Ask ethics, bias, and equity questions before adopting an AI system¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that educators interrogate AI tools before use by asking "Who developed this tool?" and "What data was used to train it?" and how that data and the algorithm are biased. It also directs attention to bias in evaluating learning materials and students, maintaining privacy of data shared with an AI system, and equity in terms of ability to access AI tools.

Design Implications¶

Context¶

Requirements¶

• Educators must have access to information about tool developers, training data, and algorithmic bias

Constraints¶

Target Learners¶

• educators
• school and district leaders

Target Learning Goals¶

• ethical and equitable evaluation and adoption of AI tools

Related Strategies¶

• Adopt a systems approach to AI integration rather than spotlights or gaps
• Design AI literacy tools with transparency in development and establish district policies for vetting AI tools
• AI in Education Toolkit for Racial Equity
• Mitigating Racial Bias in Edtech Products
• Use this Toolkit: Mitigating Racial Bias in Edtech
• Convene cross-functional generative AI task forces to guide ethical adoption decisions and procurement
• Audit AI vocal-assessment systems for bias, transparency, privacy, and recording-context robustness before evaluative use

Examples¶

-

Key Sources¶

• Judi Fusco, Pati Ruiz, Jeremy Roschelle. (2023). Ethics, Artificial Intelligence, and Digital Equity: Making Informed Decisions about the Integration of AI. https://digitalpromise.org/digital-equity/