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Re-Empower the People with Data Visualization and Game Design

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Re-Empower the People with Data Visualization and Game Design

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Slides for an upcoming interactice talk at MozFest (https://www.mozillafestival.org) exploring how information design and game design can help address common challenges in AI transparency efforts. Looks at multiple open source examples.

Slides for an upcoming interactice talk at MozFest (https://www.mozillafestival.org) exploring how information design and game design can help address common challenges in AI transparency efforts. Looks at multiple open source examples.

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Re-Empower the People with Data Visualization and Game Design

  1. 1. A Samuel Pottinger and Varsha Gopalakrishnan Re-Empower the People with Data Visualization and Game Design
  2. 2. 👋 Hello! Sam Pottinger Varsha Gopalakrishnan What makes for meaningful transparency? Introduction Challenge Discussion Workshop Setting the stage Discussion Examples Explore *Views our own and not reflective of another organization. See notice. Analysis Represent Context Participate
  3. 3. 🗺 Overview Discussion Workshop Setting the stage Discussion Examples Explore Why should I care about my data? This presentation is focused on the flow of information with regards to the public. Consider the example of personalization: there is a value add for the public in exchange for data. However, the same data offers different actors different capabilities and those capabilities change differently with scale. Introduction Challenge Analysis Represent Context Participate
  4. 4. 🏔 The Challenge: context Introduction Challenge Discussion Workshop Setting the stage Discussion Examples Explore What does disclosure really mean? Even when representative data are disclosed, a disclosure on something doesn’t mean that the reporting is actually informative. Asymmetry: One party has access to contextual or distributional data while the other does not. Analysis Represent Context Participate
  5. 5. 🏔 The Challenge: analysis Introduction Challenge Discussion Workshop Setting the stage Discussion Examples Explore What is meaningful publication of data? Just because data are made public in their entirety doesn’t mean that those data are useful. Asymmetry: Tools available to one party to make data useful are not available to another party. Analysis Represent Context Participate
  6. 6. 🏔 The Challenge: representation Introduction Challenge Discussion Workshop Setting the stage Discussion Examples Explore What does data access provide? Asymmetry can be created by data aggregation or sampling. Just by providing access to data doesn’t mean that it is representative or inclusive. Asymmetry: One party becomes more represented than another. Median income: $139k Number of sensors per 10,000 people: 15.4 Median income: $87k Number of sensors per 10,000 people: 4.3 Analysis Represent Context Participate
  7. 7. 🏔 The Challenge: participation Introduction Challenge Discussion Workshop Setting the stage Discussion Examples Explore What consent is really given? Often interaction with systems isn’t really always a choice we get to make. Asymmetry: Different parties’ ability to decide if they want to participate in a system or not. Analysis Represent Context Participate
  8. 8. 🏔 The Challenge Introduction Challenge Discussion Workshop Setting the stage Discussion Examples Explore Even when data are released, there is often asymmetry: ● Access to tools used to understand its data. ● The context needed to make those data useful. ● In decision to use or be subject to a system. Analysis Represent Context Participate
  9. 9. Introduction Challenge Discussion Workshop Setting the stage Discussion Examples Explore 🗺 What’s going on? Data in systems The fields of systems design and game design offer some help in understanding what’s going on here. Differences between actors in a system creates asymmetric design. This systems lens helps us see common issues for AI transparency efforts and what might be done to help. Analysis Represent Context Participate
  10. 10. 🏔 The Challenge Introduction Discussion Workshop Setting the stage Discussion Examples Explore Let’s talk: What day to day AI systems most concern you? Challenge Analysis Represent Context Participate
  11. 11. ⭐ Solutions Introduction Workshop Setting the stage Discussion Examples Explore Challenge Discussion Let’s turn to solutions Now that we have understood common problems, what can be done? Analysis Represent Context Participate
  12. 12. ⭐ Solution Example: context Introduction Analysis Represent Workshop Setting the stage Discussion Examples Explore Start-Up Options Bot Democratizing otherwise expensive complex mathematical modeling and visualization tools to empower the public. Pattern: Offer tools for contextualization of data to reduce information asymmetry. Tool: Information design Demo Challenge Discussion Context Participate
