A note on AI ethics with reference to the academia and education programmes in MBA and Housing Studies

A note on AI ethics with reference to the academia and education programs in MBA and Housing Studies (information from perplexity.ai)

My question 1: "Highlight 6 main ideas of AI ethics in the context of academic works in social science and related education activities, notably on MBA and Housing Studies programs"

The reply: "Responsible AI use in MBA and Housing Studies calls for adapting general AI ethics to the specific norms of social-science research and higher education. Six core ideas stand out: transparency, academic integrity, human-centred use, fairness and bias, privacy and data protection, and community-governed practice.

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1. Transparency about AI use

Students and staff should clearly disclose when, how, and which AI tools are used in literature reviews, data analysis, drafting, feedback, and teaching materials. This includes stating AI’s role in assignments, theses, supervision, and course design, and communicating limitations and potential biases of the tools.

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2. Academic integrity and authorship

Turning in unedited AI-generated work as one’s own is considered academic misconduct in higher education. Ethical use means treating AI as an assistive tool while maintaining original analysis, proper citation/attribution of AI outputs where required, and alignment with institutional plagiarism and professionalism policies.

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3. Human‑centred judgment and critical thinking

AI should augment, not replace, human judgment, particularly in interpretive and normative aspects of social-science research and professional education. MBA and Housing Studies programmes should emphasise critical evaluation of AI outputs, ensuring key decisions about research design, interpretation, and student assessment remain under human academic responsibility.

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4. Fairness, bias, and justice

AI systems can reproduce and amplify social biases, so ethical use in social science requires active attention to fairness, justice, and the distribution of benefits and burdens. When AI supports decisions about housing, communities, or managerial practice, educators and researchers should interrogate training data, assumptions, and outputs for discriminatory patterns affecting vulnerable or marginalised groups.

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5. Privacy, consent, and data protection

Using AI with student work, interview transcripts, survey data, or community information raises strong obligations to protect privacy and respect autonomy. Ethical practice involves secure handling of data fed into AI tools, compliance with data protection and copyright rules, and informed consent where human subjects’ data are processed or uploaded.

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6. Community‑based governance and ongoing reflexivity

Checklists are insufficient; AI ethics in social-science research benefits from department- or programme-level deliberation and shared norms. MBA and Housing Studies communities should co-create guidelines, regularly review AI’s educational and societal impacts, and treat AI ethics as a continuing, reflexive practice tied to professional and human-rights standards."

My question 2: "Suggest 4 main contemporary challenges to the academia arising from unethical usage of AI."

The reply: "Unethical use of AI is putting pressure on core academic values, especially around integrity, fairness, and trust. Four contemporary challenges stand out: academic misconduct at scale, erosion of trust in authorship and peer review, data/algorithmic harms to students, and inequities between AI “haves” and “have-nots”.

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1. Scaled plagiarism and contract cheating

• Generative AI enables students and academics to produce seemingly sophisticated essays, theses, and even articles with minimal effort, blurring the line between assistance and outsourcing work.
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• Institutions now treat undisclosed AI-written assignments as plagiarism or contract cheating, undermining fair assessment and devaluing genuine learning and scholarly effort.
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2. Erosion of trust in authorship and publishing

• Studies highlight unfair or “ghost” authorship where AI-generated content is passed off as original scholarship, including fabricated data and imaginary citations.
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• Journals and universities are concerned that undisclosed AI use degrades peer review, inflates publication records, and threatens the credibility of the academic record over time.
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3. Privacy, surveillance, and biased student profiling

• AI systems in higher education increasingly mine student data for predictive analytics, raising significant concerns over privacy, consent, and regulatory compliance.
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• Biased or opaque models can mislabel certain groups as “high‑risk”, divert opportunities, and deepen inequities, contradicting academia’s mission to support equitable student success.
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4. Deepening inequality and dependency on opaque tools

• Unequal access to powerful AI tools and AI literacy can give some students and researchers systematic advantages, widening gaps within and between institutions.
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• Over‑reliance on opaque AI systems for writing, analysis, and decision support risks deskilling learners and staff, while concentrating power in tool providers rather than academic communities."

A collection of blog notes on using chatgpt for research purpose.