How ChatGPT Influences Academic Publishing

 

We have been discussing algorithms, machine learning, and artificial intelligence. The issue in the news that is taking up a lot of attention is ChatGPT. We have written about it before, but the question of its influence on academic journal publishing is useful to consider. On this episode, Dr. Lilian Hill explains how ChatGPT influences academic publishing. Listen to the episode and join in the online forum.

 

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 Information Literacy Episode 13 Transcript

References

Flanagin, A., Bibbins-Domingo, K., Berkwits, M, & Christiansen, S.L. Nonhuman “Authors” and Implications for the Integrity of Scientific Publication and Medical Knowledge. JAMA. 2023;329(8):637–639. doi:10.1001/jama.2023.1344

 

Getahun, J. (2023, January 16). ChatGPT could be used for good, but like many other AI models, it's rife with racist and discriminatory bias. Insider. Retrieved https://www.insider.com/chatgpt-is-like-many-other-ai-models-rife-with-bias-2023-1

 

Open AI (2023). ChatGPT (April 10 version) [Large language model]. https://chat.openai.com/chat

 

Stokel-Walker, C. (2023). ChatGPT listed as author on research papers: many scientists disapprove. Nature, 613(7945), 620–621. https://doi-org.lynx.lib.usm.edu/10.1038/d41586-023-00107-z

 

Teixeira da Silva, J. A. (2023). Is ChatGPT a valid author? Nurse Education in Practice, 68, 103600. https://doi-org.lynx.lib.usm.edu/10.1016/j.nepr.2023.103600

Educating Citizens about Algorithms: A New Charge for Adult Educators

 

The field of adult education has historic connections to education for citizenship and for building democracy. This kind of education introduced people wishing to achieve citizenship to information that includes literacy in the language of that country, as well as its organization, ways of work and production, and cultural heritage. On this episode, Dr. Lilian Hill explores how to educate citizens about algorithms. Listen to the episode and join in the online forum.

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Information Literacy Episode 8 Transcript

 

References

Carugati, F. (2020, June 12). A Council of Citizens Should Regulate Algorithms. Retrieved https://www.wired.com/story/opinion-a-council-of-citizens-should-regulate-algorithms/

Guo, Elaine (2022, December 19). A Roomba recorded a woman on the toilet. How did screenshots end up on Facebook? MIT Technology Review. Retrieved from https://www.technologyreview.com/2022/12/19/1065306/roomba-irobot-robot-vacuums-artificial-intelligence-training-data-privacy/

IBM (n.d.) AI Ethics. Retrieved https://www.ibm.com/topics/ai-ethics

Moses, L. B. (2023, January 29). Should we worry about AI and algorithms in government? Retrieved https://www.youtube.com/watch?v=y6FDBDGNYFc

Rainie, L., & Anderson, J. (2017, February 8). Code-Dependent: Pros and Cons of the Algorithm Age. Retrieved https://www.pewresearch.org/internet/2017/02/08/code-dependent-pros-and-cons-of-the-algorithm-age/

Robinson, P., Williams, D., & Stojanović, M. (2022). Global citizenship and adult education: Advancing critical literacies for equity and social justice. Routledge.

UNESCO (2022). Recommendation on the Ethics of Artificial Intelligence. UNESCO Digital Library. Retrieved https://unesdoc.unesco.org/ark:/48223/pf0000381137

Efforts to Regulate Algorithms

 

We have discussed the dangers embedded in algorithms in previous episodes. Therefore, there is a need for oversight and regulation. On this episode, Dr. Lilian Hill talks about the efforts to regulate algorithms, provides a historical perspective, and explains what is being done in the U.S. and other countries.

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Information Literacy Episode 7 Transcript

 

References

Ferguson, C., P’ng, J., & Whiteside, H. (2022, October 18). The Regulation of Artificial Intelligence in Canada and Abroad: Comparing the Proposed AIDA and EU AI Act. Fasken. Retrieved https://www.fasken.com/en/knowledge/2022/10/18-the-regulation-of-artificial-intelligence-in-canada-and-abroad

Government of Canada. Algorithmic Impact Assessment tool. Department of Treasury. Retrieved https://www.canada.ca/en/government/system/digital-government/digital-government-innovations/responsible-use-ai/algorithmic-impact-assessment.html

Newton, C. (2020, December 29). Everything you need to know about Section 230: The most important law for online speech, The Verge. Retrieved https://www.theverge.com/21273768/section-230-explained-internet-speech-law-definition-guide-free-moderation 

White House Office of Science and Technology Policy (n.d.). Blueprint for an AI Bill Of rights: Making Automated Systems Work for the American People. Retrieved https://www.whitehouse.gov/ostp/ai-bill-of-rights/#safe

 

 

 

Women and Algorithms: How Gender Bias Affects Our Lives

 

Gender bias pervades many aspects of our daily experiences, and even our thinking, it is no surprise that it also exists in algorithms. Algorithms affects our lives. On this episode, Dr. Lilian Hill talks about how gender bias influences algorithmic outcomes.

