WK11: Ethics in AI and Current Trends

Welcome Week 11

Ethics in AI and Current Trends

Module Lecturer: Dr Raghav Kovvuri

Email: [e-mail]

Session Overview

• Understand the importance of ethics in AI development.
• Explore key ethical principles and challenges.
• Analyze real-world case studies and current trends in AI.
• Discuss best practices for responsible AI development.

Introduction

Definition of AI Ethics: Principles guiding the moral and responsible design, development, and deployment of AI.

• Why ethics matter: Trust in technology, societal impact, and avoiding harm.
• Intersection: Examples like autonomous vehicles requiring moral decision-making.
• Practitioner roles: Advocacy for fairness, accountability, and ensuring societal benefit.

Core Ethics

Core Ethical Principles in AI:

Transparency:

• Ensuring AI systems are explainable and understandable.
• Clear user instructions, purpose, and limitations.

Fairness:

• Avoiding harm by actively identifying biases in data and algorithms.
• Regular fairness audits.

Accountability:

• Example frameworks for responsibility, e.g., role-specific checklists.

Key Ethical Challenges

Data Privacy and Security:

• Modern threats, like data breaches in AI-based healthcare systems.
• Regulatory trends like CCPA (California Consumer Privacy Act) alongside GDPR.

Bias and Discrimination:

• Real-life cases, e.g., Amazon’s AI hiring tool discriminating against women.
• Proactive solutions: Diverse training datasets, adversarial testing.

Ethical Considerations

Ethical Considerations in AI Applications

• Healthcare: Ethical AI in diagnostics and access equality.
• Employment: How automation in hiring affects minorities.
• Law Enforcement: Ethical dilemmas in predictive systems.

Current Trends in AI (2024)

Large Language Models:

• ChatGPT updates and industry usage.
• Limitations like hallucinations and content moderation issues.

Autonomous Systems:

• Ethics in autonomous drone usage and transport.

Responsible AI Development

• Best practices include adopting interdisciplinary ethical teams.
• Industry frameworks such as Google's AI Principles.

Case Studies

• Facial Recognition Controversies: Misuse in public surveillance.
• AI Hiring Tool Bias: Root causes and resolution.
• Social Media Algorithms: Ethics in content recommendation.

Discussion Points:

What could alternative approaches have been?

How does public participation shape ethical AI?

Future Considerations

• Emerging challenges in AI-human partnerships (e.g., caretaking robots).
• AI system rights debate and global governance proposals.

Discussion Topics

• Analyze a current AI application (healthcare, finance, etc.) for ethical implications.
• Debate the ethical challenges of AI autonomy.
• Discuss the balance between innovation and regulation in AI development.
• Research the role of culture in defining "ethical AI."
• Evaluate the impact of social media algorithms on public opinion.

Conclusion

Key Takeaways:

• Ethical AI development is critical for societal trust and sustainability.
• Transparency, fairness, and accountability are the pillars of ethical AI.
• Current trends and case studies highlight both progress and challenges.
• Future developments demand proactive, global governance and collaboration.

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