Human Centered Data Science (Fall 2019)

Human Centered Data Science

DATA 512 - UW Interdisciplinary Data Science Masters Program - Thursdays 5:00-9:50pm in Thompson Hall room 134.

Principal instructor

Jonathan T. Morgan (jmo25 at uw dot edu)

Co-instructor

Yihan Yu

Course Website

This wiki page is the canonical information resource for DATA512. All other course-related information will be linked on this page. We will use Canvas for announcements, file hosting, and submitting reading reflections, graded in-class assignments, and other programming and writing assignments. We will use Slack for Q&A and general discussion.

Course Description

Human Centered Data Science focuses on fundamental principles of data science and its human implications, including research ethics; data privacy; legal frameworks; algorithmic bias, transparency, fairness and accountability; data provenance, curation, preservation, and reproducibility; user experience design and research for big data; human computation; effective oral, written, and visual scientific communication; and societal impacts of data science.

Overview and learning objectives

The format of the class will be a mix of lecture, discussion, in-class activities, and qualitative and quantitative research assignments. Students will work in small groups for in-class activities, and work independently on all class project deliverables and homework assignments. Instructors will provide guidance in completing the exercises each week.

By the end of this course, students will be able to:

• Analyze large and complex data effectively and ethically with an understanding of human, societal, and socio-technical contexts.
• Take into account the ethical, social, and legal considerations when designing algorithms and performing large-scale data analysis.
• Combine quantitative and qualitative research methods to generate critical insights into human behavior.
• Discuss and evaluate ethical, social and legal trade-offs of different data analysis, testing, curation, and sharing methods.

Course resources

All pages and files on this wiki that are related to the Fall 2018 edition of DATA 512: Human-Centered Data Science are listed in Category:HCDS (Fall 2019).

Office hours

• Yihan Yu: Monday 5-6:30pm (Sieg Hall #129)
• Jonathan Morgan: Thursday 3:30 - 4:30 Communications (CMU) #333

Lecture slides

Slides for weekly lectures will be available in PDF form on in the Files section of Canvas, generally within 24 hours of each course session

Schedule

Direct link: Human Centered Data Science (Fall 2019)/Schedule

Course schedule (click to expand)

Week 1: September 26

Introduction to Human Centered Data Science

What is data science? What is human centered? What is human centered data science?

Assignments due

• Fill out the pre-course survey
• Read (not graded): Provost, Foster, and Tom Fawcett. Data science and its relationship to big data and data-driven decision making. Big Data 1.1 (2013): 51-59.

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:

• Hickey, Walt. The Dollars and Cents Case Against Hollywood's Exclusion of Women. FiveThirtyEight, 2014.
• Keegan, Brian. The Need for Openness in Data Journalism. 2014.

• A1: Data curation

Resources

• Princeton Dialogues on AI & Ethics: Case studies
• Aragon, C. et al. (2016). Developing a Research Agenda for Human-Centered Data Science. Human Centered Data Science workshop, CSCW 2016.
• Kling, Rob and Star, Susan Leigh. Human Centered Systems in the Perspective of Organizational and Social Informatics. 1997.
• Harford, T. (2014). Big data: A big mistake? Significance, 11(5), 14–19.
• Ideo.org The Field Guide to Human-Centered Design. 2015.

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Week 2: October 3

Reproducibility and Accountability

data curation, preservation, documentation, and archiving; best practices for open scientific research

Assignments due

• Week 1 reading reflection
• A1: Data curation

Agenda

• Reading reflection discussion
• Assignment 1 review & reflection
• A primer on copyright, licensing, and hosting for code and data
• Introduction to replicability, reproducibility, and open research
• In-class activity
• Intro to assignment 2: Bias in data

Homework assigned

• Read and reflect: Duarte, N., Llanso, E., & Loup, A. (2018). 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

Resources

• Hickey, Walt. The Bechdel Test: Checking Our Work. FiveThirtyEight, 2014.
• GroupLens, MovieLens datasets
• J. Priem, D. Taraborelli, P. Groth, C. Neylon (2010), Altmetrics: A manifesto, 26 October 2010.
• Chapter 2 "Assessing Reproducibility" and Chapter 3 "The Basic Reproducible Workflow Template" from The Practice of Reproducible Research University of California Press, 2018.
• Halfaker, A., Geiger, R. S., Morgan, J. T., & Riedl, J. (2013). 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
• Press, Gil. Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. Forbes, 2016.
• Christensen, Garret. Manual of Best Practices in Transparent Social Science Research. 2016.

