When Machines Decide: The Promise and Peril of Living in a Data-Driven Society: Syllabus
KEY DATES FOR FALL SEMESTER
|August 25, 2016||Student survey completed; selection/assignment of student class discussion leader teams.|
|September 1, 2016||Personal bios posted on Library Guide;|
|September 8, 2016||Selection of individual research topics|
|November 17, 2016||Student oral presentations on individual research projects|
|December 1, 2016||Student oral presentations on proposed Team Projects|
|December 8, 2016||Final selection and preliminary planning on Team Projects|
Week 1: August 25---Introduction to the Course
Introductions, review of syllabus and LibGuide, completion of student survey, selection of student research topics and discussion leaders, ice-breaker exercise,other administrative information, etc.
Week 2: September 1---What is Big Data & Why is it Important?
The characteristics of big data, the three “V’s”; how does big data differ from small data?; the life-cycle of big data; the growing importance of big data; how big data is and will be affecting our daily lives; some examples of high value big data uses.
- The Human Face of Big Data,” PBS (February 25, 2016) available at https://www.youtube.com/watch?v=r6v15Z60eUI (52 minutes in length)
- “What is Big Data and Why Should You Care? Forbes (April 22, 2016) available at https://www.youtube.com/watch?v=jGhRiwGHh30 (2:45 minutes in length)
- “The Impact of Big Data on Business Efficiency,” insidebigdata.com (July 30,2016) available at http://insidebigdata.com/2016/07/30/the-impact-of-big-data-on-business-efficiency/
Week 3: September 8---The Collection, Consolidation, and Storage of Data
The generators of data (e.g. social networks, mobile devices, geo-fencing, internet of things, businesses, governments, etc.); the aggregators of data (e.g. data brokers, businesses, government, etc.); data storage, data management & processing technologies (e.g. Hadoop, MapReduce, Spark, Pig, NoSQL, etc.); public and private databases.
- Bernard Marr, “Big Data: 33 Brilliant and Free Data Sources for 2016,” Forbes.co (February 12, 2016) available at
- Review the website of “Persona Non Data: Art in the Age of Big Data,”artisopensource.net available at
- Brian Naylor, “Firms Are Buying, Sharing Your Online Info. What Can You Do About It?” NPR (July 11, 2016) available at http://www.npr.org/sections/alltechconsidered/2016/07/11/485571291/firms-arebuying-sharing-your-online-info-what-can-you-do-about-it
- Kaushik Pal, “Impact of IoT on Big Data Landscape,” KD Nuggets (July 2015) available at http://www.kdnuggets.com/2015/07/impact-iot-big-data-landscape.html
Week 4: September 15--- The Mining, Analysis and Use of Data
The ABC’s of algorithms, machine learning and artificial intelligence; descriptive vs.
predictive analysis; de-identification and re-identification; reliability and limitations of big data analytics.
- Jennifer Golbeck, “How to Teach Yourself About Algorithms,” (February 9, 2016) Slate, available at http://www.slate.com/articles/technology/future_tense/2016/02/how_to_teach_yourself_about_algorithms.html
- Jacob Brogan, “What’s the Deal With Algorithms,” (February 2, 2016) Slate, available at http://www.slate.com/articles/technology/future_tense/2016/02/what_is_an_algorithm_an_explainer.html?wpsrc=sh_all_dt_tw_top
- Suresh Venkat, “When An Algorithm Isn’t…,” October 2, 2015 available at https://medium.com/@geomblog/when-an-algorithm-isn-t-2b9fe01b9bb5
- Ed Finn, “Algorithms Aren’t Like Spock,” (February 22, 2016) Slate, available at http://www.slate.com/articles/technology/future_tense/2016/02/algorithms_are_like_kirk_not_spock.html
- Jeff Hawkins & Donna Dubinsky, “What is Machine Intelligence vs. Machine Learning vs. Deep Learning vs. Artificial Intelligence?”, KD Nuggets (January 2016) available at http://numenta.com/blog/2016/01/11/machine-intelligence-machine-learning-deep-learning-artificial-intelligence/
- Molly Galetto, “What is Big Data Analytics,” ngdata.com (July 5, 2016) available at http://www.ngdata.com/what-is-big-data-analytics/
- Alex Hern, “Google says machine learning is the future so I tried it myself”, The Guardian (June 28, 2016). Available at https://www.theguardian.com/technology/2016/jun/28/google-says-machine-learning-is-the-future-so-i-tried-it-myself
- Homework/classroom activity on the use of machine learning for building a model.
