Algorithmic Literacy and the Role for Libraries
DOI:
https://doi.org/10.6017/ital.v40i2.12963Abstract
Artificial intelligence (AI) is powerful, complex, ubiquitous, often opaque, sometimes invisible, and increasingly consequential in our everyday lives. Navigating the effects of AI as well as utilizing it in a responsible way requires a level of awareness, understanding, and skill that is not provided by current digital literacy or information literacy regimes. Algorithmic literacy addresses these gaps. In arguing for a role for libraries in algorithmic literacy, the authors provide a working definition, a pressing need, a pedagogical strategy, and two specific contributions that are unique to libraries.
References
“2019 Edelman AI Survey,” Edelman, 2019, https://www.edelman.com/sites/g/files/aatuss191/files/2019-03/2019_Edelman_AI_Survey_Whitepaper.pdf.
Alejandro Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI,” ArXiv:1910.10045 [Cs], 2019, http://arxiv.org/abs/1910.10045.
Alfred Thomas Bauer and Ebrahim Mohseni Ahooei, “Rearticulating Internet Literacy,” Cyberspace Studies 2, no. 1 (2018): 29–53, https://doi.org/10.22059/jcss.2018.245833.1012.
Alison J. Head, Barbara Fister, and Margy MacMillan, “Information Literacy in the Age of Algorithms: Student Experiences with News and Information, and the Need for Change” (Project Information Literacy, 2020), 41, https://www.projectinfolit.org/uploads/2/7/5/4/27541717/algoreport.pdf.
Allan Martin and Jan Grudziecki, “DigEuLit: Concepts and Tools for Digital Literacy Development,” Innovation in Teaching and Learning in Information and Computer Sciences 5, no. 4 (2006): 249–67, https://doi.org/10.11120/ital.2006.05040249.
Altexsoft, “Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson,” Data Science (blog), September 27, 2019, https://www.altexsoft.com/blog/datascience/comparing-machine-learning-as-a-service-amazon-microsoft-azure-google-cloud-ai-ibm-watson/.
Andreas Dengel and Ute Heuer, “A Curriculum of Computational Thinking as a Central Idea of Information & Media Literacy,” in Proceedings of the 13th Workshop in Primary and Secondary Computing Education (WiPSCE’18) October 4-6, 2018, Potsdam, Germany (New York: ACM, 2018), https://doi.org/10.1145/3265757.3265777.
Annemaree Lloyd, “Chasing Frankenstein’s Monster: Information Literacy in the Black Box Society,” Journal of Documentation 75, no. 6 (2019): 1476, https://doi.org/10.1108/JD-02-2019-0035.
Ashraf Abdul et al., “Trends and Trajectories for Explainable, Accountable, and Intelligible Systems: An HCI Research Agenda,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI ’18 (New York: ACM, 2018), 582:1–582:18, https://doi.org/10.1145/3173574.3174156.
Association of College and University Libraries, “Framework for Information Literacy for Higher Education,” 2015, http://www.ala.org/acrl/standards/ilframework.
B. Alexander, S. Adams Becker, and M. Cummins, “Digital Literacy: An NMC Horizon Project Strategic Brief” (Austin, TX: The New Media Consortium, 2016), https://www.nmc.org/publication/digital-literacy-an-nmc-horizon-project-strategic-brief/.
Betual C. Czerkawski and Eugene W. Lyman III, “Exploring Issues about Computational Thinking in Higher Education,” TechTrends 59, no. 2 (2015): 57–65.
Bryce Goodman and Seth Flaxman, “European Union Regulations on Algorithmic Decision Making and a ‘Right to Explanation,’” AI Magazine 38, no. 3 (2017): 50–57, https://doi.org/10.1609/aimag.v38i3.2741.
“Canada’s AI Imperative: From Predictions to Prosperity” (Toronto: Deloitte, 2019), 16, https://www.canada175.ca/en/reports/ai-imperative?&id=ca:2el:3or:awa_2019_FCC_Omnia1:from_dca_FCCOmnia2.
