Providing Conflict-Based Learning with AI

When AI is used in everyday life—like in hiring, security, or social media—ethical dilemmas often arise. By stepping into role-playing and following structured conflict resolution, you can build empathy, sharpen communication, and practice making responsible decisions about AI.

Why role-playing matters

• Real-world feel: You act out different perspectives (like CEO, AI engineer, or legal advisor), which makes complex issues clearer and more relatable.
• Hands-on learning: By discussing and debating problems, you learn to negotiate, collaborate, and solve dilemmas about AI’s risks and benefits.
• Ethical focus: Hearing multiple viewpoints highlights how AI decisions can affect fairness, privacy, or corporate goals.

Conflict scenario: AI hiring system

Imagine a tech company using an AI tool to sift through job applications. An internal audit shows the tool unfairly rejects applicants of a certain gender or racial group. Now, the company must figure out how to fix the problem without hurting its reputation or ignoring the rights of applicants.

Roles

• Human resources manager: You’re worried about hiring fairness and the company’s image.
• AI engineer: You built the AI system and need to explain why the bias happened—and how to fix it.
• Legal advisor: You focus on the legal risks if the company fails to address bias.
• Diversity and inclusion officer: You push for changes so that hiring is as fair as possible.
• CEO: You must balance ethical concerns with business needs.
• Journalist: You investigate the story and hold everyone accountable to the public.

Key discussion points

1. Immediate response: How should the company handle the bias now that it’s discovered?
2. Public statement: Should they announce it openly, or handle it quietly?
3. Technical fix: How can the AI system be retrained or redesigned to eliminate bias?
4. Long-term changes: What policies or oversight can prevent this from happening again?
5. Communication: How do you inform employees, the public, or stakeholders about what went wrong?

Role-play instructions:

• Set the scene: The human resources manager opens a meeting and shares the audit results.
• Explain the cause: The AI engineer outlines possible biases in the data or algorithm.
• Legal insights: The legal advisor warns about possible lawsuits or fines if they ignore it.
• Fairness perspective: The diversity and inclusion officer calls for real reforms.
• Decision: The CEO chooses a course of action after hearing everyone’s input.
• Questioning: The journalist probes each decision, asking what it means for the public and for accountability.

Structured conflict resolution

When confronting an AI-related problem, break it down into clear steps:

1. Identify the conflict: Name the issue—like an AI system showing bias in hiring.
2. Analyze causes: Figure out why it’s happening (biased training data, lack of oversight, unclear regulations).
3. Propose fixes: Brainstorm possible solutions, from technical changes to organizational policies.
4. Negotiate interests: Balance the goals of each role—like protecting employee rights, following laws, or sustaining profits.
5. Decide and implement: Choose the best solution, plan how to carry it out, and track results.

Example: Adopting AI responsibility at your school

Imagine your school introducing an AI tool for scheduling classes or recommending extracurricular programs. Some students notice the AI might be unfair—maybe it favors certain grade levels, or it overlooks kids with special needs.

1. Set up a role-play:

   • Administrator: Concerned about using AI to save time.
   • Student representative: Points out how the AI might miss personal preferences or unique needs.
   • Parent: Worries about data privacy and transparency.
   • Tech coordinator: Focuses on the tech side, explaining how the AI works and potential biases.
   • Journalist: Tries to see whether the school is transparent and fair.

2. Conflict resolution:

   • Analyze the system’s data. Where did it come from? Could it contain bias?
   • Suggest improvements, like new data or letting students override AI decisions.
   • Discuss how to handle privacy—what data does the AI really need?

3. Outcome:

   • The group decides on rules to ensure the AI tool is used ethically, like checking it for fairness each semester or giving students a say in how the algorithm weighs preferences.

By practicing this approach, your school learns to think critically about AI, making decisions that balance efficiency with fairness.

Takeaways

• Conflict-based learning: Through realistic scenarios, you see how AI can fail certain groups, raising important ethical issues.
• Role-playing: Helps you understand different viewpoints—like the legal angle, technical side, and public concern.
• Structured problem-solving: Breaking down conflicts into steps ensures thoughtful, balanced choices.
• Real-life application: These exercises aren’t just for the classroom. They mirror real debates in companies and communities dealing with AI challenges.

When you tackle dilemmas about fairness, bias, and ethics in AI—using role-play and a clear resolution process—you grow in empathy, negotiation skills, and moral responsibility. You’ll learn not only how AI systems can fail but also how to propose strong, ethical solutions.

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