ABSTRACT
Machine learning systems are increasingly a part of everyday life, and often used to make critical and possibly harmful decisions that affect stakeholders of the models. Those affected need enough literacy to advocate for themselves when models make mistakes. To understand how to develop this literacy, this paper investigates three ways to teach ML concepts, using linear regression and gradient descent as an introduction to ML foundations. Those three ways include a basic Facts condition, mirroring a presentation or brochure about ML, an Impersonal condition which teaches ML using some hypothetical individual's data, and a Personal condition which teaches ML on the learner's own data in context. Next, we evaluated the effects on learners' ability to self-advocate against harmful ML models. Learners wrote hypothetical letters against poorly performing ML systems that may affect them in real-world scenarios. This study discovered that having learners learn about ML foundations with their own personal data resulted in learners better grounding their self-advocacy arguments in the mechanisms of machine learning when critiquing models in the world.
- Ruth E. Anderson, Michael D. Ernst, Robert Ordóñez, Paul Pham, and Ben Tribelhorn. 2015. A Data Programming CS1 Course. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (SIGCSE '15). ACM, New York, NY, USA, 150--155. https://doi.org/10.1145/2676723.2677309Google ScholarDigital Library
- Mad Price Ball. [n. d.]. Open Humans. https://www.openhumans.org/.Google Scholar
- Austin Cory Bart, Dennis Kafura, Cliford A Shafer, and Eli Tilevich. 2018. Reconciling the Promise and Pragmatics of Enhancing Computing Pedagogy with Data Science. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education. ACM, 1029--1034.Google ScholarDigital Library
- Austin Cory Bart, Ryan Whitcomb, Dennis Kafura, Cliford A Shafer, and Eli Tilevich. 2017. Computing with corgis: Diverse, real-world datasets for introductory computing. ACM Inroads 8, 2 (2017), 66--72.Google ScholarDigital Library
- Rahul Bhargava and Catherine D'Ignazio. [n. d.]. Designing tools and activities for data literacy learners.Google Scholar
- Nick Bostrom. [n. d.]. The Vulnerable World Hypothesis. ([n. d.]).Google Scholar
- Anna Brown, Alexandra Chouldechova, Emily Putnam-Hornstein, Andrew Tobin, and Rhema Vaithianathan. 2019. Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-making in Child Welfare Services. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 41.Google ScholarDigital Library
- Ed Burns. 2018. Search Enterprise AI Current state of AI is poorly understood by the public. https://searchenterpriseai.techtarget.com/opinion/ Current-state-of-AI-is-poorly-understood-by-the-public.Google Scholar
- Howard Chen. 2018. MSandE 238 Blog Public perception of artifcial intelligence. https://mse238blog.stanford.edu/2018/07/howachen/ public-perception-of-artifcial-intelligence/.Google Scholar
- National Research Council et al. 2000. How people learn: Brain, mind, experience, and school: Expanded edition. National Academies Press.Google Scholar
- Imad Dabbura. 2018. Predicting Loan Repayment. https://towardsdatascience. com/predicting-loan-repayment-5df4e0023e92.Google Scholar
- Sayamindu Dasgupta and Benjamin Mako Hill. 2017. Scratch community blocks: Supporting children as data scientists. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 3620--3631.Google ScholarDigital Library
- Erica Deahl. 2014. Better the data you know: Developing youth data literacy in schools and informal learning environments. Available at SSRN 2445621 (2014).Google Scholar
- John Dewey. 1986. Experience and education. In The Educational Forum, Vol. 50. Taylor & Francis, 241--252.Google Scholar
- Catherine D'Ignazio and Rahul Bhargava. 2016. Data Basic: Design principles, tools and activities for data literacy learners. The Journal of Community Informatics 12, 3 (2016).Google ScholarCross Ref
- Stefania Druga, Sarah T Vu, Eesh Likhith, and Tammy Qiu. 2019. Inclusive AI literacy for kids around the world. In Proceedings of FabLearn 2019. ACM, 104--111.Google ScholarDigital Library
