Algorithm, Data & Donuts
Mitigate & Monitor

5.1 Quick-Win Checklist

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IN THIS LESSON

Learning Focus: Moving from identifying bias to taking practical steps to mitigate it.

Essential Question: How can we, as educators, advocate for and choose AI tools that are more fair and equitable for our students?

Core Concepts Explained: Bias Mitigation Strategies

Draft a Bias-Mitigation Checklist

This checklist is designed to be a practical tool to assess a new AI tool's potential for bias and ensure it is fair and effective for all students.

Purpose

  • What specific educational problem does this tool claim to solve?

    • Is this a genuine need in my classroom?

  • Is AI the most effective and appropriate solution for this problem?

    • Could a simpler, non-AI tool or a different teaching strategy achieve the same goal?

  • What are the advertised benefits for student learning and for me as a teacher?

    • Are these benefits realistic and aligned with my pedagogical goals?

Fairness & Equity

  • How does the tool perform for different student groups?

    • Can I test the tool with hypothetical student profiles that represent the diversity in my classroom?

  • Does the tool risk reinforcing existing stereotypes or creating new ones?

    • For example, in a writing evaluation tool, does it favor certain dialects or writing styles?

  • Does the tool offer accessibility features for students with disabilities?

    • Is it compatible with screen readers and other assistive technologies?

Accountability

  • What is the process if the AI makes a mistake or produces a biased output?

    • Is there a clear and simple way for me or my students to report errors or problematic content?

  • Is there a way to override, appeal, or correct the AI's decisions?

    • Can I manually adjust a grade or modify a recommendation the AI has made?

  • Who is responsible for the consequences of the AI's mistakes?

    • Does the developer provide clear terms of service regarding their accountability?

Data & Training

  • Can I easily find information about the data used to train this AI?

    • Does the developer provide a datasheet or information on the data sources?

  • Does the training data reflect the diversity of my students?

    • Consider: linguistic background, cultural experiences, socioeconomic status, and learning abilities.

  • Is there a risk that the data is outdated or from a narrow context?

    • How might this impact the tool's relevance and fairness for my students?

Transparency

  • Does the developer explain, in understandable terms, how the AI works?

    • Is there a clear explanation of its capabilities and limitations?

  • Can I understand why the AI made a specific recommendation or decision?

    • For example, if it flags a student's answer as incorrect, is the reasoning clear?

  • Are the tool's outputs presented as suggestions or as definitive facts?

    • Does it encourage critical thinking from both the teacher and the student?