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
| 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 8: NO CLASS FALL BREAK | |
| 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 |
