Algorithms and Culture: A Complex Concept

 

As algorithms permeate many aspects of everyday life. Algorithms influence culture. On this episode, Dr. Lilian Hill discusses the relationship of algorithms and culture. Listen to the episode and join in the discussion.

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Information Literacy Episode 5 Transcript

 

References

Carah, N. (2017, August 16). Algorithmic culture and machine learning. http://nicholascarah.com/log/2017/8/15/algorithmic-culture-and-machine-learning

NBC. (2023 January 1). Meet the Press - January 1, 2023. https://www.nbcnews.com/meet-the-press/meet-press-january-1-2023-n1302274  

Seaver, N. (2017, November 9). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. https://journals.sagepub.com/doi/full/10.1177/2053951717738104

Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(4-5). https://journals.sagepub.com/doi/full/10.1177/1367549415577392

 

 

Why Adult Educators Should be Concerned About Algorithms

 

 

Algorithms are highly efficient at automating laborious functions and have permeated into many aspects of our everyday life, but their characteristics can have devastating consequences. On this episode, Dr. Lilian Hill discusses the characteristics of algorithms and why adult educators should be concerned about them.

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Information Literacy Episode 4 Transcript

 

References

Bennett, E. E., & McWhorter, R. R. (2020). Digital technologies for teaching and learning. In T. S. Rocco, M. C. Smith, R. C. Mizzi, L. R. Merriweather, & J. D. Hawley (Eds,), The Handbook of Adult and Continuing Education (pp. 177-186). Stylus.

Dickson, B. (2020, June 10). What makes AI algorithms dangerous? Retrieved https://bdtechtalks.com/2020/06/10/ai-weapons-of-math-destruction/

O’Neill, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.

Algorithms and the Social Implications of Facial Recognition

 

 

Algorithms can influence decision-making about college admissions, healthcare, criminal justice, legal, housing, and much more. It is important to recognize that there are problems in the way algorithms can incorporate existing human biases and magnify them through Artificial Intelligence and machine learning. On this episode of Information Literacy, moderated by Lilian Hill, we’re going to explore algorithms and the social implications of facial recognition through use of Artificial Intelligence and machine learning. Join in the online forum.

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Information Literacy Episode 3 Transcript

References

Algorithm Justice League. Retrieved https://www.ajl.org/about

Data, Algorithms, and Social Justice. Retrieved https://www.socialdifference.columbia.edu/projects-/data-algorithms-and-social-justice

Harwell, D. (2019, December 19). AI's social justice problem: It's amplifying human bias. Retrieved https://www.washingtonpost.com/technology/2019/12/19/federal-study-confirms-racial-bias-many-facial-recognition-systems-casts-doubt-their-expanding-

Hiner, J. (2021, April 15). AI's social justice problem: It's amplifying human bias. Retrieved https://www.cnet.com/culture/ais-social-justice-problem-its-amplifying-human-bias/

O’Neill, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group. 

 

Diving Deeper Into Algorithms

 

Algorithms have more influence than we know. Algorithms are embedded in the software program we use every day and may keep track of our personal information. This episode explores algorithms more deeply – why they are difficult to understand, how should we view them, and recognize their goals and objectives. This episode is a follow-up to the first episode on Information Literacy category as part of the Adult Learning Exchange Virtual Community. Dr. Lilian H. Hill is the moderator of the online forum. Listen to the episode and join in the discussion.

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Information Literacy Episode 2 Transcript

 

References

Mastantuono, M. (2021) The mathematics of misinformation. https://www.bentley.edu/news/mathematics-misinformation

Pflueger-Peters, N. (2022, May 20). An algorithm to detect fake news. https://engineering.ucdavis.edu/news/algorithm-detect-fake-news

Reading Beyond the Headlines

 

It has been reported that people only read the headlines of news reports they encounter. While this can be a time-saving strategy, headlines do not provide enough information to understand the information provided. This episode focuses on reading beyond the headlines and exploring the reasons why people should read more of the news story. This is the first episode on Information Literacy category as part of the Adult Learning Exchange Virtual Community. Dr. Lilian H. Hill is the moderator of the online forum. Listen to the episode and join in the online forum.

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Information Literacy Episode 1 Transcript

References

Etzioni, R. (2020, February 13). Spinning science: Overhyped headlines, snarled statistics lead readers astray. Fred Hutch Cancer Center. Retrieved https://www.fredhutch.org/en/news/center-news/2020/02/spinning-science-overhyped-headlines-snarled-statistics-lead-readers-astray.html

Marquet, N. (2016, September 17). How to write a catchy headline in 1 Minute and 7 Seconds. Retrieved https://www.grammarly.com/blog/how-to-write-a-catchy-headline-in-1-minute-and-7-seconds/?gclid=Cj0KCQiApb2bBhDYARIsAChHC9vvWagplkp3jsuWN9LRfhSS0KBpEFBnbSeq2htwuyLBFlNhD3ovKjcaApnlEALw_wcB&gclsrc=aw.ds

Maybin, S. (2017). Busting the attention span myth. BBC World Service, More or Less. Retrieved https://www.bbc.com/news/health-38896790