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Week 3: October 10

Interrogating datasets

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

Assignments due

• Week 2 reading reflection

Agenda

• Reading reflection review
• Sources and consequences of bias in data collection, processing, and re-use
• In-class activity

Homework assigned

• Read both, reflect on one:

• Wang, Tricia. Why Big Data Needs Thick Data. Ethnography Matters, 2016.
• Kery, M. B., Radensky, M., Arya, M., John, B. E., & Myers, B. A. (2018). 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

• Bender, E. M., & Friedman, B. (2018). Data Statements for NLP: Toward Mitigating System Bias and Enabling Better Science. To appear in Transactions of the ACL.
• Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumeé III, H., & Crawford, K. (2018). Datasheets for datasets. arXiv preprint arXiv:1803.09010.
• Olteanu, A., Castillo, C., Diaz, F., Kıcıman, E., & Kiciman, E. (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers in Big Data, 2, 13. https://doi.org/10.3389/fdata.2019.00013
• Rose Eveleth The biggest lie tech people tell themselves — and the rest of us. October 8, 2019, Vox.com.
• Rani Molla 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

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

• 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. (PDF available on Canvas)
• A3: Crowdwork ethnography

Resources

• Singer, P., Lemmerich, F., West, R., Zia, L., Wulczyn, E., Strohmaier, M., & Leskovec, J. (2017, April). Why we read wikipedia. In Proceedings of the 26th International Conference on World Wide Web.
• Taxonomy of reasons why people trust/distrust Wikipedia, Jonathan Morgan, Wikimedia Research report, May 2019.
• Ladner, S. (2016). Practical ethnography: A guide to doing ethnography in the private sector. Routledge.
• Spradley, J. P. (2016). The ethnographic interview. Waveland Press.
• Spradley, J. P. (2016) Participant Observation. Waveland Press
• Eriksson, P., & Kovalainen, A. (2015). Ch 12: Ethnographic Research. In Qualitative methods in business research: A practical guide to social research. Sage.
• Qualitative research activity: categorizing student responses. Mark Girod, Western Oregon University
• Empirical Epistemologies Applied to Human-­‐Centered Computing Research Leysia Palen, University of Colorado Boulder, November 16 2014.

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Week 5: October 24

Research ethics for big data

privacy, informed consent and user treatment

Assignments due

• Reading reflection

Agenda

• Reading reflection review
• Guest lecture
• A2 retrospective
• Final project deliverables and timeline
• A brief history of research ethics in the United States

Homework assigned

• Read and reflect: Gray, M. L., & Suri, S. (2019). Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Eamon Dolan Books. (PDF available on Canvas)

Resources

• National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. The Belmont Report. U.S. Department of Health and Human Services, 1979.
• Bethan Cantrell, Javier Salido, and Mark Van Hollebeke (2016). Industry needs to embrace data ethics: Here's how it could be done. Workshop on Data and Algorithmic Transparency (DAT'16). http://datworkshop.org/
• Javier Salido (2012). Differential Privacy for Everyone. Microsoft Corporation Whitepaper.
• Markham, Annette and Buchanan, Elizabeth. Ethical Decision-Making and Internet Researchers. Association for Internet Research, 2012.
• Kelley, P. G., Bresee, J., Cranor, L. F., & Reeder, R. W. (2009). 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). 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

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Week 6: October 31

Data science and society

power, data, and society; ethics of crowdwork

Assignments due

• Reading reflection
• A3: Crowdwork ethnography

Agenda

• Reading reflections
• Assignment 3 review
• Guest lecture: Stefania Druga
• In-class activity
• Introduction to assignment 4: Final project proposal

Homework assigned

• Read both, reflect on one:

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

• A4: Final project proposal

Resources

• Lilly C. Irani and M. Six Silberman. 2013. 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
• Salehi, Niloufar, Lilly C. Irani, Michael S. Bernstein, Ali Alkhatib, Eva Ogbe, and Kristy Milland. 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.
• Hill, B. M., Dailey, D., Guy, R. T., Lewis, B., Matsuzaki, M., & Morgan, J. T. (2017). 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
• Ingold, David and Soper, Spencer. Amazon Doesn’t Consider the Race of Its Customers. Should It?. Bloomberg, 2016.
• Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. Machine Bias: Risk Assessment in Criminal Sentencing. Propublica, May 2018.