Canadian Federation of Library Associations, “Artificial Intelligence and Intellectual Freedom: Key Policy Concerns for Canadian Libraries” (Ottawa: CFLA, 2018), http://cfla-fcab.ca/wp-content/uploads/2018/07/CFLA-FCAB-2018-National-Forum-Paper-final.pdf.
Catherine Coleman, “Artificial Intelligence and the Library of the Future, Revisited,” Digital Library Blog (blog), November 3, 2017, https://library.stanford.edu/blogs/digital-library-blog/2017/11/artificial-intelligence-and-library-future-revisited.
Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York: Crown, 2016).
Daniel Zeng, “From Computational Thinking to AI Thinking,” IEEE Intelligent Systems (November/December, 2013), 2–4.
DARPA, “Explainable Artificial Intelligence (XAI)” (Arlington, VA: DARPA, 2016), http://www.darpa.mil/attachments/DARPA-BAA-16-53.pdf.
David Bawden, “Information and Digital Literacies: A Review of Concepts,” Journal of Documentation 57, no. 2 (2001): 233.
Duri Long and Brian Magerko, “What Is AI Literacy? Competencies and Design Considerations,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20 (Honolulu, HI: Association for Computing Machinery, 2020), 1–16, https://doi.org/10.1145/3313831.3376727.
Ed Finn, “Algorithm of the Enlightenment,” Issues in Science and Technology 33, no. 3 (2017): 25.
Ed Finn, What Algorithms Want: Imagination in the Age of Computing (Cambridge, MA: MIT Press, 2017), 2.
Edward Greenspon and Taylor Owen, “Democracy Divided: Countering Disinformation and Hate in the Digital Public Sphere” (Ottawa: Public Policy Forum, 2018), 19, https://ppforum.ca/wp-content/uploads/2018/08/DemocracyDivided-PPF-AUG2018-EN.pdf.
European Union, “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016,” 2016, http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679.
Evelyn Stiller and Cathie LeBlanc, “From Computer Literacy to Cyber-Literacy,” Journal of Computing Sciences in Colleges 21, no. 6 (2006): 4–13.
Finland, “Work in the Age of Artificial Intelligence: Four Perspectives on Economy, Employment, Skills and Ethics” (Helsinki: Ministry of Economic Affairs and Employment, 2018), http://urn.fi/URN:ISBN:978-952-327-313-9.
Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information (Cambridge, MA: Harvard University Press, 2015).
George Steiner, “School Terms: Redefining Literacy for the Digital Age,” Lapham’s Quarterly 1, no. 4 (2008): 198.
Jason Clark, “Building Competencies around Algorithmic Awareness” (Washington, DC: Code4Lib, 2018), https://www.lib.montana.edu/~jason/talks/algorithmic-awareness-talk-code4lib2018.pdf.
Jason Clark, Algorithmic Awareness (2018; repr., GitHub, 2020), https://github.com/jasonclark/algorithmic-awareness.
Jason Clark, Lisa Janicke Hinchliffe, and Scott Young, “Unpacking the Algorithms That Shape Our UX” (Washington, DC: IMLS, 2017), https://www.imls.gov/sites/default/files/grants/re-72-17-0103-17/proposals/re-72-17-0103-17-full-proposal-documents.pdf.
Jeannette Wing, “Computational Thinking, 10 Years Later,” Communications of the ACM 59, no. 7 (2016): 10, https://doi.org/10.1145/2933410.
Jeannette Wing, “Computational Thinking,” Communications of the ACM 49, no. 3 (2006): 35.
Jenna Burrell, “How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms,” Big Data & Society 3, no. 1 (2016), https://doi.org/10.1177/2053951715622512.
Jesús Moreno-León and Marcos Román-González, “On Computational Thinking as a Universal Skill,” in IEEE Global Engineering Education Conference (EDUCON, Santa Cruz de Tenerife, Spain: IEEE, 2018), 1684–89.
Jos de Mul and Bibi van den Berg, “Remote Control: Human Autonomy in the Age of Computer-Mediated Agency,” in Law, Human Agency, and Autonomic Computing, ed. Mireille Hildebrandt and Antoinette Rouvroy (Abingdon: Routledge, 2011), 58.
