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Ethics by Design – The Need for Critical Attitude in Ethical-Centered Design

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AI Competencies Self-Assesssment Checklist

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Conflict-Based Learning for AI Ethics – Debate and Evaluation

Conflict-Based Learning for AI Ethics

 

Using conflict-based learning helps you practice ethical decision-making in real-world AI scenarios. Instead of focusing only on personal preferences or emotions, you learn to rely on ethical principles, legal standards, and data to reach fair solutions. This approach involves setting ground rules—such as active listening, transparency, and evidence-based reasoning—so that discussions remain constructive and respectful.

Debate Guidelines

 

  1. Focus on Ethical Standards and Data

    • Use facts, legal frameworks, or real-world examples to support your arguments.
    • Avoid purely emotional or personal stances.

  2. Listen Actively

    • Give full attention to the speaker, ask clarifying questions, and try to understand their viewpoint.
    • Summarize key points to ensure everyone’s voice is heard.

  3. Be Transparent

    • Share relevant information openly.
    • If you reference data or specific laws, cite your sources clearly.

  4. Aim for Mutual Understanding

    • Look for common ground, even if you disagree on details.
    • Work toward a compromise or a creative solution that respects everyone’s concerns.

  5. Document and Reflect

    • Take notes on the process, decisions made, and lessons learned.
    • Reflect on how ethical principles guided your final conclusions.

Evaluation Criteria

Below is a sample rubric for assessing your performance during conflict-based learning activities. It covers how well you apply ethics, use data, and engage with others. Each criterion is rated from 1 (lowest) to 5 (highest).

Criterion Excellent (5) Good (4) Satisfactory (3) Needs Improvement (2) Unsatisfactory (1)
Application of Ethical Principles Demonstrates a deep understanding of AI ethics and applies them effectively in discussions. Applies ethical principles correctly, with minor gaps. Shows basic understanding but lacks depth. Struggles to apply principles effectively. No evidence of understanding ethics.
Critical Thinking & Problem-Solving Analyzes issues from multiple viewpoints, identifies ethical violations, and proposes creative solutions. Identifies key concerns and suggests reasonable solutions. Recognizes some issues but provides limited analysis. Struggles to analyze or propose solutions. Fails to engage in ethical reasoning.
Negotiation & Communication Skills Clearly articulates arguments, listens actively, and collaborates in constructive negotiation. Communicates ideas well, though some points need clarity. Expresses ideas but lacks persuasive argumentation. Has difficulty responding to counterarguments. Does not engage effectively in discussion.
Team Collaboration & Leadership Actively contributes, respects others’ input, and shows leadership in group tasks. Works well with the team; contributes meaningfully. Participates but shows minimal initiative. Contributes little or struggles to collaborate. Does not participate in the team process.
Use of Data & Evidence-Based Reasoning Uses relevant data, legal frameworks, and examples to convincingly support arguments. Supports ideas with data but may lack depth. Uses some evidence but with generalizations. Relies on weak sources or unverified claims. Provides no data or evidence.
Engagement & Participation Actively participates, asks insightful questions, and challenges assumptions constructively. Engages in discussion but seldom challenges perspectives. Participates occasionally but not deeply. Shows limited engagement or passive involvement. Does not participate meaningfully.

Total Score: ____ / 30

 

Assessment Notes:

  • 25–30 points: Outstanding application of conflict-based learning principles.
  • 20–24 points: Strong ethical understanding, but with minor gaps.
  • 15–19 points: Basic grasp of ideas; needs more depth.
  • 10–14 points: Significant improvement needed in ethical reasoning and discussion.
  • Below 10 points: Minimal effort or engagement.

Example: How a School Can Adopt AI Responsibility with Conflict-Based Learning

Imagine your school plans to use an AI-driven recommendation system to guide students toward electives, clubs, or after-school programs. Some students claim the AI’s suggestions feel biased—certain clubs get more visibility, or advanced electives are often recommended to the same types of students.

 

  1. Set up a Role-Play Scenario

    • School administrator: Emphasizes scheduling efficiency and cost savings.
    • Student representative: Argues that the AI overlooks personal interests or unique talents.
    • Tech teacher: Explains how data was collected and how it might be biased.
    • Legal or policy advisor: Points out privacy and fairness requirements.
    • Diversity and inclusion advocate: Urges caution about reinforcing stereotypes.

  2. Apply the Debate Guidelines

    • Everyone uses data to back up their points: such as how many clubs are recommended to each grade level, or how personal data is collected.
    • Participants actively listen and look for shared concerns—like fairness for all students.

  3. Discuss Solutions

    • Modify the algorithm to include more diverse data.
    • Give students partial override so they can customize recommendations.
    • Ensure transparency: Publish summary reports on how the AI makes its choices.

  4. Use the Evaluation Criteria

    • Check how each student role used ethical principles, critical thinking, and negotiation.
    • Grade group collaboration and evidence-based reasoning.
    • Reflect on how well the final solution addresses the fairness issue.

  5. Outcome

    • Your class develops a set of rules or improvements, e.g., biannual audits of the AI for bias and student feedback loops to refine recommendations.
    • Everyone learns how conflict-based discussions can lead to a balanced approach, respecting both practicality and ethics.

Key Takeaways

  • Conflict-based learning lets you practice handling AI-related disagreements by applying fairness, legal guidelines, and data.
  • Debate guidelines like active listening, transparency, and data-based reasoning help keep discussions productive and respectful.
  • Evaluation rubrics provide a clear framework to assess your understanding of ethics, communication, teamwork, and evidence use.
  • Real-world relevance: These skills mirror the dilemmas that schools, companies, and communities face when adopting AI, making your learning experience more meaningful.
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