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Information Literacy Episode 6 Transcript

 

References

Gartner Says Nearly Half of CIOs are Planning to Deploy Artificial Intelligence. https://www.gartner.com/en/newsroom/press-releases/2018-02-13-gartner-says-nearly-half-of-cios-are-planning-to-deploy-artificial-intelligence

Gender Discrimination in the Algorithmic Field: A Look at The Algorithms in The Oasi Register (2022, March 14). ETICAS Foundation. Retrieved https://Eticasfoundation.Org/Gender-discrimination-in-the-algorithmic-field-a-look-at-the-algorithms-in-the-oasi-register/

Niethammer, C. (2020, March 2), AI Bias could put women’s lives at risk—A challenge for regulators. Forbes. https://www.forbes.com/sites/carmenniethammer/2020/03/02/ai-bias-could-put-womens-lives-at-riska-challenge-for-regulators/?sh=fcd5ac534f27

Perez, C. C. (2019). Invisible women: Data bias in a world designed for men. Abrams Press.

Smith, G., & Rustagi, I. (2021, March 21). When Good Algorithms Go Sexist: Why and How to Advance AI Gender Equity. Stanford Social Innovation Review. Retrieved https://ssir.org/articles/entry/when_good_algorithms_go_sexist_why_and_how_to_advance_ai_gender_equity

Dangers and Recommendations of Algorithms

 

By Lilian H. Hill

A lot of uses of algorithms are beneficial; however, there are dangers involved. Decisions about admissions, scholarship awards, and hiring have been turned over to algorithms. Flaws in programming can cost individuals the opportunity to attend their college of choice because their entrance exams were graded based on a faulty metric. Hiring decisions can be negatively influenced if metrics involved are biased against minorities. People’s privacy can be violated if the algorithms designed to share individual’s information are inaccurate. 

In an interesting news story, Northeastern University installed heat sensor devices undergraduate students’ desks to track usage (Ongweso, 2022). Given that the students were enrolled in Northeaster’s Cybersecurity and Privacy Institute, it should not be surprising that the students detected the presence of the devices, hacked into them, developed an open-source guide so that other students could hack them. They then removed the devices and displayed them in an art exhibit spelling the word NO! The university had installed the devices at night without informing the students and without Institutional Review Board (IRB) permission. The students found that the devices were not as secure as the university claimed. 

Recommendations

The Center for Democracy and Technology recommends the following:

• Human beings need to retain control of decision-making that involves people’s privacy, safety, and opportunities. Context and nuance are difficult to program into algorithms. 
• Regulate data governance: Establish policies that determine long information should be kept and under what certain conditions it should be deleted.
• Conduct regular audits to ensure that discriminatory outcomes or other unexpected harm do not occur.
• Communicate regularly with stakeholders to provide feedback and address concerns about the systems that affect their schools.
• Use algorithms for the purposes they were designed for. Adapting them to other purposes has the potential to yield harmful results.
• Foster accountability by developing plans and policies to identify and correct errors in the programming. Have strategies and resources available to make amends when errors have been harmful to people. 
• Ensure legal compliance so that the decisions made by algorithms are fair, accurate, and comply with legal standards for education. 

References

Center for Democracy and Technology. Algorithmic systems in education: Incorporating equity and fairness when using student data. Retrieved from https://cdt.org/insights/algorithmic-systems-in-education-incorporating-equity-and-fairness-when-using-student-data/ 

Ongweso, E. (2022, December 2). ‘NO’: Grad students analyze, hack, and remove under-desk surveillance devices designed to track them. https://www.vice.com/en/article/m7gwy3/no-grad-students-analyze-hack-and-remove-under-desk-surveillance-devices-designed-to-track-them

 

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

 

 

Pandemic Applications of Algorithms

 

By Lilian H. Hill

Algorithms were essential in maintaining educational activity during the pandemic isolation that caused many students to learn from home through educational technology. We are only just finding out what has been gained and lost in terms of students’ learning, knowledge, and abilities. I heard a recent NPR report about children entering kindergarten who were lacking in knowledge, social skills, and even fine motor coordination because they had spent so much time interacting with devices (Feiereisel, 2022). Adult students may have similarly lost ground in learning, but the results are much harder to track because adults are learning in more diverse settings and are working toward many different goals).