Week 5: September 22---An Overview of the Potential Benefits & Dangers of Big Data
The benefits of better decision-making, financial gain & advancing social good; the risks of bias, discrimination, exploitation, inequity and inequality; loss of privacy and surveillance and tracking by business and government;
- Madeleine Clare Elish, “Algorithms Can Make Good Co-Workers,” (February 22, 2016) Slate, available at http://www.slate.com/articles/technology/future_tense/2016/02/algorithms_can_make_good_co_workers.html
- Kenneth Cukier, “Big Data Dystopia,” Tedx Talks (April 14, 2014) available at https://www.youtube.com/watch?v=Z_HdhhzG-b0
- Maciej Cegtowski, “Haunted By Data,”October 1, 2015 available at http://idlewords.com/talks/haunted_by_data.htm
- Michael Gregg, “How Data Brokers Threaten Consumer Privacy,” Huffington Post (April 12, 2016) available at http://www.huffingtonpost.com/michael-gregg/howdata-brokers-threaten_b_9661468.html
- “Data Broker Defendants Settle FTC Charges They Sold Sensitive Personal Information to Scammers”, FTC press release (February 18, 2016) available at https://www.ftc.gov/news-events/press-releases/2016/02/data-broker-defendants-settle-ftc-charges-they-sold-sensitive
- “The Ethics of Data – Education & Self-management,” BBC Research & Development (March 16, 2016) available at https://www.youtube.com/watch?v=naaDBNSx610 (5:55 minutes in length)
- David Ingold & Spencer Soper, “Amazon Doesn’t Consider the Race of its Customers. Should it,” Bloomberg News (April 21, 2016) available at http://www.bloomberg.com/graphics/2016-amazon-same-day/
- Laura Sydell, “Can Computer Programs Be Racist & Sexist?” National Public Radio (March 15, 2016) available at http://www.npr.org/sections/alltechconsidered/2016/03/15/470422089/cancomputer-programs-be-racist-and-sexist
Week 6: September 29--- A Deeper Dive into Algorithms & Fairness Issues
What are the different types of algorithms? How do they work? Are algorithms fair, discriminatory, racist, biased etc.? Can they substitute for human judgment? Transparency, accountability and ethical issues issues.
- A Report on Algorithmic Systems, Opportunity and Civil Rights, Executive Office of the President (May 2016) available at https://www.whitehouse.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf
- How big data is unfair. Moritz Hardt. Available at https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de#.69j789w6z
- Why Machines Discriminate---and How to Fix Them, Science Friday podcast (November 20, 2015) featuring Professor Venkat and Microsoft research Kate Crawford available at http://www.sciencefriday.com/segments/why-machines-discriminate-and-how-to-fix-them/
- Hanna Wallach, “Big Data, Machine Learning and the Social Sciences: Fairness, Accountability and Transparency” (December 19, 2014) available at https://medium.com/@hannawallach/big-data-machine-learning-and-the-socialsciences-927a8e20460d
- Taylor Owen, “The Violence of Algorithms: Why big data is only as smart as those who generate it.” (May 25, 2015) Foreign Affairs. Available at https://www.foreignaffairs.com/articles/2015-05-25/violence-algorithms
- Jonathan Zittrain, “Automation and Algorithms in the Digital Age,” World Economic Forum, (February 24, 2015) available at https://www.youtube.com/watch?v=I6gD7Yq-_jk (6:03 minutes in length)
- “The Ethics of Data – Personal Data & Privacy” BBC Research & Development (March 16, 2016) available at https://www.youtube.com/watch?v=naaDBNSx610 (6:14 minutes in length)
- Robyn Caplan & laura Reed, “Who Controls the Public Sphere in an Era of Algorithms: Case Studies,” Data & Society (May 16, 2016) available at http://www.datasociety.net/pubs/ap/CaseStudies_PublicSphere_2016.pdf
- Dario Borghino, “Would You Buy a Car Programmed to Kill You For the Greater Good?” newatlas.com (June 23, 2016) available at http://newatlas.com/driverless-car-ethics/43926/
- Go to the MIT “Moral Machine” website at http://moralmachine.mit.edu/ and complete the exercise involving autonomous vehicles.
- “Professor Sorelle Friedler on Discriminatory Machine Learning,” Data & Society Research Institute (November 30, 2015) available at https://www.youtube.com/watch?v=8qXCi41jNvs (6:16 minutes)
Week 7: October 6---The Regulation/Governance of Big Data
Existing laws, regulations, policies and best practices potentially applicable to the collection, management, use, analysis & privacy of data; ethical issues.
