Getting Started with Generative AI: Generative AI Basics

Bias and Inequity - AI systems are at risk of reflecting and exacerbating human biases since they are trained on human-generated data, which can marginalize underrepresented groups.

Intellectual Property - AI models are often trained on copyrighted data, and many authors have objected to this. The long-run impact is not yet clear, but many publishers are already moving to restrict the use of scholarly information sources by AI tools.

Ethics - The misuse of AI to create and spread disinformation is a major concern.

Privacy and Data Security - There are legitimate concerns regarding how user data is collected, stored and secured by AI platforms, especially in sensitive domains.

Reliability, Transparency and Verification - It can be difficult to verify the accuracy and source of information provided by AI as models may generate non-truthful or unreliable responses without transparency around sources.

Additional Information

Artificial Intelligence: Examples of ethical dilemmas (UNESCO, 2023)

Creativity augmentation - Used responsibly and with appropriate oversight, generative AI may help spark new ideas and perspectives by exploring possibilities outside normal human conceptual boundaries.

Increased productivity - Generative AI has the potential to accelerate research by automating tedious tasks, suggesting insights, identifying patterns and knowledge gaps, summarizing findings, and more. This could free up researcher time.

New discoveries - With its ability to retrieve and analyze massive quantities of data, generative AI may help connect interdisciplinary insights and surface new hypotheses or areas for further investigation that might otherwise be missed.

Reproducibility and sharing - AI-generated artifacts, analyses and reports could more easily be reproduced, shared and built upon by others, accelerating scientific progress.

ChatGPT and Artificial Intelligence in Higher Education Quick Start Guide (UNESCO, 2023, p.6)