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Week 7: November 7

Human centered machine learning

algorithmic fairness, transparency, and accountability; methods and contexts for algorithmic audits

Assignments due

• Reading reflection
• A4: Project proposal

Agenda

• Reading reflection review
• Algorithmic transparency, interpretability, and accountability
• Auditing algorithms
• In-class activity
• Introduction to assignment 5: Final project proposal

Homework assigned

• Read and reflect: Kocielnik, R., Amershi, S., & Bennett, P. N. (2019). 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
• A5: Final project plan

Resources

• Christian Sandvig, Kevin Hamilton, Karrie Karahalios, Cedric Langbort (2014/05/22) 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.
• Friedman, B., & Nissenbaum, H. (1996). Bias in Computer Systems. ACM Trans. Inf. Syst., 14(3), 330–347.
• Nate Matias, 2017. How Anyone Can Audit Facebook's Newsfeed. Medium.com
• Hill, Kashmir. Facebook figured out my family secrets, and it won't tell me how. Engadget, 2017.
• Blue, Violet. Google’s comment-ranking system will be a hit with the alt-right. Engadget, 2017.
• Anderson, Carl. The role of model interpretability in data science. Medium, 2016.
• Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner. 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. Citation Needed a Taxonomy and Algorithmic Assessment of Wikipedia’s Verifiability. The Web Conference.
• Google's Perspective API

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Week 8: November 14

User experience and data science

algorithmic interpretibility; human-centered methods for designing and evaluating algorithmic systems

Assignments due

• Reading reflection
• A5: Final project plan

Agenda

• coming soon

Homework assigned

• Reading and reflect: Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, III, Miro Dudik, and Hanna Wallach. 2019. 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
• A6: Final project presentation

Resources

• Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Making recommendations better: an analytic model for human-recommender interaction. In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06).
• Sean M. McNee, Nishikant Kapoor, and Joseph A. Konstan. 2006. Don't look stupid: avoiding pitfalls when recommending research papers. In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work (CSCW '06).
• Shahriari, K., & Shahriari, M. (2017). 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 Statement on Algorithmic Transparency and Accountability. January 2017.
• Diakopoulos, N., Friedler, S., Arenas, M., Barocas, S., Hay, M., Howe, B., … Zevenbergen, B. (2018). Principles for Accountable Algorithms and a Social Impact Statement for Algorithms. Fatml.Org 2018.
• Morgan, J. 2016. Evaluating Related Articles recommendations. Wikimedia Research.
• Morgan, J. 2017. Comparing most read and trending edits for the top articles feature. Wikimedia Research.
• Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User perception of differences in recommender algorithms. In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14).

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Week 9: November 21

Data science in context

Doing human centered datascience in product organizations; communicating and collaborating across roles and disciplines; HCDS industry trends and trajectories

Assignments due

• Reading reflection

Agenda

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

Homework assigned

• Read and reflect: Passi, S., & Jackson, S. J. (2018). 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 (ACCESS PDF HERE)
• A7: Final project report

Resources

• Rich Caruana, Harsha Nori, Samuel Jenkins, Paul Koch, Ester de Nicolas. 2019. InterpretML software toolkit (github repo, blog post)
• Partnership on AI, 2019 Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System.
• Morgan, J. T., 2019. Ethical and Human-centered AI at Wikimedia. Wikimedia Research 2030​.

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Week 10: November 28 (No Class Session)

Assignments due

• Reading reflection

Homework assigned

• 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 (PDF available on Canvas)

Resources

• Marilynn Larkin, How to give a dynamic scientific presentation. Elsevier Connect, 2015.
• Megan Risdal, Communicating data science: a guide to presenting your work. Kaggle blog, 2016.
• Megan Risdal, Communicating data science: Why and how to visualize information. Kaggle blog, 2016.
• Megan Risdal, Communicating data science: an interview with a storytelling expert. Kaggle blog, 2016.
• Brent Dykes, Data Storytelling: The Essential Data Science Skill Everyone Needs. Forbes, 2016.

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Week 11: December 5

Final presentations

presentation of student projects, course wrap up

Assignments due

• Reading reflection
• A5: Final presentation

Readings assigned

• NONE

Homework assigned

• NONE

Resources

• NONE

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Week 12: Finals Week (No Class Session)

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

Assignments

For details on individual assignments, see Human Centered Data Science (Fall 2019)/Assignments

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.