- “Big Data: A Tool for Inclusion or Exclusion?” Federal Trade Commission Report (January 2016) available at https://www.ftc.gov/system/files/documents/reports/big-data-tool-inclusion-or-exclusion-understanding-issues/160106big-data-rpt.pdf
- John k. Higgins, “FTC Issues Regulatory Warning on Big Data Use,” Ecommerce Times (January 20, 2016) available at http://www.ecommercetimes.com/story/83004.html
- Nicholas Diakopoulos, “How to Hold Governments Accountable for the Algorithms They Use,” Slate.com (February 11, 2016) available at http://www.slate.com/articles/technology/future_tense/2016/02/how_to_hold_governments_accountable_for_their_algorithms.html
- Metcalf, Jacob, Emily F. Keller, and Danah Boyd, “Perspectives on Big Data, Ethics, and Society.” Council for Big Data, Ethics, and Society. (May 23, 2016) http://bdes.datasociety.net/council-output/perspectives-on-big-data-ethics-and-society/.
- Omer Tene & Jules Polonetsky, “Beyond IRB’s: Ethical Guidelines for Data Research,” Washington and Lee Law Review Online (June 7, 2016) available at http://scholarlycommons.law.wlu.edu/cgi/viewcontent.cgi?article=1044&context=wlulr-online
- Julia Angwin, “Make Algorithms Accountable”, New York Times (August 1, 2016) available at http://www.nytimes.com/2016/08/01/opinion/make-algorithms-accountable.html
- Joshua A. Kroll, Joanna Huey, Solon Barzas, Edward W. Felten, Joel R. Reidenberg, David G. Robinson & Harlan Yu, “Accountable Algorithms,” (March 31, 2016) available at http://balkin.blogspot.com/2016/03/accountable-algorithms.html
- Cathy O’Neil, “The Ethical Data Scientist,” Slate (February 4, 2016) available at http://www.slate.com/articles/technology/future_tense/2016/02/how_to_bring_better_ethics_to_data_science.html
- Brainstorming on potential Spring semester Team Projects
Week 8: October 13---No Class- Fall Break
Week 9: October 20---Big Data and Law Enforcement
The benefits and dangers associated with using big data for crime prevention, interdiction and in the criminal justice system; predictive policing, data consolidation and sharing, identifying crime patterns and using big data for realtime situational threat assessment; the specter of “big brother” and mass surveillance; the loss of the human element in crime interdiction; data and sentencing.
- “How Predictive Policing Software Works,” The Verge (February 3, 2016) available at https://www.youtube.com/watch?v=YxvyeaL7NEM (2:04 minutes)
- Jason Tashea, “Websites and Apps for Sharing Crime and Safety Have Become Outlets for Racial Profiling,” ABA Journal (August 1, 2106) available at http://www.abajournal.com/magazine/article/crime_safety_website_racial_profiling
- “Is Predictive Policing the Law-Enforcement Tactic of the Future?” Wall Street Journal (April 24, 2016) available at http://www.wsj.com/articles/is-predictive-policing-the-law-enforcement-tactic-of-the-future-1461550190
- Hector Chaidez, “Interactive Predictive Policing Program in South Pasadena, California,” (July 25, 2016) available at https://www.youtube.com/watch?v=LqoFk0Y3XXg (11:26 minutes)
- Thomas H. Davenport, “How Big Data is Helping the NYPD Solve Crimes Faster,” Fortune.com (July 17, 2016) available at http://fortune.com/2016/07/17/big-data-nypd-situational-awareness/
- “Algorithms in the Criminal Justice System,” Electronic Privacy Information Center available at https://epic.org/algorithmic-transparency/crim-justice/
- Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner, “Machine Bias,” ProPublica (May 23, 2016) available at https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
- Response to ProPublica article by developer of computer program available at https://www.documentcloud.org/documents/2998391-ProPublica-Commentary-Final-070616.html
- ProPublica rejoinder to developer’s response available at https://www.propublica.org/article/propublica-responds-to-companys-critique-of-machine-bias-story
Week 10: October 27--- Big Data and Education
Use of big data for admissions, retention, identifying students at risk, predicting success, course scheduling, student advising and delivery of courses. University use of outside vendors or data analytics; privacy and data security issues
- Candace M. Thille, “As Big Data Companies Come to Teaching, a Pioneer Issues a Warning,” Chronicle of Higher Education (February 23, 2016) available at http://chronicle.com/article/As-Big-Data-Companies-Come-to/235400
Bridget Burns, “Big Data’s Coming of Age in Higher Education,”Forbes.com (January 29, 2016) available at http://www.forbes.com/sites/schoolboard/2016/01/29/big-datas-coming-of-age-in-higher-education/#2f6ba732a325
Mikaela Pitcan, “Real Life Harms of Student Data,” Data & Society Research Institute (June 16, 2016) available at https://points.datasociety.net/real-life-harms-of-student-data-956a30aaff32#.z185lmtuy
Read pages 21-26 of Elana Zeide, “19 Ways Data Analysis Empowered Students and Schools,” Future of Privacy Forum (March 16, 2016) available at https://fpf.org/wp-content/uploads/2016/03/Final_19Times-Data_Mar2016-1.pdf
Goldie Blumenstyk, “As Big Data Comes to College, Officials Wrestle to Set New Ethical Norms”, The Chronicle of Higher Education (June 28, 2016) available at http://chronicle.com/article/As-Big-Data-Comes-to-College/236934
Jeffrey R. Young, “This Chart Shows the Promise and Limits of ‘Learning Analytics’”, The Chronicle of Higher Education ( January 4, 2016) available at http://chronicle.com/article/This-Chart-Shows-the-Promise/234573
- Mike Martineau, Director of Institutional Analysis, University of Utah
Week 11: November 3---Big Data and Business
Understanding customer needs and how businesses are using machines to make decisions not only in marketing and retail sales, but also in making hiring and other human resources decisions; consent and privacy issues when businesses collect information about consumers.
