📋 Group Discussion (GD) Analysis Guide: Should AI be Used in Making High-Stakes Decisions such as Medical Diagnoses or Criminal Sentencing?

🌐 Introduction to the Topic

• 🔍 Opening Context: Artificial intelligence is rapidly transforming industries, with its applications stretching from simple automation to high-stakes domains like healthcare and criminal justice. The ethical and operational implications of AI in these fields make it a critical discussion for future leaders.
• 💡 Background: AI’s role in decision-making has seen increased adoption, with algorithms aiding medical diagnostics and judicial predictions. However, concerns about bias, accountability, and societal impact persist, fueling a global debate.

📊 Quick Facts and Key Statistics

🛠️ Stakeholders and Their Roles

• 🌍 Governments: Define regulations and frameworks for ethical AI deployment.
• 🏥 Healthcare Providers: Utilize AI for diagnostics while ensuring patient safety.
• ⚖️ Judicial Systems: Incorporate AI tools while maintaining human oversight.
• 💻 AI Developers: Create transparent, bias-free algorithms.

🏆 Achievements and Challenges

✨ Achievements

• 🩺 Enhanced Diagnostics: AI detects breast cancer with 99% accuracy (Nature, 2022).
• 🔍 Crime Prediction: Predictive policing reduced crimes in Chicago by 15% (2019).

⚠️ Challenges

• ⚖️ Ethical Dilemmas: Bias in judicial algorithms leading to unfair sentencing.
• 📉 Accountability Issues: Difficulty attributing errors in AI-based decisions.

📄 Structured Arguments for Discussion

• 🟢 Supporting Stance: “AI can enhance decision-making efficiency and reduce human error, crucial in medical and legal contexts.”
• 🔴 Opposing Stance: “AI’s inherent biases and lack of transparency make it unsuitable for high-stakes decisions.”
• ⚪ Balanced Perspective: “AI should augment, not replace, human decision-making, ensuring accountability and fairness.”

🗣️ Effective Discussion Approaches

• 📊 Opening Approaches:
  • 📈 Begin with a statistic: “AI can detect diseases like breast cancer with over 99% accuracy.”
  • ⚖️ Highlight an ethical issue: “Should an AI system decide life-or-death situations like criminal sentencing?”
• 🔄 Counter-Argument Handling:
  • 📊 Bias in AI: Acknowledge and suggest stricter testing and training data diversification.
  • ⚖️ Accountability Concerns: Propose hybrid models combining AI tools with human oversight.

🔍 Strategic Analysis of Strengths and Weaknesses

• 💪 Strengths: Efficiency, error reduction, scalability.
• ⚠️ Weaknesses: Bias, lack of empathy, ethical dilemmas.
• 🌟 Opportunities: Collaboration in hybrid decision-making systems.
• 🚨 Threats: Public mistrust and misuse.

🎓 Connecting with B-School Applications

• 🌟 Real-World Applications: AI’s role in operational decisions, healthcare innovations, and ethical business management.
• ❓ Sample Interview Questions:
  • “How can ethical concerns in AI be addressed in decision-making?”
  • “Should businesses rely on AI for strategic decisions?”
• 💡 Insights for Students: Understanding AI ethics is crucial for leadership in tech-driven industries.