class: center, middle, inverse, title-slide .title[ # Data Ethics ] .subtitle[ ## STAT 4380 (in-class) ] .author[ ### Katie Fitzgerald ] --- layout: true <div class="my-footer"> <span> <a href="https://nova-stat-4380.netlify.app" target="_blank">nova-stat-4380.netlify.app</a> </span> </div> --- ## Warm-up We've encountered several data ethics issues so far in this class. Which one(s) are you most concerned about? Why? + Bias & Representation + Gender data gap + Algorithmic bias + Missing datasets (some groups/issues never measured) + Data privacy + (mis)use of hacked data + (mis)use of data by corporations, surveillance + (mis)use of data by government, surveillance + Visual misrepresentation of data + Weakening federal data infrastructure --- class: middle ## Why might different people worry about different issues? --- ## Social Identity Activity On your four notecards, write your: 1. gender 1. race/ethnicity 1. religious/spiritual affiliation 1. an additional aspect of your choosing that reflects something about who you are (e.g., major, hobby, familial identity). On a separate piece of paper, identify a place off-campus that you go regularly (e.g., grocery store, coffee shop, bar, park, place of worship, concert venue, etc) --- class: middle ## When you are at HOME, which of your identities are you most aware of / which is most important in that context? Order your cards from most aware to least aware --- class: middle ## When you are on Villanova's campus (but outside class)? --- class: middle ## What about when you are in a MATH/STAT class? --- class: middle ## What about when you are at [the location of your choosing]? --- ## Social Identity Debrief + Did the order of your identities change across spaces? + Were there places where you felt more visible or less visible? In positive or negative ways? + Did you notice any identities that rarely came to mind? --- ## Our identities shape our perspectives, experiences, worldviews + It's important to work on diverse teams! + Data is collected, analyzed, and interpreted by people whose perspectives and identities shape what they notice, measure, and optimize. --- ## How do we talk about ethics? How do we make ethical decisions? It's helpful to move beyond vague notions of "this seems bad" or "this seems okay" and be able to **articulate why**. This skill will serve you well in your future (data science) careers, especially as you navigate decisions within teams and organizations. + Virtue ethics + Deontological ethics + Utilitarian ethics Sometimes these lead to different decisions! --- ## Values activity + Begin by circling the values in the list that are most important to you. + One way to start to do this is to look back on your life to identify when you felt good or confident about a choice. For example, learning to play a sport, instrument or some other skill that involve practice- then maybe you value physical activity, competition or challenge. + Try to choose 20-25 top values. -- + After you have circled your 20-25 top values, put a W next to the circled values that might inform where you would choose to work when you graduate. --- class: middle ## Case studies --- class: middle ## Data Ethics Day 2: your careers & lives Go to this padlet: [padlet.com/kfitzg24/data_ethics_4380](https://padlet.com/kfitzg24/data_ethics_4380) --- ## Let's brainstorm with AI Go to your preferred AI platform, and brainstorm with it using the following prompts: 1. I'm a student in a data science course. I am interested in pursuing a career in ______. How is data typically used in this field? What kinds of decisions do data and data scientists influence in this field? 2. What ethical risks or controversies have arisen in this field related to data, algorithms, or analytics? Give concrete examples. 3. Imagine I am a data scientist working in this field. What kinds of ethical dilemmas might I realistically encounter in my job? 4. If I wanted to practice responsible data science in this field, what safeguards or habits should I adopt? --- ## Small Group discussion + Compare AI's ideas with your own - what did it add? What did it miss? + What ethical issue(s) surprised you? + What types of decisions do data (scientists) actually influence in your field? + What safeguards seemed most important? --- ## Whole class reflection This semester we’ve seen that: + governments collect and use enormous datasets + corporations track huge amounts of behavior + algorithms influence decisions about jobs, credit, policing, etc **The question is not whether data will be used.** **The question is: what structures make its use responsible?** *What would need to exist in society for this power to be used responsibly?* --- ## (at least) three layers of responsibility 1. Inside organizations (companies, government agencies) + What safeguards should exist before & after data is collected, or data systems are built & deployed? 2. Societal infrastructure + What broader systems are needed? 3. Individual responsibility + What can data scientists and citizens actually do? Discuss in groups and add your ideas to this padlet: [padlet.com/kfitzg24/ethical_brainstorm](https://padlet.com/kfitzg24/ethical_brainstorm) --- class: middle ## Ethical data science doesn't happen by default - it's done intentionally by people who take responsibility for building better systems