Door Prizes. Exploring Big Issues with Data in Society: Using Case Studies with Students

Transcription

1 7/10/18 Exploring Big Issues with Data in Society: Using Case Studies with Students Door Prizes Kristin Fontichiaro University of Michigan School of Information 4T Virtual Conference on Data Literacy July 11-12, 2018 A project of the University of Michigan School of Information, and ICPSR, with help from the U-M School of Education. This project was made possible in part by the Institute of Museum and Library Services RE Door Prize: 3 Months of Easel.ly Pro Clients and Partners Sought! The University of Michigan School of Information offers organizations an opportunity to benefit from students' developing skill sets through client-based courses and programs. Students are available to: Establish and maintain internal library systems or archival collections Provide community profiles to inform questions affecting cities, non-profits, or libraries Organize and analyze information systems Improve information flow through process analysis Provide user experience and design recommendations for websites, apps, and software Gather, manipulate, analyze, and visualize data Develop, implement, and analyze social media strategy Analyze or develop guidelines for online communities Develop a content management system (CMS) or interactive website Contact umsi.client.engagement@umich.edu Visit umsi.info/clientopportunities to learn more and propose a project. 1

2 SCECHs Log in with your full name Attend all of at least 4 live sessions and up to 8 Scan/photograph your form and to contact.data.literacy@umich.edu by 11:59pm Eastern on Friday, July 13. More information: For non-michigan educators needing proof of attendance Follow SCECH instructions (scan and ). Plus u Go to u Pay $25 there no later than 11:59pm Eastern on Friday, July 13. u Submit your form to 4t.data@gmail.com #4tdl 2

3 Exploring Big Issues with Data in Society: Using Case Studies with Students Kristin Fontichiaro University of Michigan School of Information 4T Virtual Conference on Data Literacy July 11-12, 2018 Kristin Fontichiaro A project of the University of Michigan School of Information and ICPSR, with help from the U-M School of Education. This project was made possible in part by the Institute of Museum and Library Services RE Today, we ll 1. Discuss some common examples of data in our world 2. Take a sample case study for a spin 3. Discuss possibilities for integration in your PD or classroom 1. When did you interact with a data tracker today? What did the tracker collect about you, and what did you get back? Share your interactions at 3

4 7/10/18 Your answers may have included: Fitness tracker (e.g., Fitbit, Garmin) GPS ebook service like Overdrive, Audible, or Kindle Maps service (e.g., Apple or Google Maps) Cable TV (via cable box or app) Social media Online searches and results (search engines and databases) IoT thermostats (Nest), door locks Personal assistant like Google Home or Amazon Echo Illustration by Arthur Rackham. Public domain. etel#/media/file:hansel-and-gretel-rackham.jpg We re all Hansels and Gretels. 4

5 7/10/ The Problem of Normalizing Surveillance - Doug Levin, blog/hacking-the-iste18-smart-badge-part-ii/ Photos by Doug Levin, blog/hacking-the-iste18-smart-badge-part-ii/ 5

6 Let s do a poll Do the benefits of the smart badge outweigh the concerns? Choose your best answer. A. Absolutely yes! What a cool value-add to my conference attendance. B. Not really, but I really don t care who knows where I went, so I m OK wearing a Smart Badge. C. I don t care one way or another. D. Not really, and I hope my principal doesn t ask for my Journey document, but I don t want to look like a Luddite at a tech conference, so I m not going to say anything. E. Absolutely not. Where do I surrender my Smart Badge? Other things to consider 1. Levin realizes that number on paper badge number = Smart Badge code. So is it anonymous? 2. Research shows that a fair amount of anonymized data can be re-identified. 3. Who benefits most? So is the benefit worth the cost? There s no right answer it depends! I m not here to say, Don t engage with smart or Internet of Things objects. I m saying that we need to think before we use. 6

7 Who is teaching students about ambient data gathering and how it might be used? but how? Maybe it s you. PART I: Webinar Discussion Sheets based on archived webinars from the 2016 & 2017 conferences PART II: Case Studies 7

8 7/10/18 Today s Case Study Amazon Echo Look 8

9 A E: Fast Company Design: fastcodesign.com/ /amazons-new-ai-selfie-machineisthe-privacy-parable-for-our-time Assigned Readings! F J: Engadget: K O: CNN: P S: Forbes: -amazon-really-created-echo-look-a-camera-in-your-bedroom-andbathroom/2/#50f e5 T Z: Tufecki Twitter thread (scroll all the day down!): Take 5 to skim your article (open a new tab so we can call you back!) Let s discuss! 9

10 Provide the article source (e.g., CNN, Forbes), then at least one main or impactful idea from your reading. A. What are the advantages/disadvantages of this $200 tool for users? For Amazon? B. What are the potential benefits/risks on this tool on someone s self-esteem? C. What else might be recorded, as Amazon Look is on all the time? (In marketing materials, the device is shown in the bedroom. Other digital assistants like Amazon Echo, Apple HomePod, or Google Home tend to be in public spaces. Does that make a difference?) 10

11 D. Some machine learning projects do a better job identifying Caucasian faces as humans than African-American faces. What would the implications be if Amazon Look is better at working with one skin type than another? A rhetorical set of questions before we do our final question: Whose standards determine whether you look good? How will you know what data was used to train the algorithm that is passing judgment on your fashion sense? Selected additional case studies from the book: E. FINAL QUESTION: Would you buy this product? For whom? Why or why not? Social media tracking Fitness trackers Smart home devices in court Hiding from digital marketing Internet providers selling your data Civic engagement Encrypted data Privacy US vs. EU approaches to privacy Reading terms of service Cambridge Analytica Unroll.me Big Data and discrimination Smart TVs and data Self-driving trucks Predictive policing Big Data, banking, and loans Student predictive analytics data Cross-conversion tracking Mechanical Turk Data and surveillance on the vulnerable Protecting student data What happens to student SAT and ACT data Surveillance cameras in special ed Student privacy and cloud storage Big Data and government nudges Hyped-up data use and swaying public opinion Chicago, Flint, and haves/haven-nots in data use Internet privacy policy changes Data philanthropy Plus: citizen science 11

12 Recap: 1. Kids need practice turning over data concepts and making their own decisions. 2. Those decisions are everywhere! 3. Case studies can help structure those conversations in a non-judgmental way. Questions? Kristin Fontichiaro U-M School of Info. dataliteracy.si.umich.edu Please take the evaluation (link in chat) to be entered for door prizes! 12