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

Unless otherwise noted, all assignments are individual assignments.

Assignment timeline

Assignments due every week

• In-class activities - 2 points (weekly): In-class activity output posted to Canvas (group or individual) within 24 hours of class session.
• Reading reflections - 2 points (weekly): Reading reflections posted to Canvas (individual) before following class session.

Scheduled assignments

• A1 - 5 points (due 10/3): Data curation (programming/analysis)
• A2 - 10 points (due 10/17): Bias in data (programming/analysis)
• A3 - 10 points (due 10/31): Crowdwork Ethnography (written)
• A4 - 5 points (due 11/7): Final project proposal (written)
• A5 - 5 points (due 11/14): Final project plan (written)
• A6 - 10 points (due 12/5): Final project presentation (oral, slides)
• A7 - 15 points (due 12/10): Final project report (programming/analysis, written)

more information...

Policies

The following general policies apply to this course.

Attendance and participation

Students are expected to attend class regularly. If you run into a conflict that requires you to be absent (for example, medical issues) feel free to reach out to the instructors. We will do our best to ensure that you don’t miss out, and treat your information as confidential.

If you miss class session, please do not ask the professor or TA what you missed during class; check the website or ask a classmate (best bet: use Slack). Graded in-class activities cannot be made up if you miss a class session (without prior approval or a specific accommodation--see below).

Grading

Active participation in class activities is one of the requirements of the course. You are expected to engage in group activities, class discussions, interactions with your peers, and constructive critiques as part of the course work. This will help you hone your communication and other professional skills. Correspondingly, working in groups or on teams is an essential part of all data science disciplines. As part of this course, you will be asked to provide feedback of your peers' work.

Individual assignments will have specific requirements listed on the assignment sheet, which the instructor will make available on the day the homework is assigned. If you have questions about how your assignment was graded, please see the TA or instructor.

Assignments and coursework

Grades will be determined as follows:

• 20% in-class work
• 20% reading reflections
• 60% assignments

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.

By default, 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. Final projects cannot be turned in late and are not eligible for any extension without prior written permission. Requests for special dispensation on final project due dates must be submitted to the instructor via email no less than 2 week before the final project deadline.

Academic integrity and plagiarism

Students are expected to adhere to rules around academic integrity. Simply stated, academic integrity means that you are to do your own work in all of your classes, unless collaboration is part of an assignment as defined in the course. In any case, you must be responsible for citing and acknowledging outside sources of ideas in work you submit.

Please be aware of the HCDE Department's and the UW's policies on plagiarism and academic misconduct: HCDE Academic Conduct policy. This policy will be strictly enforced.

The University takes academic integrity very seriously. Behaving with integrity is part of our responsibility to our shared learning community. If you’re uncertain about if something is academic misconduct, ask me. I am willing to discuss questions you might have.

Acts of academic misconduct may include but are not limited to:

• Cheating (working collaboratively on quizzes/exams and discussion submissions, sharing answers and previewing quizzes/exams)
• Plagiarism (representing the work of others as your own without giving appropriate credit to the original author(s))
• Unauthorized collaboration (working with each other on assignments that are intended to be completed on an individual basis)

Concerns about these or other behaviors prohibited by the Student Conduct Code will be referred for investigation and adjudication by (include information for specific campus office).

Students found to have engaged in academic misconduct may receive a zero on the assignment (or other possible outcome).

Other academic integrity resources:

• Center for Teaching and Learning: Cheating or Plagiarism
• University of Washington Student Academic Responsibility (PDF)

Disability accommodations

Your experience in this class is important to me. If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

For more information on disability accommodations, and how to apply for one, please review UW's Disability Resources for Students.

Religious accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Faculty Syllabus Guidelines and Resources. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form available at https://registrar.washington.edu/students/religious-accommodations-request/.

Conduct and safety

The University of Washington Student Conduct Code (WAC 478-121) defines prohibited academic and behavioral conduct and describes how the University holds students accountable as they pursue their academic goals. Allegations of misconduct by students may be referred to the appropriate campus office for investigation and resolution. More information can be found online at https://www.washington.edu/studentconduct/

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.

This course takes place in the evening, during the darkest part of the year. Call SafeCampus at 206-685-7233 anytime – no matter where you work or study – to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support, while discussing short- and long-term solutions and connecting you with additional resources when requested.

Disclaimer

This syllabus and all associated assignments, requirements, deadlines and procedures are subject to change.