LibGuides: AI Taskforce: Ethics of AI

Ethics

AI in academic writing: Assessing current usage and future implications
Concerns arise over authorship preservation and maintaining academic work's unique character, emphasizing the need for clear guidelines. Ethical considerations and best practices are also highlighted to use AI while safeguarding academic integrity effectively.
Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora's box
The article proposes a suggestive revamping of course learning objectives due to students' inclinations to misuse AI to do their coursework with students willing to use AI to complete their coursework should university punitive measures be removed; furthermore, the article equally proposes future research investigating the impact and use of AI in the higher educational classroom on student performance
Burn It Down: A License for AI Resistance
Writing faculty have both the agency and the academic freedom to examine generative AI’s dishonest training origins and conclude: There is no path to ethically teach AI skills. Not only are we allowed to say no, we ought to think deeply about the why of that no.
The ethical dilemmas of AI
Ethical issues related to artificial intelligence are a complex and evolving field of concern. As AI technology continues to advance, it raises various ethical dilemmas and challenges.
The Ethics of Emotional Artificial Intelligence: A Mixed Method Analysis.
The qualitative study of students' essays shows a rather optimistic view of the use of emotional AI, which helps underscore the need to increase awareness about the ethical pitfalls of AI technologies in the complex field of human emotions.
Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review
There is, as such, a need to understand how notions of Fairness, Accountability, Transparency, and Ethics or FATE are identified in the AI and higher education studies to date
A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour.
This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research. Research gaps identified suggest a need for greater ethical considerations within future research
Navigating the Ethical Challenges of Artificial Intelligence in Higher Education: An Analysis of Seven Global AI Ethics Policies
This paper examined three AI objectives related to higher education: biased algorithms, AI and decision-making, and human displacement. In conclusion, AI must be considered ethically in higher education, and stakeholders must ensure that AI is used responsibly, fairly, and in a way that maximizes its benefits while minimizing its risks.
Teaching AI Ethics
Artificial Intelligence presents many complex ethical concerns which are well worth discussing with our students. The Teaching AI Ethics series covers nine areas the author identifies as particularly important to education.Each post includes a detailed discussion of the ethical concern along with links to other articles and resources. There are case studies, discussion questions for a variety of disciplines, and lesson ideas which can be used across the curriculum.
What do we mean by equitable and accountable AI?
Through a combination of art, research, policy guidance and media advocacy, the Algorithmic Justice League is leading a cultural movement towards equitable and accountable AI. This requires us to look at how AI systems are developed and to actively prevent the harmful use of AI systems. We aim to empower communities and galvanize decision makers to take action that mitigates the harms and biases of AI.

Ethics of Artificial Intelligence Case Studies and Options for Addressing Ethical Challenges by Stahl, Bernd Carsten; Schroeder, Doris; Rodrigues, Rowena
Call Number: e-book
ISBN: 9783031170409
Publication Date: 2023
This open access collection of AI ethics case studies is the first book to present real-life case studies combined with commentaries and strategies for overcoming ethical challenges. Case studies are one of the best ways to learn about ethical dilemmas and to achieve insights into various complexities and stakeholder perspectives. Given the omnipresence of AI ethics in academic, policy and media debates, the book will be suitable for a wide range of audiences, from scholars of different disciplines (e.g. AI science, ethics, politics, philosophy, economics) to policy-makers, teachers and the educated public.

I'm a Student. You Have No Idea How Much We're Using ChatGPT.
The article presents the discussion on concept of academic integrity and the use of generative AI in written work created a significant disconnect. Topics include availability of AI tools for writing assistance presenting an opportunity for enhancing their work and improving efficiency; and investing in AI-powered plagiarism detection tools and training faculty members for recognizing signs of AI assistance.
Rethink Your Resistance to ChatGPT: AI tools can meet faculty concerns about ethics and equity.
If we aim to prepare today's students to tackle tomorrow's problems, we would do well to teach them how to think about and use AI tools to enhance their work. We should bring AI into our syllabi, class activities, and assignments. Presented are five ideas to get you started.
ChatGPT: ethical concerns and challenges in academics and research.
The main objective of the present study was to highlight the use of AI and AI assisted technologies such as the ChatGPT and other chatbots in the scientific writing and in the research domain resulting in bias, spread of inaccurate information and plagiarism
How A.I. Chatbots Become Political
We increasingly rely on artificial intelligence chatbots as tools to understand the world. Keeping an eye on chatbots’ emergent behaviors — including their political attitudes — is becoming more and more important.

Stakeholder Perspectives on the Ethics of AI in Distance-Based Higher Education
The study presented in this paper sought to investigate the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education

Academic Integrity

Academic Integrity Considerations of AI Large Language Models in the Post-Pandemic Era: ChatGPT and Beyond
This paper explores the academic integrity considerations of students' use of Artificial Intelligence (AI) tools using Large Language Models (LLMs)
Universities yield to AI tools but face plagiarism challenges; Academics revamp teaching styles while ensuring students keep pace with rapidly changing world
The speed of AI development has led to most of the city’s universities adopting the generative tools in the new school year. Academics said a major problem was the detection of plagiarism and assessing originality.
The Risks of Trusting AI.
This article discusses the risks associated with trusting artificial intelligence (AI) in scientific research. While AI models are increasingly being used in various scientific fields, there are concerns about their limitations and potential biases. The authors argue that humans tend to attribute too much authority and trustworthiness to AI systems. They highlight the risks of relying on AI tools, such as the potential for cognitive illusions and the loss of diversity in human perspectives. The authors suggest strategies for mitigating these risks.
ACADEMIC INTEGRITY IN THE AGE OF AI
While many “AI detection tools” have sprung up, they have not been shown to be consistently reliable. Instead, many schools are now focusing their energies on how to design assignments and teach students how to use AI in ways that will still allow them to build writing, research, and critical-thinking skills. Included here are some tips on how to encourage a culture of academic integrity in the age of AI.