- Lisa Morgan, Big Data: 6 Real-Life Business Cases, Information Week August 30, 2016) available at http://www.informationweek.com/software/enterpriseapplications/big-data-6-real-life-business-cases/d/did/1320590?image_number=1
- Bernard Marr, “The 18 Best Analytic Tools Every Business Manager Should Know,” Forbes.com (February 4, 2016) available at http://www.forbes.com/sites/bernardmarr/2016/02/04/the-18-best-analytics-tools-every-business-manager-should-know/#257deeae2c4a
- Rob Marvin, “Predictive Analytics, Big Data, and How to Make Them Work for You,” pcmag.com (July 12, 2016) available at http://www.pcmag.com/article/345858/predictive-analytics-big-data-and-how-to-make-them-work-fo
- Bernard Marr, “Four Ways Big Data Will Change Every Business,” Forbes.com September 8, 2015) available at http://www.forbes.com/sites/bernardmarr/2015/09/08/4-ways-big-data-will-change-every-business/#6c4956ff7900
Trevor Dryer, Founder and CEO, Mirador
Week 12: November 10---Big Data and Healthcare
Precision medicine, electronic health records, privacy of medical information, aggregation and sharing of records, transparency, medical device wearables, improved efficiency, patient outcomes and reduction of costs.
- Donna Marbury, “Making Sense of Big Data: Data Projects Spur Progress,”modernmedicine.com (July 3, 2016) available at http://managedhealthcareexecutive.modernmedicine.com/managed-healthcare-executive/news/making-sense-big-data-data-projects-spur-progress?page=0,0
- Adam Tanner, “How Data Brokers Make Money Off Your Medical Records,”Scientific American (February 1, 2016) available at http://www.scientificamerican.com/article/how-data-brokers-make-money-off-your-medical-records/
- Agata Kwapien, “Top 5 Examples of Big Data in Healthcare That Can Save Lives,” Datapine.com (February 24, 2016) available at http://www.datapine.com/blog/big-data-examples-in-healthcare/
- Dylan Scott, What Does the Mormon Church have to do With Biden’s Cancer Moonshot?” Statnews.com (February 26, 2016 available at https://www.statnews.com/2016/02/26/biden-cancer-moonshot-utah/
- John Russell, “Obama, NIH Asnnounce Big Data Gathering Push for Precision Medicine,” hpcwire.com (July 7, 2016) available at https://www.hpcwire.com/2016/07/07/obama-nih-announce-big-data-gathering-push-precision-medicine/
- Tiffany Trader, “This Hospital Computer Knows When Your Days Are Numbered,” hpcwire.com (September 25, 2015) available at https://www.hpcwire.com/2015/09/25/this-hospital-computer-knows-when-your-days-are-numbered/ (read article and view embedded video)
- Muqbil Ahmar, Big Data Analytics and IoT Can Solve Some of the Hardest Medical Problems,” techfirstpost.com (July 5, 2016) available at http://tech.firstpost.com/biztech/big-data-analytics-and-iot-can-solve-some-of-the-hardest-medical-problems-323803.html
Willard H. Dere, MD, Director of the Program in Personalized Health and Co-Director of Center for Clinical and Translational Science at the University of Utah Health Care System.
Loren Larsen, Chief Technology Officer, HireVue
Week 13: November 17---Student Oral Presentations on Research Projects
Week 14: November 24---No Class - Thanksgiving Holiday
Week 15: December 1---Student Oral Presentation on Proposed Team Projects
Week 16: December 8---Final Selection and Preliminary Planning on Team Projects: Discuss detailed description of team project(s) and determin milestones and assignments over the semester break