Julia Angwin et al., “Machine Bias,” ProPublica, May 23, 2016, https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
Karen Nicholson, “Information into Action? Reflections on (Critical) Practice” (Workshop on Instruction in Library Use (WILU), University of Ottawa, 2018), 7–8, https://ir.lib.uwo.ca/fimspres/51/.
Lee Rainie and Janna Anderson, “Code-Dependent: Pros and Cons of the Algorithmic Age” (Pew Research Center, February 2017), http://www.pewinternet.org/wp-content/uploads/sites/9/2017/02/PI_2017.02.08_Algorithms_FINAL.pdf.
Loanne Snavely and Natasha Cooper, “The Information Literacy Debate,” Journal of Academic Librarianship 23, no. 1 (1997): 9–14, https://doi.org/10.1016/S0099-1333(97)90066-5.
Marcos Román-González, Juan-Carlos Pérez-González, and Carmen Jiménez-Fernández, “Which Cognitive Abilities Underlie Computational Thinking? Criterion Validity of the Computational Thinking Test,” Computers in Human Behavior 72 (2017): 678–91, https://doi.org/dx.doi.org/10.1016/j.chb.2016.08.047.
Matt Turek, “Explainable Artificial Intelligence (XAI)” (Arlington, VA: DARPA, 2016), https://www.darpa.mil/program/explainable-artificial-intelligence.
Michael Ridley, “Explainable Artificial Intelligence,” Research Library Issues, no. 299 (2019): 28–46, https://doi.org/10.29242/rli.299.3.
Motahhare Eslami et al., “First I ‘Like’ It, Then I Hide It: Folk Theories of Social Feeds,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI ’16 (San Jose, CA: Association for Computing Machinery, 2016), 2371–82, https://doi.org/10.1145/2858036.2858494.
Nicholas Diakopoulos, “Algorithmic Accountability Reporting: On the Investigation of Black Boxes” (New York: Tow Center for Digital Journalism, Columbia University, 2014), 2, https://doi.org/10.7916/D8ZK5TW2.
Nick Seaver, “Algorithms as Culture: Some Tactics for the Ethnography of Algorithm Systems,” Big Data & Society 4 (2017): 10, https://doi.org/10.1177/2053951717738104.
Nick Seaver, “What Should an Anthropology of Algorithms Do?” (American Anthropological Association, Chicago, 2013), 1–2, http://nickseaver.net/papers/seaverAAA2013.pdf.
Paul T. Jaeger et al., “The Intersection of Public Policy and Public Access: Digital Divides, Digital Literacy, Digital Inclusion, and Public Libraries,” Public Library Quarterly 31, no. 1 (2012): 1, https://doi.org/10.1080/01616846.2012.654728.
Peter J. Denning and Matti Tedre, Computational Thinking (Cambridge MA: MIT Press, 2019).
PriceWaterhouseCooper, “Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise?,” 2017, https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf.
R. David Lankes, “Decoding AI and Libraries,” R. David Lankes (blog), July 3, 2019, https://davidlankes.org/decoding-ai-and-libraries/.
Rob Kitchin, “Thinking Critically about and Researching Algorithms,” Information, Communication & Society 20, no. 1 (2017): 18, https://doi.org/10.1080/1369118X.2016.1154087.
Rosanne Cordell, “Information Literacy and Digital Literacy: Competing or Complementary?,” Communications in Information Literacy 7, no. 2 (2013): 177–83, https://doi.org/10.15760/comminfolit.2013.7.2.150.
Ruha Benjamin, Race after Technology: Abolitionist Tools for the New Jim Code (Polity Press, 2019).
Ryan Cordell, “Machine Learning + Libraries: A Report on the State of the Field” (Washington DC: Library of Congress, 2020), 31, https://labs.loc.gov/static/labs/work/reports/Cordell-LOC-ML-report.pdf.
Safiya Noble, Algorithms of Oppression: How Search Engines Reinforce Racism (New York: New York University Press, 2018), 1.
Sarah Gretter and Aman Yadav, “Computational Thinking and Media & Information Literacy: An Integrated Approach to Teaching Twenty-First Century Skills,” TechTrends 60 (2016): 510–16, https://doi.org/10.1007/s11528-016-0098-4.
