AI Literacy: Notes, References & Resources

Definitions

Reading development involves a range of complex language underpinnings, including awareness of speech sounds (phonology), spelling patterns (orthography), word meaning (semantics), syntax, and patterns of word formation (morphology), all of which provide a necessary platform for reading fluency and comprehension.

Research into digital literacies draws from traditions of information literacy and research into media literacy which rely on socio-cognitive traditions, as well as research into multimodal composition, which relies on anthropological methodologies.[4] Digital literacy is built on the expanding role of social science research in the field of literacy[5] as well as on concepts of visual literacy,[6] computer literacy,[7] and information literacy.[8]

Media literacy is a broadened understanding of literacy that encompasses the ability to access, analyze, evaluate, and create media in various forms.[1][2]

Examples of media literacy include reflecting on one’s media choices,[5] identifying sponsored content,[6] recognizing stereotypes,[7] analyzing propaganda[8] and discussing the benefits, risks, and harms of media use.[9] Critical analysis skills can be developed through practices like constructivist media decoding[10] and lateral reading,[11] which entails looking at multiple perspectives in assessing the quality of a particular piece of media.[12]

Data literacy refers to the ability to understand, interpret, critically evaluate, and effectively communicate data in context to inform decisions and drive action. It is not a technical skill but a fundamental capability for everyone, encompassing the skills and mindset necessary to transform raw data into meaningful insights and apply these insights within real-world scenarios.[82]

AI literacy as a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AIas a tool online, at home, and in the workplace. [Long, Magerko, 2020]

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 (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–16. https://doi.org/10.1145/3313831.3376727

AACRA

Other approaches focus on positioning media literacy in relation to “reading,” “writing,” and “relevance.” Renee Hobbs developed the AACRA model (access, analyze, create, reflect and act)[49] and identifies three frames for introducing media literacy to learners: authors and audiences (AA), messages and meanings (MM), and representation and reality (RR), synthesizing the scholarly literature from media literacy, information literacy, visual literacy and new literacies.[50]

Practicing Literacy

SIFT

S’ is for stop and reflect, especially before sharing or acting on the information. ‘I’ is for investigate the source. Looking at the source’s Wikipedia page, for example, can sometimes give a sense of their reliability. ‘F’ is for find better coverage, such as a reputable fact-checking website. ‘T’ is for trace the claim to its original context, whether an image or a quote to help make sure it was not taken out of context or comes from a reliable source.[12]

Lateral Reading

Lateral reading, or getting a brief overview of a topic from lots of sources instead of digging deeply into one, is a popular method professional fact-checkers use to quickly get a better sense of the truth of a particular claim.[48]

Steps:

  1. Exit out of the post or website you are currently on.
  2. Do a keyword search of the website/source to find out more information.
  3. Open up multiple websites that tell you more about the source.
  4. Compare and contrast the information you read about the source to determine if it is reliable or not.

Fact Checking Process

Steps for Evaluating & Fact-Checking AI Output

Questions to ask yourself:

  • Can claims be verified in reliable, credible sources that cover the same topic?
  • Could the content be missing any important information or points of view?  Is there any inherent bias?
  • If the AI tool provides links to sources, have I checked the generated response against those sources?

Here’s further detail on how to fact-check an output from ChatGPT or similar tool.

Break It Down

Break down the information. Take a look at the response and see if you can find specific claims or facts that need to be verified. 

Check the Original Source 

Some AI tools will include links to sources, which sounds great! But AI is not designed to cite sources accurately, so the response could include an article, book, or webpage that does not actually contain the information provided.

Make sure to go to the source and check the information in the source against what claims and facts the Gen AI tool claims to have found in it. 

If the AI tool does not include links to the sources, you can ask it the following questions (and then fact-check the sources it gives you):

  • Where did this information come from?
  • Can you list your sources?
  • Is this a widely accepted fact or still being debated?

Search & Compare

If the AI tool still does not provide links to its sources, open a new browser tab and use other search tools to look for information that supports a specific fact or claim made by the AI tool. If several reliable search tools confirm the same information, it’s likely accurate.

If you are searching for a specific fact or claim, start with Google or a fact-checking site.

Be especially cautious with the following types of outputs, or responses, which are especially prone to AI mistakes. 

  • Statistics
  • Historical facts
  • Medical or legal advice
  • Quotes

If your topic relates to current events, search news databases for relevant stories. 

Try Google News and US Newsstream, a library database of national and regional newspapers.

AI can generate very convincing fake citations. 

Sometimes the entire citation is made up, with a fake author, an article title that doesn’t exist, and a non-existent journal.

Other times, the fake citation lists the name of a real researcher who studies the topic you’re researching, but the article title in the citation might not exist or might not be from the journal title listed.

Search for the article title in Google Scholar or NWTC’s Library Search to see if the article actually exists!

Analyze 

Consider the information you discovered in light of these assumptions:

  • What did your prompt assume? 
  • What did the AI tool assume? 
  • What perspective or agenda do your fact-checked findings have? Who would know things about this topic? Would they have a different perspective than what the AI tool is offering? Where could you check to find out? 

Watch for outdated information!  AI can sometimes give answers based on older data. Check publication dates and/or whether newer research or announcements exist. Be especially careful if the topic has changed recently (science, technology, laws, etc.) Check the currency.

Decide

  • What is true, what is misleading, and what is factually incorrect?
  • Can you re-prompt the AI to try and fix some of these errors?
  • Can you dive deeper into one of the sources you found while fact-checking?

Repeat/Conclude

Repeat this process for each of the claims you identified in the Break It Down stage. 

Currency

Currency, when a document was created, edited, updated, or revised, is an important factor in evaluating any information source. If you need recent information on a world event or a new development in research, generative AI may not have that information in its dataset.

References & Resources

Foundational Concepts (Wikipedia)

Wikipedia concepts for launching your own reading.

Digital Fact-Checking Tools (Wikipedia)

Tools mentioned by articles for verifying genAI outputs.

External Tools and Resources

Articles and sources used during research.

Books I Want to Read

Sources have not been vetted – reading suggestions only.

  • Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble
  • Data Feminisim by Catherine D’Ignazio and Lauren Klein
  • Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell
  • Lurking: How a Person Became a User by Joanne McNeil
  • On Intelligence by Jeff Hawkins
  • Superintelligence: paths dangers strategies by Nick Borstrom
  • Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think by James Vlahos
  • Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Watcher-Boettcher
  • The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale
  • The Book of Why: The New Science of Cause and Effect bu Judea Pearl and Dana Mackenzie
  • The Creativity Code: Art and Innovation in the Age of AI by Marcus Du Sautoy
  • Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil

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