AI-Detectors Biased Against Non-Native English Writers
Don’t put faith in detectors that are “unreliable and easily gamed,” says scholar.
AI Detectors Don’t Work. Here’s What to Do Instead.
As AI tools like ChatGPT gain popularity on campus, instructors face new questions around academic integrity and AI. In response to these questions, some companies have developed “AI detection” software. However, early experiences show that AI detection software is far from foolproof. In this guide, we’ll go beyond AI detection software. We’ll discuss how clear guidelines, open dialogue with students, creative assignment design, and other strategies can promote academic honesty and critical thinking in an AI-enabled world.
AI Detectors Falsely Accuse Students of Cheating—With Big Consequences
About two-thirds of teachers report regularly using tools for detecting AI-generated content. At that scale, even tiny error rates can add up quickly.
Do teachers spot AI? Evaluating the detectability of AI-generated texts among student essays
Our findings demonstrate that with relatively little prompting, current AI can generate texts that are not detectable for teachers, which poses a challenge to schools and universities in grading student essays. Our study provides empirical evidence for the current debate regarding exam strategies in schools and universities in light of the latest technological developments.
GPT-4 is judged more human than humans in displaced and inverted Turing tests
We measured how well people and large language models can discriminate using two modified versions of the Turing test: inverted and displaced. GPT-3.5, GPT-4, and displaced human adjudicators judged whether an agent was human or AI on the basis of a Turing test transcript. We found that both AI and displaced human judges were less accurate than interactive interrogators, with below chance accuracy overall. Moreover, all three judged the best-performing GPT-4 witness to be human more often than human witnesses. This suggests that both humans and current LLMs struggle to distinguish between the two when they are not actively interrogating the person, underscoring an urgent need for more accurate tools to detect AI in conversations.
OpenAI confirms that AI writing detectors don’t work
OpenAI has admitted that AI writing detectors are ineffective, often producing false positives. Their AI Classifier, which had a low accuracy rate of 26%, was discontinued. OpenAI's FAQ confirms that no current tools reliably distinguish AI-generated from human-generated content, and ChatGPT itself cannot identify AI-written text
Why We Should Normalize Open Disclosure of AI Use
It’s time we reclaim faculty-student trust through clear advocacy — not opaque surveillance.

It is important to note that many institutions and education experts are now cautioning against relying solely on AI detection tools due to several concerns:

Accuracy issues, including false positives and false negatives
Potential bias against non-native English speakers
The ability for students to bypass detection using various techniques
Ethical concerns about falsely accusing students of misconduct

Instead, many educators and institutions are shifting focus towards:

Clear communication about AI usage policies in syllabi
Redesigning assignments to be more resistant to AI-generated content
Fostering discussions about academic integrity with students
Using AI detection results as a starting point for conversations rather than as definitive proof

Overall, while AI detection tools are available, their use in higher education is becoming more nuanced and cautious as awareness of their limitations grows.

Generative AI tools and assessment: Guidelines of the world's top-ranking universities
The public release of generative artificial intelligence (GAI) tools has had a disruptive effect on the assessment practices of higher education institutions (HEIs) worldwide. Concerns have largely been associated with academic integrity, cheating and plagiarism.
Using Generative Artificial Intelligence Tools to Explain and Enhance Experiential Learning for Authentic Assessment
This article investigates how GenAI can enhance teaching and learning by constructively addressing study situations beyond conventional learning approaches and cultivating high-order skills and knowledge acquisition.

LLM Hallucination Index
Our new Index evaluates how well 22 of the leading models adhere to given context. We conducted rigorous testing of top LLMs with input ranging from 1,000 to 100,000 tokens to answer the question of how well they perform across short, medium, and long context lengths.

ChatGPT conundrums: Probing plagiarism and parroting problems in higher education practices.
The article explore the challenges and potential issues posed by the use of large language models like ChatGPT in higher education. It discusses the concerns related to plagiarism, academic integrity, and the reliability of scientific research in the context of ChatGPT.
Maintaining the integrity of the South African university: The impact of ChatGPT on plagiarism and scholarly writing
The impact, as well as the perceived impact, of AI on teaching and learning in higher education has been consistently documented in popular media. Therefore, the aim of this article is to understand the impact of ChatGPT on plagiarism and scholarly writing.
ChatGPT Is a Plagiarism Machine
The article discusses the silence of college administrators regarding the widespread use of Artificial Intelligence (AI) chatbot technology for plagiarism among students. Topics include the lack of institutional response to AI plagiarism, challenges faced by faculty, the absence of institutional guidelines, and recommendations for addressing the issue effectively.