The OECD (2021) estimates that “many forms of learning, in particular informal learning, were inevitably lost, as workplaces remained physically closed,” educational institutions switched to remote learning, and people were isolating themselves to protect their health. Reliance on technology for educational delivery magnified the digital divide.

References

Feiereisel, A. (2022, December 1). Kindergarten teachers observe speech and physical delays in young students. NPR Radio Broadcast. https://www.wbur.org/hereandnow/2022/12/01/kindergarten-student-delays

OECD (2021, March 25). Adult Learning and COVID-19: How much informal and non-formal learning are workers missing? https://www.oecd.org/coronavirus/policy-responses/adult-learning-and-covid-19-how-much-informal-and-non-formal-learning-are-workers-missing-56a96569/#snotes-d4e393

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.

Instructional Applications of Algorithms

 

By Lilian H. Hill

Algorithms are also used to support the Flipped classroom in which students learn content independently so that facilitator can use class time for activities that support meaning making, applying information, and retaining knowledge.

Platforms that allow students to practice basic skills are also benefited by algorithmic responses. Programs used in adult basic and literacy education make use of algorithms to provide personalized content for students, tailored to their level of achievement. Software to support learning can provide pronunciation guides for language learning. It can even engage in simplified conversations. 

Computer algorithms are essential to simulated learning. They are used to replicate complex and dangerous situations while allowing students, and the public, to remain safe. For students to practice appropriate responses for stressful situations, algorithms determine symptoms displayed by robotic simulated patients for health professions students, automate flight practice for pilot trainees, and orchestrate the atmosphere of war to train soldiers. 

References

Collins, R. (2020). Interdisplinarity in adult and continuing education. In Rocco, T. S., Smith, M. C., Mizzi, R. C., Merriweather, L. R., & Hawley, J. D. (Eds.), Handbook of adult and continuing education (pp. 31-37). Stylus. 

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. 

 

Algorithms for Automation and More

 

By Lilian H. Hill

We have been discussing the role of algorithms in computer programming and how they structure what we view online. Today, we are starting a series of postings about the role of algorithms in education, with emphasis on adult learning.

Algorithms are useful in automating many repetitive tasks in the education world. For example, plotting course schedules, scheduling classrooms, allocating resources, organizing campus policing, and running student success and retention initiatives are all tasks that can be made more efficient with automation. Workplace orientation and training programs are delivered using computer algorithms. 

Some colleges and universities are moving to using chatbots to answer standardized questions. When you make a call for information and you receive responses from an automated voice, that is a chatbot in action. Information queries online can also be mediated by algorithms. 

Learning management systems (LMS) such as Canvas and Blackboard make use of algorithms to automate delivery of online courses and programs. They provide for efficient documentation of attendance, participation, submission of assignments, grading tests, and documentation and tracking of student achievement. 

When there are a finite number of responses to questions, for example in multiple-choice questions, surveys, and even personality assessments, algorithms are responsible for the instant responses you receive. You only have to wait for a grade when your written essay is graded by a human being. Some essays are even being graded by using algorithms capable of machine learning. Unfortunately, these assessments may assess students’ abilities to memorize facts, or use specific vocabulary the system is programmed to recognize, and not how well they are able to question and apply information.

References

Dans, E. (2020). Algorithms and education: Not so fast. Retrieved https://www.forbes.com/sites/enriquedoans/2020/09/04/algorithms-and-education-not-sofast/?sh=4835b63c446e.  Retrieved https://aquila.usm.edu/highereddoctoralprojects/2/

Robinson, C. (2020). Impressions of viability: How current enrollment management personnel and former students perceive the implementation of a chatbot focused on student financial communication. https://aquila.usm.edu/highereddoctoralprojects/2/

Rosen, D. (2019, June 4). The role of artificial intelligence in adult basic skills education. LINCS Community for Adult Educators. Retrieved https://community.lincs.ed.gov/group/21/discussion/role-artificial-intelligence-adult-basic-skills-education