Human Centered Data Science (Fall 2019)/Schedule



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.

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.
 * 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.
 * 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.

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

Week 4: October 17

 * Introduction to 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 review
 * Review of assignment 2
 * Survey of qualitative research methods
 * Mixed-methods case study
 * Introduction to ethnography
 * Ethnographic research case study
 * In-class activity
 * 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


 * Qualitative and mixed-methods research resources
 * Ford, D., Smith, J., Guo, P. J., & Parnin, C. (2016). Paradise unplugged: Identifying barriers for female participation on stack overflow. Proceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering, 13-18-Nove, 846–857. https://doi.org/10.1145/2950290.2950331
 * 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.
 * Usability.gov, System usability scale.
 * Nielsen, Jakob (2000). Why you only need to test with five users. nngroup.com.


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

Week 5: October 24

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


 * Assignments due
 * Reading reflection


 * Agenda
 * Reading reflection review
 * A brief history of research ethics in the United States
 * Research ethics in data science
 * Technological approaches to data privacy
 * Guest lecture
 * Procedural approaches to data privacy


 * 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.
 * Hill, Kashmir. Facebook Manipulated 689,003 Users' Emotions For Science. Forbes, 2014.
 * Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock Experimental evidence of massive-scale emotional contagion through social networks. PNAS 2014 111 (24) 8788-8790; published ahead of print June 2, 2014.
 * Barbaro, Michael and Zeller, Tom. A Face Is Exposed for AOL Searcher No. 4417749. New York Times, 2008.
 * Zetter, Kim. Arvind Narayanan Isn’t Anonymous, and Neither Are You. WIRED, 2012.
 * Gray, Mary. When Science, Customer Service, and Human Subjects Research Collide. Now What? Culture Digitally, 2014.
 * Tene, Omer and Polonetsky, Jules. Privacy in the Age of Big Data. Stanford Law Review, 2012.
 * Dwork, Cynthia. Differential Privacy: A survey of results. Theory and Applications of Models of Computation, 2008.
 * Hsu, Danny. Techniques to Anonymize Human Data. Data Sift, 2015.

Week 6: October 31

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


 * Assignments due
 * Reading reflection
 * A3: Crowdwork ethnography


 * Agenda
 * Reading reflections
 * Assignment 3 review
 * 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
 * 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.

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.
 * 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.
 * Asilomar AI Principles. Future of Life Institute, 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.
 * 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.
 * Google's Perspective API
 * 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).

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
 * Ethical OS Toolkit and Risk Mitigation Checklist. EthicalOS.org.
 * Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Making recommendations better: an analytic model for human-recommender interaction. In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHI EA '06).
 * Sean M. McNee, Nishikant Kapoor, and Joseph A. Konstan. 2006. Don't look stupid: avoiding pitfalls when recommending research papers. In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work (CSCW '06).
 * Michael D. Ekstrand and Martijn C. Willemsen. 2016. Behaviorism is Not Enough: Better Recommendations through Listening to Users. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16).
 * Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
 * Anderson, Carl. The role of model interpretability in data science. Medium, 2016.
 * Fabien Girardin. Experience design in the machine learning era. Medium, 2016.
 * Xavier Amatriain and Justin Basilico. Netflix Recommendations: Beyond the 5 stars. Netflix Tech Blog, 2012.
 * Jess Holbrook. Human Centered Machine Learning. Google Design Blog. 2017.
 * Bart P. Knijnenburg, Martijn C. Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. 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, Facebook, Google, and Microsoft Use Design to Trick You Into Handing Over Your Data, New Report Warns. Gizmodo, 6/18/2018
 * Cremonesi, P., Elahi, M., & Garzotto, F. (2017). User interface patterns in recommendation-empowered content intensive multimedia applications. Multimedia Tools and Applications, 76(4), 5275-5309.

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
 * coming soon


 * Homework assigned
 * Read and reflect: Alkhatib, A., & Bernstein, M. (2019). Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3290605.3300760
 * A7: Final project report


 * Resources

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.

Week 11: December 5

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


 * Assignments due
 * Reading reflection
 * A5: Final presentation


 * Readings assigned
 * NONE


 * Homework assigned
 * NONE


 * Resources
 * NONE

Week 12: Finals Week (No Class Session)

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