- Catherine D'Ignazio and Rahul Bhargava. [n. d.]. Approaches to building big data literacy.Google Scholar
- Catherine D'Ignazio and Lauren F Klein. 2016. Feminist data visualization. In Workshop on Visualization for the Digital Humanities (VIS4DH), Baltimore. IEEE.Google Scholar
- Norma González, Luis C Moll, and Cathy Amanti. 2006. Funds of knowledge: Theorizing practices in households, communities, and classrooms. Routledge.Google Scholar
- Norma Gonzalez, Luis C Moll, Martha Floyd Tenery, Anna Rivera, Patricia Rendon, Raquel Gonzales, and Cathy Amanti. 1995. Funds of knowledge for teaching in Latino households. Urban Education 29, 4 (1995), 443--470.Google ScholarCross Ref
- Dan Goodley. 2000. Self-advocacy in the lives of people with learning diffculties: The politics of resilience. Open University Press Buckingham.Google Scholar
- Dan Goodley. 2005. Empowerment, self-advocacy and resilience. Journal of Intellectual Disabilities 9, 4 (2005), 333--343.Google ScholarCross Ref
- Samantha Hautea, Sayamindu Dasgupta, and Benjamin Mako Hill. 2017. Youth perspectives on critical data literacies. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 919--930.Google ScholarDigital Library
- Birte Heinemann, Simone Opel, Lea Budde, Carsten Schulte, Daniel Frischemeier, Rolf Biehler, Susanne Podworny, and Thomas Wassong. 2018. Drafting a data science curriculum for secondary schools. In Proceedings of the 18th Koli Calling International Conference on Computing Education Research. 1--5.Google ScholarDigital Library
- Tom Hitron, Yoav Orlev, Iddo Wald, Ariel Shamir, Hadas Erel, and Oren Zuckerman. 2019. Can Children Understand Machine Learning Concepts?: The Efect of Uncovering Black Boxes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 415.Google ScholarDigital Library
- Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, and Steven M Drucker. 2019. Gamut: A design probe to understand how data scientists understand machine learning models. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 579.Google ScholarDigital Library
- C. D. Kidd and C. Breazeal. 2004. Efect of a robot on user perceptions. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), Vol. 4. 3559--3564 vol.4. https://doi.org/10.1109/IROS.2004.1389967Google Scholar
- Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2017. Human decisions and machine predictions. The quarterly journal of economics 133, 1 (2017), 237--293.Google ScholarCross Ref
- Konstantina Kourou, Themis P Exarchos, Konstantinos P Exarchos, Michalis V Karamouzis, and Dimitrios I Fotiadis. 2015. Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal 13 (2015), 8--17.Google Scholar
- Sean Kross and Philip J Guo. 2019. Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges. (2019).Google Scholar
- Duri Long and Brian Magerko. 2020. What is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1--16.Google ScholarDigital Library
- Camillia Matuk, Anna Amato, Kayla DesPortes, Marian Tes, Veena Vasudevan, Susan Yoon, Jooeun Shim, Amanda Cottone, Kate Miller, Blanca Himes, et al. [n. d.]. Data Literacy for Social Justice. ([n. d.]).Google Scholar
- Luis C Moll, Cathy Amanti, Deborah Nef, and Norma Gonzalez. 1992. Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms. Theory into practice 31, 2 (1992), 132--141.Google Scholar
- Engineering National Academies of Sciences, Medicine, et al. 2018. How people learn II: Learners, contexts, and cultures. National Academies Press.Google Scholar
- Greg L Nelson, Benjamin Xie, and Amy J Ko. 2017. Comprehension frst: evaluating a novel pedagogy and tutoring system for program tracing in CS1. In Proceedings of the 2017 ACM Conference on International Computing Education Research. ACM, 2--11.Google ScholarDigital Library
- Andrew Ng. 2011. Machine Learning Coursera Course. https://www.coursera.org/learn/machine-learning.Google Scholar
- Cathy O'Neil. 2016. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.Google Scholar
- Seymour Papert. 1980. Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.Google ScholarDigital Library
