data.csv?dl=0 Dropbox]. Download this data as a CSV file (hint: look for the 'Microsoft Excel' icon in the upper right).

==== Getting article quality predictions ====

Now you need to get the predicted quality scores for each article in the Wikipedia dataset. For this step, we're using a Wikimedia API endpoint for a machine learning system called [https://www.mediawiki.org/wiki/ORES ORES] ("Objective Revision Evaluation Service"). ORES estimates the quality of an article (at a particular point in time), and assigns a series of probabilities that the article is in one of 6 quality categories. The options are, from best to worst:

# FA - Featured article
# GA - Good article
# B - B-class article
# C - C-class article
# Start - Start-class article
# Stub - Stub-class article

For context, these quality classes are a sub-set of quality assessment categories developed by Wikipedia editors. If you're curious, you can read more about what these assessment classes mean on [https://en.wikipedia.org/wiki/Wikipedia:WikiProject_assessment#Grades English Wikipedia]. We will talk about what these categories mean, and how the ORES model predicts which category an article goes into, next week in class. For this assignment, you only need to know that these categories exist, and that ORES will assign one of these 6 categories to any article you send it.

The ORES API is configured fairly similarly to the pageviews API we used last assignment; documentation can be found [https://ores.wikimedia.org/v3/#!/scoring/get_v3_scores_context_revid_model here]. It expects a revision ID, which is the third column in the Wikipedia dataset, and a model, which is "wp10". The [https://github.com/Ironholds/data-512-a2 sample iPython notebooks for this assignment] provide examples of a correctly-structured API query that you can use to understand how to gather your data, and also to examine the query output.

In order to get article predictions for each article in the Wikipedia dataset, you will need to read <tt>page_data.csv</tt> into Python (or R), and then read through the dataset line by line, using the value of the <tt>last_edit</tt> column in the API query. If you're working in Python, the [https://docs.python.org/3/library/csv.html CSV module] will help with this.

When you query the API, you will notice that ORES returns a <tt>prediction</tt> value that contains the name of one category, as well as <tt>probability</tt> values for each of the 6 quality categories. For this assignment, you only need to capture and use the value for <tt>prediction</tt>. We'll talk more about what the other values mean in class next week.

==== Combining the datasets ====
 
Some processing of the data will be necessary! In particular, you'll need to - after retrieving and including the ORES data for each article - merge the wikipedia data and population data together. Both have fields containing country names for just that purpose. After merging the data, you'll invariably run into entries which ''cannot'' be merged. Either the population dataset does not have an entry for the equivalent Wikipedia country, or vice versa. You will need to remove the rows that do not have matching data.

Consolidate the remaining data into a single CSV file which looks something like this:


{|class="wikitable"
|-
! Column
|-
|country
|-
|article_name
|-
|revision_id
|-
|article_quality
|-
|population
|}

Note: <tt>revision_id</tt> here is the same thing as <tt>last_edit</tt>, which you used to get scores from the ORES API.

==== Analysis ====
Your analysis will consist of calculating the proportion (as a percentage) of articles-per-population and high-quality articles for each country. By "high quality" articles, in this case we mean the number of articles about politicians in a given country that ORES predicted would be in either the "FA" (featured article) or "GA" (good article) classes.

Examples:
* if a country has a population of 10,000 people, and you found 10 articles about politicians from that country, then the percentage of articles-per-population would be .1%. 
* if a country has 10 articles about politicians, and 2 of them are FA or GA class articles, then the percentage of high-quality articles would be 20%. 

==== Tables ====
The tables should be pretty straightforward. Produce four tables that show:
#10 highest-ranked countries in terms of number of politician articles as a proportion of country population
#10 lowest-ranked countries in terms of number of politician articles as a proportion of country population
#10 highest-ranked countries in terms of number of GA and FA-quality articles as a proportion of all articles about politicians from that country
#10 lowest-ranked countries in terms of number of GA and FA-quality articles as a proportion of all articles about politicians from that country

Embed them in the iPython notebook.

==== Writeup ====
Write a few paragraphs, either in the README or in the notebook, reflecting on what you have learned, what you found, what (if anything) surprised you about your findings, and/or what theories you have about why any biases might exist (if you find they exist). You can also include any questions this assignment raised for you about bias, Wikipedia, or machine learning. Particular questions you might want to answer:

# What biases did you expect to find in the data, and why?
# What are the results?
# What theories do you have about why the results are what they are?