  13. 13. ⭐ Solution Example: analysis Introduction Workshop Setting the stage Discussion Examples Explore Colorado TRACER Analysis Leverage the types of algorithms used on voters but turn them instead to political donors on a similar dataset. Pattern: Provide the same capabilities to all actors for deriving value from data. Tool: Machine learning Link Challenge Discussion Analysis Represent Context Participate
  14. 14. ⭐ Solution Example: representation Introduction Workshop Setting the stage Discussion Examples Explore Interactive hyperlocal air quality Allow individuals to assert their representation in the dataset. Pattern: Allow for individualization in advocacy. Tool: Information design Link Challenge Discussion Analysis Represent Context Participate
  15. 15. ⭐ Solution Example: participation Introduction Workshop Setting the stage Discussion Examples Explore FoodSim: San Francisco Making public data actionable through game design and modeling, allowing the public a foothold for informed participation. Pattern: Lower the barrier for entry, allowing a broader group of people to choose to participate. Tool: Game design Demo Challenge Discussion Analysis Represent Context Participate
  16. 16. 🗣 Conversation Introduction Workshop Setting the stage Discussion Explore Let’s talk: What AI system would we want to change or democratize? Challenge Discussion Examples Analysis Represent Context Participate
  17. 17. 🔧 Example Introduction Workshop Setting the stage Explore Let’s talk: How might we invert or democratize the example system from the last slide? Challenge Discussion Examples Discussion Analysis Represent Context Participate
  18. 18. [ Appendix ]
  19. 19. 📝 License / notice Creative Commons These slides are made available under the CC BY-NC-SA 3.0 license. Link Views are our own The views expressed are those of Sam and Varsha. They do not attempt to reflect those of any other organization or any work cited. Open science All works cited are publicly available. All demos linked are pre-existing open source contributions. See Works Cited for code and data.
  20. 20. 📚 Works Cited Bell-Mayeda, Melanie. “What Is Systems Design? How to Surface Opportunities for Change.” IDEO U, IDEO, https://www.ideou.com/blogs/inspiration/what-is-systems-design-how-to-surface-opportunities-for-change. Burgun, Keith. “Asymmetry in Games.” Game Developer, Informa, 1 Oct. 2015, https://www.gamedeveloper.com/design/asymmetry-in-games. Crockford, Kade. “How Is Face Recognition Surveillance Technology Racist?: News & Commentary.” American Civil Liberties Union, 16 June 2020, https://www.aclu.org/news/privacy-technology/how-is-face-recognition-surveillance-technology-racist. Gopalakrishnan, Varsha. “Hyperlocal Air Quality Prediction Using Machine Learning.” Medium, Towards Data Science, 8 Feb. 2021, https://towardsdatascience.com/hyperlocal-air-quality-prediction-using-machine-learning-ed3a661b9a71. Harwell, Drew, and Nick Miroff. “Ice Just Abandoned Its Dream of 'Extreme Vetting' Software That Could Predict Whether a Foreign Visitor Would Become a Terrorist.” The Washington Post, WP Company, 5 Dec. 2021, https://www.washingtonpost.com/news/the-switch/wp/2018/05/17/ice-just-abandoned-its-dream-of-extreme-vetting-software-that-could-predict-whether-a-foreign-visitor-would-become-a-terrorist/. Hodge, Rae. “Police Will Get AI-Powered License Plate Readers, but Ethical Concerns Remain.” CNET, CNET, 24 Oct. 2019, https://www.cnet.com/home/security/police-will-get-ai-powered-license-plate-readers-but-ethical-concerns-remain/. “Home.” TRACER, Colorado Secretary of State, https://tracer.sos.colorado.gov/PublicSite/HomePage.aspx. Jung, Yoohyun, and Danielle Echeverria. “Where Low Cost Air Quality Sensors Are - and Aren't - in the Bay Area.” The San Francisco Chronicle, The San Francisco Chronicle, 11 Oct. 2021, https://www.sfchronicle.com/projects/2021/purple-air-monitors-california/. Kramer, Adam D., et al. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” Proceedings of the National Academy of Sciences, vol. 111, no. 24, 2 June 2014, pp. 8788–8790., https://doi.org/10.1073/pnas.1320040111. Pottinger, A S. “FoodSim: San Francisco.” FoodSim, Sam Pottinger, 16 Apr. 2023, https://foodsimsf.com/. Pottinger, A S. “Startup Options Bot.” Startup Options Bot, Sam Pottinger, 20 Oct. 2022, https://startupoptionsbot.com/. Pottinger, A S. “TRACER Analysis.” Sam Pottinger, 2013, https://gleap.org/content/tracer_analysis. Ryan-Mosley, Tate. “A New Map of NYC's Cameras Shows More Surveillance in Black and Brown Neighborhoods.” MIT Technology Review, MIT Technology Review, 14 Feb. 2022, https://www.technologyreview.com/2022/02/14/1045333/map-nyc-cameras-surveillance-bias-facial-recognition/. Smith, Stacey Vanek. “The Big Reveal: New Laws Require Companies to Disclose Pay Ranges on Job Postings.” NPR, NPR, 5 Nov. 2022, https://www.npr.org/2022/11/05/1134193927/salary-transparency-range-new-york-pay-laws. “Welcome to My Activity.” Google, Google, https://myactivity.google.com/activitycontrols.
  21. 21. 🌇 Image Credits Lucas Clara Unsplash License Redd F Unsplash License John Cameron Unsplash License Johnathan Borba Unsplash License

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