- Seymour Papert. 1999. Papert on piaget. Time magazine, pág 105 (1999).Google Scholar
- Evan M Peck, Sofa E Ayuso, and Omar El-Etr. 2019. Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, 244.Google ScholarDigital Library
- Javier Calzada Prado and Miguel Ángel Marzal. 2013. Incorporating data literacy into information literacy programs: Core competencies and contents. Libri 63, 2 (2013), 123--134.Google Scholar
- Milo Schield. 2004. Information literacy, statistical literacy and data literacy. In Iassist Quarterly (IQ). Citeseer.Google Scholar
- Anneliese A Singh, Sarah E Meng, and Anthony W Hansen. 2014. "I am my own gender": Resilience strategies of trans youth. Journal of counseling & development 92, 2 (2014), 208--218.Google ScholarCross Ref
- Jenny Slater. 2012. Self-advocacy and socially just pedagogy. Disability Studies Quarterly 32, 1 (2012).Google Scholar
- Elisabeth Sulmont, Elizabeth Patitsas, and Jeremy R Cooperstock. 2019. Can You Teach Me To Machine Learn?. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education. 948--954.Google ScholarDigital Library
- Elisabeth Sulmont, Elizabeth Patitsas, and Jeremy R Cooperstock. 2019. What is hard about teaching machine learning to non-majors? Insights from classifying instructors' learning goals. ACM Transactions on Computing Education (TOCE) 19, 4 (2019), 1--16.Google ScholarDigital Library
- Dave Touretzky. 2019. AI4K12. https://github.com/touretzkyds/ai4k12/wiki.Google Scholar
- Sherry Turkle and Seymour Papert. 1990. Epistemological pluralism: Styles and voices within the computer culture. Signs: Journal of women in culture and society 16, 1 (1990), 128--157.Google ScholarCross Ref
- James Vincent. 2017. The Verge Robots and AI are going to make social inequality even worse, says new report. https://www.theverge.com/2017/7/13/15963710/robots-ai-inequality-social-mobility-study.Google Scholar
- Thomas Way, Lillian Cassel, Paula Matuszek, Mary-Angela Papalaskari, Divya Bonagiri, and Aravinda Gaddam. 2016. Broader and earlier access to machine learning. In Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education. 362--362.Google ScholarDigital Library
- Thomas Way, Mary-Angela Papalaskari, Lillian Cassel, Paula Matuszek, Carol Weiss, and Yamini Praveena Tella. 2017. Machine learning modules for all disciplines. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education. 84--85.Google ScholarDigital Library
- Michelle Hoda Wilkerson and Joseph L Polman. 2020. Situating data science: Exploring how relationships to data shape learning. Journal of the Learning Sciences 29, 1 (2020), 1--10.Google ScholarCross Ref
- Yin-Ling IWong, Trevor R Hadley, Dennis P Culhane, Stephen R Poulin, Morris R Davis, Brian A Cirksey, and James L Brown. 2006. Predicting staying in or leaving permanent supportive housing that serves homeless people with serious mental illness. Departmental Papers (SPP) (2006), 111.Google Scholar
- Benjamin Xie, Greg L Nelson, and Amy J Ko. 2018. An explicit strategy to scafold novice program tracing. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education. ACM, 344--349.Google ScholarDigital Library
- Moira L Zellner, Leilah B Lyons, Charles J Hoch, Jennifer Weizeorick, Carl Kunda, and Daniel C Milz. 2012. Modeling, Learning, and Planning Together: An Application of Participatory Agent-based Modeling to Environmental Planning. Journal of the Urban & Regional Information Systems Association 24, 1 (2012).Google Scholar
- Baobao Zhang and Allan Dafoe. 2019. Artifcial Intelligence: American Attitudes and Trends. Available at SSRN 3312874 (2019).Google Scholar
- Abigail Zimmermann-Niefeld, Makenna Turner, Bridget Murphy, Shaun K Kane, and R Benjamin Shapiro. 2019. Youth Learning Machine Learning through Building Models of Athletic Moves. In Proceedings of the 18th ACM International Conference on Interaction Design and Children. 121--132.Google ScholarDigital Library
Index Terms
- Learning Machine Learning with Personal Data Helps Stakeholders Ground Advocacy Arguments in Model Mechanics
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