==== Submission instructions ====
#Complete your Notebook and datasets in Jupyter Hub.
#Create the data-512-a2 repository on GitHub w/ your code and data.
#Complete and add your README and LICENSE file.
#Submit the link to your GitHub repo to: https://canvas.uw.edu/courses/1244514/assignments/4376107

==== Required deliverables ====
A directory in your GitHub repository called <tt>data-512-a2</tt> that contains the following files:
:# 1 final data file in CSV format that follows the formatting conventions.
:# 1 Jupyter notebook named <tt>hcds-a2-bias</tt> that contains all code as well as information necessary to understand each programming step, as well as your writeup (if you have not included it in the README) and the tables.
:# 1 README file in .txt or .md format that contains information to reproduce the analysis, including data descriptions, attributions and provenance information, and descriptions of all relevant resources and documentation (inside and outside the repo) and hyperlinks to those resources, and your writeup (if you have not included it in the notebook). A prototype framework is included in the [https://github.com/Ironholds/data-512-a2 sample repository]
:# 1 LICENSE file that contains an [https://opensource.org/licenses/MIT MIT LICENSE] for your code.

==== Helpful tips ====
* Read all instructions carefully before you begin
* Read all API documentation carefully before you begin
* Experiment with queries in the sandbox of the technical documentation for the API to familiarize yourself with the schema and the data
* Explore the data a bit before starting to be sure you understand how it is structured and what it contains
* Ask questions on Slack if you're unsure about anything. Please email Os to set up a meeting, or come to office hours, if you want to! This time is set aside specifically for you - it is not an imposition.
* When documenting/describing your project, think: "If I found this GitHub repo, and wanted to fully reproduce the analysis, what information would I want? What information would I need?"

=== A3: Crowdwork ethnography ===
For this assignment, you will go undercover as a member of the Amazon Mechanical Turk community. You will preview or perform Mechanical Turk tasks (called "HITs"), lurk in Turk worker discussion forums, and write an ethnographic account of your experience as a crowdworker, and how this experience changes your understanding of the phenomenon of crowdwork.

The full assignment description is available [https://docs.google.com/document/d/16lZdTxkw1meUPMzA-BYl8TVtk0Jxv4Wh8mbZq_BursM/edit?usp=sharing as a Google doc] and [[:File:HCDS_Crowdwork_ethnography_instructions.pdf|as a PDF]].

=== A4: Final project plan ===
For this assignment, you will write up a study plan for your final class project. The plan will cover a variety of details about your final project. Identify the organization that you will work with, c data you will use, what you will do with the data (e.g. statistical analysis, train a model), what results you expect or intend, and most importantly, why your project is interesting or important (and to whom, besides yourself).

=== A5: Final project presentation ===
For this assignment, you will give an in-class presentation of your final project. The goal of this assignment is to demonstrate that you are able to effectively communicate your research questions, methods, conclusions, and implications to your target audience.

=== A6: Final project report ===
For this assignment, you will publish the complete code, data, and analysis of your final research project. The goal is to demonstrate that you can incorporate all of the human-centered design considerations you learned in this course and create research artifacts that are understandable, impactful, and reproducible.


== Policies ==
The following general policies apply to this course.

=== Attendance ===

As detailed in [[Teaching Assessment | my page on assessment]], attendance in class is expected of all participants. If you need to miss class for any reason, please contact a member of the teaching team ahead of time (email is best). Multiple unexplained absences will likely result in a lower grade or (in extreme circumstances) a failing grade. In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc. 

=== Respect === 
Students are expected to treat each other, and the instructors, with respect. Students are prohibited from engaging in any kind of harassment or derogatory behavior, which includes offensive verbal comments or imagery related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, or religion. In addition, students should not engage in any form of inappropriate physical contact or unwelcome sexual attention, and should respect each others’ right to privacy in regards to their personal life. In the event that you feel you (or another student) have been subject to a violation of this policy, please reach out to the instructors in whichever form you prefer.

The instructors are committed to providing a safe and healthy learning environment for students. As part of this, students are asked not to wear any clothing, jewelry, or any related medium for symbolic expression which depicts an indigenous person or cultural expression reappropriated as a mascot, logo, or caricature. These include, but are not limited to, iconography associated with the following sports teams:

# Chicago Blackhawks
# Washington Redskins
# Cleveland Indians
# Atlanta Braves


=== Devices in Class ===

Electronic devices (e.g., phones, tablets, laptops) are not going to permitted in class. If you have a documented need to use a device, please contact me ahead of time to let me know. If you do get permission to use a device, I will ask you to sit in the very back of the classroom.

The goal of this policy is to help you stay focused and avoid distractions for yourself and your peers in the classroom. This is really important and turns out to be much more difficult in the presence of powerful computing devices with brightly glowing screens and fast connections to the Internet. For more on the rationale behind this policy, please read [https://medium.com/@cshirky/why-i-just-asked-my-students-to-put-their-laptops-away-7f5f7c50f368 Clay Shirky’s thoughtful discussion of his approach to this issue].

