Ethical Implications Using the FATE Framework

• In Event: Artificial Intelligence as a Catalyst for Transforming Accreditation in Educator Preparation

Abstract

As artificial intelligence gains traction in educator preparation and accreditation, ethical concerns surrounding its use become increasingly important. This session delves into the ethical dimensions of AI in accreditation through the lens of the FATE framework—Fairness, Accountability, Transparency, and Ethics. Grounded in scholarship from Barocas, Hardt, & Narayanan (2019) and Microsoft Research (n.d.), the presentation offers a structured approach for critically evaluating AI’s impact on equity, trust, and institutional integrity.

The session begins by examining why ethical reflection is necessary when implementing AI in accreditation. Although these tools can enhance efficiency and insight, they also carry risks, such as reinforcing bias, masking decision-making processes, or diminishing human judgment. Using the FATE framework, the presenter will guide participants through key ethical questions:
Fairness: Are AI-driven decisions equitable for all candidates and stakeholders?

Accountability: Who is responsible when AI systems produce errors or skewed results?

Transparency: Can stakeholders understand and trust how AI arrives at conclusions?

Ethics: Do AI applications align with institutional values and accreditation principles?

Participants will analyze case studies illustrating both responsible and problematic uses of AI in accreditation—from automated rubric scoring that disadvantages nontraditional candidates, to ethical AI implementations that flag disparities and inform equity strategies. The presenter will provide guidance on identifying hidden biases in training data, ensuring human oversight, and documenting AI-assisted decisions to promote auditability and trust.

The session will also highlight policy considerations and best practices for implementing AI responsibly. These include forming institutional AI ethics committees, developing usage policies aligned with accreditation standards, and educating stakeholders about AI capabilities and limitations.

To foster dialogue, participants will engage in small-group ethical scenario analysis, applying the FATE framework to realistic accreditation situations. Discussions will prompt institutions to reflect on their own data governance structures, privacy safeguards, and inclusivity efforts when deploying AI tools.

By the end of the session, attendees will:

Understand the FATE framework and its relevance to accreditation.

Identify ethical risks in AI use for educator preparation and quality assurance.

Apply strategies to evaluate and mitigate bias, opacity, and ethical misalignment.

Gain tools to foster accountability and transparency in AI-supported decision-making.

This session is especially valuable for accreditation leads, faculty, data analysts, and institutional leaders who seek to balance innovation with ethical responsibility in the evolving accreditation landscape.

Author

• Malina Monaco, Council for the Accreditation of Educator Preparation