How to Get Started
- Review Policies: Familiarize yourself with institutional policies and legal regulations surrounding AI use and data privacy.
- Adopt Ethical Tools: Incorporate AI tools designed for fairness, accessibility, and inclusivity into your teaching and research practices.
- Educate and Collaborate: Share resources with students and colleagues to foster a culture of ethical AI use.
FERPA
FERPA (Family Educational Rights and Privacy Act) Overview
FERPA is a federal law safeguarding the privacy of student education records in all schools receiving federal funding from the U.S. Department of Education.
For more information on FERPA go to these links: General FERPA Information and Family Educational Rights and Privacy Act (FERPA) Policy.
Key Aspects of FERPA Compliance In Relation to AI:
- AI and FERPA: AI models trained on student data must be carefully managed to avoid disclosing PII and ensure compliance. Transparency in AI systems is critical, with regular monitoring of AI outputs necessary to prevent inadvertent disclosure.
- AI platforms store prompt information therefore it's essential to be careful what information is entered.
- Any information related to students must be completely anonymized before being entered into an AI platform such as ChatGPT. Even information that has been anonymized must not be entered in such a way that it could be traced back to students.
- If unsure, do not enter the student data into an AI platform.
Examples of Good and Bad FERPA Prompts
| Prompt Type | Prompt | Explanation |
|---|---|---|
| Good Example | "We're developing a student performance tracking system for our university, ensuring compliance with FERPA regulations is our top priority. Can you assist in creating algorithms that analyze academic data while maintaining student privacy and confidentiality?" | Demonstrates awareness of FERPA and emphasizes the importance of protecting student privacy while analyzing academic data. |
| Bad Example | "I want to analyze student grades and attendance data to identify patterns using AI. Can you help me access this data and create predictive models?" | Raises concerns as it suggests accessing student academic data without considering FERPA regulations. Fails to address authorization and security measures. |
Ethics in Academia, FERPA, and Inclusivity
The integration of Artificial Intelligence (AI) in academia brings opportunities to advance teaching, research, and institutional effectiveness. However, it also raises critical ethical considerations. Faculty must address issues like data privacy, algorithmic bias, and the equitable use of AI tools to ensure inclusivity and fairness. This page provides a comprehensive overview of ethical concerns in academia and resources for fostering inclusivity through AI.
1. Ethical AI Use in Teaching and Research
Responsible AI use requires transparency, accountability, and adherence to ethical guidelines:
- Avoiding Bias in AI Models:
- Algorithms can perpetuate biases if trained on skewed datasets. Tools like IBM AI Fairness 360 and Google’s What-If Tool can help assess and mitigate bias.
- Transparency
- Faculty should strive for transparency in how AI tools make decisions. LIME (Local Interpretable Model-agnostic Explanations) is a tool that clarifies AI decision-making processes.
- Plagiarism and Integrity:
- Faculty should set clear standards for students in their syllabi as to what is allowed or not allowed in terms of AI usage and what the consequences are for students who violate the terms on the syllabus. Faculty may use tools on EduCat, such as Turnitin to assess plagiarisim and academic integrity.
2. Data Privacy and Security
AI applications often involve data collection and processing, requiring adherence to privacy laws and ethical guidelines:
- Compliance with Regulations:
- Familiarize yourself with legal frameworks such as GDPR (General Data Protection Regulation) for international research and FERPA (Family Educational Rights and Privacy Act) for student data in the U.S.
3. Promoting Ethical Awareness Among Students
Educators play a key role in fostering ethical AI practices among students:
- Courses and Workshops:
- Discussion Topics:
- Incorporate debates on AI ethics into classroom discussions. Topics can include algorithmic bias, AI in hiring practices, or privacy concerns.
4. Resources for Ethical AI and Inclusivity
Explore the following organizations and tools for guidance:
- Ethics Resources:
- AI Ethics Lab provides workshops, tools, and publications on ethical AI use.
- Partnership on AI promotes best practices and research on responsible AI.
- Inclusivity Initiatives:
- CAST’s Universal Design for Learning Guidelines offer strategies for creating inclusive learning environments.
- W3C Web Accessibility Initiative provides resources to make digital content accessible to all learners.