<!-- Of course, we will discuss assignments and topics that involve referring to things online. Toward that end, you might find it convenient to bring a laptop or tablet to class. If you want to look something up on your device outside of a time I clearly point out are device-allowed, please ask me.
I will always point out explicitly in class if it's OK to use devices. 
 '''Except during these parts of class — which  — I ask that you refrain from using your laptops, tablets, phones, and pretty much any (digital) device with a screen.''' -->

=== Electronic Mail Standards of Conduct ===

Email communications (and all communications generally) among UW community members should seek to respect the rights and privileges of all members of the academic community. This includes not interfering with university functions or endangering the health, welfare, or safety of other persons. With this in mind, in addition to the [http://app.leg.wa.gov/WAC/default.aspx?cite=478-120 University of Washington's Student Conduct Code], I establishes the following standards of conduct in respect to electronic communications among students and faculty:

* If, as a student, you have a question about course content or procedures, please use the online discussion board designed for this purpose. If you have specific questions about your performance, contact me directly.
* I strive to respond to Email communications within 48 hours. If you do not hear from me, please come to my office, call me, or send me a reminder Email.
* Email communications should be limited to occasional messages necessary to the specific educational experience at hand.
* Email communications should not include any CC-ing of anyone not directly involved in the specific educational experience at hand.
* Email communications should not include any blind-CC-ing to third parties, regardless of the third party’s relevance to the matter at hand.


=== Academic integrity and plagiarism ===
As a University of Washington student, you are expected to practice high standards of academic honesty and integrity. You are responsible to understand and abide by [http://www.washington.edu/admin/rules/policies/WAC/478-121-107.html UW’s Student Governance Code on Academic Misconduct], and the  [http://www.washington.edu/admin/rules/policies/WAC/478-121-107.html UW’s Administrative Code on Academic Misconduct], and to comply with verbal or written instructions from the professor or TA of this course. This includes plagiarism, which is a serious offense. All assignments will be reviewed for integrity. All rules regarding academic integrity extend to electronic communication and the use of online sources. If you are not sure what constitutes plagiarism, read [https://owl.english.purdue.edu/owl/resource/589/02/ this overview] in addition to UW’s policy statements.

I am committed to upholding the academic standards of the University of Washington’s Student Conduct Code. If I suspect a student violation of that code, I will first engage in a conversation with that student about my concerns. If we cannot successfully resolve a suspected case of academic misconduct through our conversations, I will refer the situation to the department of communication advising office who can then work with the COM Chair to seek further input and if necessary, move the case up through the College.

While evidence of academic misconduct may result in a lower grade, I will not unilaterally lower a grade without addressing the issue with you first through the process outlined above.

Other academic integrity resources:
* [http://www.washington.edu/teaching/cheating-or-plagiarism/ Center for Teaching and Learning: Cheating or Plagiarism]
* [https://depts.washington.edu/grading/pdf/AcademicResponsibility.pdf University of Washington Student Academic Responsibility (PDF)]

'''Notice:''' The University has a license agreement with VeriCite, an educational tool that helps prevent or identify plagiarism from Internet resources. Your instructor may use the service in this class by requiring that assignments are submitted electronically to be checked by VeriCite. The VeriCite Report will indicate the amount of original text in your work and whether all material that you quoted, paraphrased, summarized, or used from another source is appropriately referenced.


=== Disability and accommodations ===

As part of ensuring that the class is as accessible as possible, the instructors are entirely comfortable with you using whatever form of note-taking method or recording is most comfortable to you, including laptops and audio recording devices. The instructors will do their best to ensure that all slides and scripts/notes are immediately available online after a lecture has concluded. In addition, if asked ahead of time we can try to record the audio of individial lectures for students who have learning differences that make audiovisual notes preferable to written ones.

If you require additional accommodations, please contact Disabled Student Services: 448 Schmitz, 206-543-8924 (V/TTY). If you have a letter from DSS indicating that you have a disability which requires academic accommodations, please present the letter to the instructors so we can discuss the accommodations you might need in the class. If you have any questions about this policy, reach out to the instructors directly.

For more information on disability accommodations, and how to apply for one, please review [http://depts.washington.edu/uwdrs/current-students/accommodations/ UW's Disability Resources for Students].

=== Assignments and coursework ===
Grades will be determined as follows: 

* 20% Participation
* 20% Reading reflections
* 20% Midterm project
* 40% Final project

You are expected to produce work in all of the assignments that reflects the highest standards of professionalism. For written documents, this means proper spelling, grammar, and formatting.

Late assignments will not be accepted; if your assignment is late, you will receive a zero score. Again, if you run into an issue that necessitates an extension, please reach out.
[[Category:Groceryheist drafts]]


[[Category:Groceryheist drafts]]