AI Ethics in Business Steven Katz (Rutgers State University)

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MODULE 1: Understanding AI and Ethical Considerations

1.1 Key Actionable Takeaways

1.1.1 Understanding AI

  • Define AI: Clearly establish its scope and functionality.
  • AI as a Tool: Recognize it as driven by human intention, not malice or self-determination.
  • Examples of AI Applications: Identify potential benefits and risks.

1.1.2 Setting Priorities for AI Use

  • Framework for AI-Based Tools: Focus on desired outcomes and discourage harmful applications.
  • Clear Goals: Impact on automation, privacy, commerce, and societal aspects.

1.1.3 Guiding Principles for Ethical AI

  • Transparency: Ensure systems are understandable.
  • Accountability: Hold developers and users responsible.
  • Fairness: Minimize bias and promote equitable treatment.
  • Privacy: Safeguard personal data and uphold individual rights.

1.1.4 Addressing Ethical Concerns

  • Mitigate Risks: Job displacement, biased decision-making, loss of privacy.
  • Proactive Management: Maximize societal benefits while managing potential harms.

1.1.5 Shaping AI Regulation

  • Current and Future Uses: Define AI’s impact on society.
  • Advocate for Policies: Balance innovation with safety, fairness, and accountability.

1.1.6 Driving Positive Change with AI

  • Ethical Use: Create tools that solve problems and improve quality of life.
  • Shape AI’s Role: Ensure equitable and responsible use.

1.2 Understanding AI vs. Non-AI Programs

1.2.1 Non-AI Programs

  • Pre-Programmed Rules: Do not learn or improve over time.
  • Examples: Calculators.

1.2.2 AI Programs

  • Machine Learning: Analyze large datasets and improve performance.
  • Human-Like Intelligence: Approximate human responses (e.g., Turing Test).

1.3 Characteristics of AI

  • Self-Correction: Improve using data (machine learning).
  • Complex Problem Solving: Traditionally associated with human intelligence.
  • Weak AI: Designed for specific tasks under human direction.

1.4 Machine Learning: Key Processes

1.4.1 Supervised Learning

  • Classification and Prediction: Train AI with labeled data.
  • Improve Accuracy: Expected outputs guide learning.

1.4.2 Unsupervised Learning

  • Discover Patterns: Find associations in unlabeled data.
  • Applications: Personalized recommendations.

1.5 Potential and Limitations of AI

  • Focus on How AI is Built: Data and methods used.
  • Applications Over Hypotheticals: Avoid fear of “strong AI” or sentient machines.
  • Utility: Identify non-intuitive patterns and solve complex problems.

1.6 Applications of AI

  • Daily Life: Automated recommendations, predictive analytics, AI-driven tools.

1.7 Understanding the Turing Test

  • Evaluate Human-Like Intelligence: Based on behavior, not internal qualities.
  • Limits: Measures conversational mimicry, not general intelligence.
  • Criticisms: Focuses on deception, not true comprehension.

1.8 Strong vs Weak AI

1.8.1 Weak AI (Narrow AI)

  • Specific Tasks: Chatbots, smart assistants, self-driving cars.
  • Applications: Automation, process optimization.

1.8.2 Strong AI (AGI)

  • Theoretical: Capable of learning and adapting like humans.
  • Future Applications: Advanced robotics, healthcare, security.

MODULE 2: Using Generative AI (GenAI) Tools

2.1 General Overview of AI Tools

  • Self-Driving Cars: Recognize obstacles, signs, pedestrians.
  • Spam Filters: Identify spam based on marked emails.
  • Automated Hiring: Match candidates to job profiles.
  • Diagnostic Programs: Interpret medical data.
  • Recommendation Systems: Suggest content based on user preferences.

2.2 Understanding Generative AI (GenAI)

  • Examples: ChatGPT, DALL-E.
  • How They Work: Pattern recognition, not actual intelligence.
  • Potential Risks: Misleading competence, concerns about capabilities.

2.3 Key Actions to Consider When Using GenAI

  • Avoid Plagiarism: Disclose AI use in content creation.
  • Verify Information: Validate AI-generated answers.
  • Be Aware of Bias: Supplement research with diverse perspectives.
  • Develop Skills Independently: Avoid over-reliance on AI.
  • Avoid Sharing Sensitive Information: Be cautious with personal data.

2.4 Ethical Considerations in AI

  • AI and Potential Harm: Risks from malfunction and misuse.
  • Corporate Responsibility: Hold corporations accountable.
  • Individual Accountability: Educate on AI dangers.
  • AI Ethics: Develop moral guidelines for AI use.

MODULE 3: Unconscious Bias and AI

3.1 Problem: Bias in AI and Hiring Practices

  • Preexisting Bias: Human biases lead to unfair hiring practices.
  • AI Training: Biased data reinforces discrimination.

3.2 AI’s Role in Perpetuating Bias

  • Reinforcement of Bias: AI replicates historical biases.
  • Examples: Discrimination in hiring, medical diagnostics, credit scoring.

3.3 Potential Solutions and Limitations

  • Remove Sensitive Data: May not eliminate hidden biases.
  • Monitor AI Output: Continuous oversight to prevent discrimination.

3.4 Accountability of AI Ethics

  • AI’s Impact on Decision-Making: Fast and accurate, but can make errors.
  • Responsibility: Human oversight required for accountability.

MODULE 4: Ethical Issues with AI Use

4.1 AI Misuse

  • Harmful Content: Misinformation, unsafe health advice.
  • Low-Quality Content: Flood search results, mislead users.

4.2 Regulation Approaches to AI

  • No Legislation: Market decides effectiveness.
  • Reactive Regulation: Laws enacted after misuse.
  • Proactive Regulation: Anticipate harms, create regulations in advance.

4.3 Ethical Concerns of AI in the Workforce

  • Invasive AI Monitoring: Physical and mental strain on workers.
  • Automation: Job displacement and creation of harmful work conditions.

MODULE 5: AI in Robotics and Autonomous Freight

5.1 Robotics and Automation Potential

  • Current Focus: Manufacturing, single-purpose robots.
  • Future Aspirations: Law enforcement, military robots, bomb defusal.

5.2 Self-Driving Vehicles and Trucks

  • Advantages: Improve health, safety, work-life balance.
  • Risks: Unreliable technology, job displacement.

5.3 AI’s Role in Translation and Transcription

  • Benefits: Enhance accessibility, reduce costs.
  • Drawbacks: Lack of contextual understanding, reduced quality.

5.4 Future Implementation Strategies for AI

  • Market-Driven Approach: Allow market to dictate AI use.
  • Preemptive Action: Ban AI for certain tasks, mandate transparency.

5.5 Monitoring AI: Consequences and Conclusion

  • Understanding AI’s Societal Impact: Anticipate future effects.
  • Addressing Unethical AI Use: Implement consequences for misuse.
  • Ensuring AI Benefits Society: Maximize positive impact through ethical regulation.

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