When Machines Decide: The Promise and Peril of Living in a Data-Driven Society: Discussion Leader Sign-Up

Honors 3700-002, Fall, 2016-Spring 2017 Thursdays 2:00 pm-5:00 pm MHC 1205

Discussion Leads

Discussion Leader by Week

Team Assignments

Week 2: 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. Team 1
Week 3: 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. Team 2
Week 4: 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. Team 3
Week 5: 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; Team 4
Week 6: 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. Team 1
Week 7: Existing laws, regulations, policies and best practices potentially applicable to the collection, management, use, analysis & privacy of data; ethical issues. Team 1
Week 9 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 real-time situational threat assessment; the specter of “big brother” and mass surveillance; the loss of the human element in crime interdiction; data and sentencing. Team 2
Week 10: 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 Team 3
Week 11: Understanding customer needs; optimizing work flows, sales, marketing, distribution and manufacturing; improved financial performance; consent and privacy issues; data management and security issues. Team 4
Week 12: 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. Team 1
Marriott Library Eccles Library Quinney Law Library