Sayamindu Dasgupta and Benjamin Mako Hill, “Designing for Critical Algorithmic Literacies,” ArXiv:2008.01719 [Cs], 2020, http://arxiv.org/abs/2008.01719.
Scott Lash, “Power after Hegemony: Cultural Studies in Mutation?,” Theory, Culture & Society 24, no. 3 (2007): 71, https://doi.org/10.1177/0263276407075956.
Sharin Rawhiya Jacob and Mark Warschauer, “Computational Thinking and Literacy,” Journal of Computer Science Integration 1, no. 1 (2018): 3, https://doi.org/10.26716/jcsi.2018.01.1.1.
Shuchi Grover and Roy Pea, “Computational Thinking in K–12: A Review of the State of the Field,” Educational Researcher 42, no. 1 (2013): 38–43, https://doi.org/10.3102/0013189X12463051.
Sun-Ha Hong, Technologies of Speculation: The Limits of Knowledge in a Data-Driven Society (New York: New York University Press, 2020), 2.
Sylvia Scribner and Michael Cole, The Psychology of Literacy, ACLS Humanities E-Book (Series) (Cambridge, MA: Harvard University Press, 1981), 99.
Taina Bucher, “The Algorithmic Imaginary: Exploring the Ordinary Affects of Facebook Algorithms,” Information, Communication & Society 20, no. 1 (2017): 42, https://doi.org/10.1080/1369118X.2016.1154086.
Taina Bucher, If . . . Then: Algorithmic Power and Politics (New York: Oxford University Press, 2018), 20.
Tanya Kant, Making It Personal: Algorithmic Personalization, Identify, and Everyday Life (Oxford: Oxford University Press, 2020), 152.
Tarleton Gillespie, “The Relevance of Algorithms,” in Media Technologies: Essays on Communication, Materiality, and Society, ed. Tarleton Gillespie, Pablo J. Boczkowski, and Kirsten A. Foot (Cambridge, MA: MIT Press, 2014), 184.
Thomas H. Cormen et al., Introduction to Algorithms, 3rd ed. (Cambridge MA: MIT Press, 2009), 13.
Thomas Padilla, Responsible Operations. Data Science, Machine Learning, and AI in Libraries (Dublin, OH: OCLC Research, 2019), 10, https://doi.org/10.25333/xk7z-9g97.
Time Dutton, “Building an AI World: Report on National and Regional AI Strategies” (Toronto: CIFAR, 2018), https://www.cifar.ca/docs/default-source/ai-society/buildinganaiworld_eng.pdf?sfvrsn=fb18d129_4.
Ting-Chia Hsu, Shao-Chen Chang, and Yu-Ting Hung, “How to Learn and How to Teach Computational Thinking: Suggestions Based on a Review of the Literature,” Computers & Education 126 (2018): 296–310, https://doi.org/10.1016/j.compedu.2018.07.004.
“ULC Snapshot: Artificial Intelligence,” Urban Libraries Council Weekly Newsletter, July 18, 2018.
Virginia Eubanks, Automating Inequity: How High-Tech Tools Profile, Police, and Punish the Poor (New York: St. Martin’s Press, 2018).
“What Consumers Really Think about AI: A Global Study,” Pega, 2019, https://www.ciosummits.com/what-consumers-really-think-about-ai.pdf.
Wojciech Samek and Klaus-Robert Muller, “Towards Explainable Artificial Intelligence,” in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, ed. Wojciech Samek et al., 2019., Lecture Notes in Artificial Intelligence 11700 (Cham: Springer International Publishing, 2019), 5–22.
Yoav Shohman et al., “AI Index 2017 Report” (Stanford, CA: Human-Centered AI Initiative, Stanford University, 2017), http://cdn.aiindex.org/2017-report.pdf.
Z. Katai, “The Challenge of Promoting Algorithmic Thinking of Both Sciences- and Humanities-Oriented Learners,” Journal of Computer Assisted Learning 31, no. 4 (2015): 287–99, https://doi.org/10.1111/jcal.12070.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Michael Ridley, Danica Pawlick-Potts
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors that submit to Information Technology and Libraries agree to the Copyright Notice.
