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Davidson College

AI Ethics for Professionals

Davidson College via edX

Overview

In a world where Artificial Intelligence is rapidly transforming every aspect of our lives, understanding the ethical implications of AI has never been more crucial. This comprehensive AI Ethics course equips you with the knowledge and skills to navigate the complex landscape of AI development and deployment responsibly.

Over four engaging units, you'll dive deep into the promises and perils of generative AI, explore key ethical challenges, and master practical frameworks for ethical AI governance. Whether you're a developer, business leader, policymaker, or simply an AI enthusiast, this course will empower you to make informed decisions in the age of AI.

Key Learning Objectives

  • Understand the fundamental principles of AI ethics and their importance in AI development and deployment.

  • Analyze the societal impact of AI technologies, including privacy, employment, and social interaction implications.

  • Identify and critically evaluate key ethical challenges in AI, such as bias, transparency, accountability, and potential misuse.

  • Examine legal and regulatory considerations surrounding AI, including current laws and the role of regulation.

  • Develop and apply ethical frameworks for AI decision-making in various contexts.

  • Evaluate and compare different AI governance approaches, from organizational policies to national and international regulations.

  • Communicate effectively about AI ethics issues and proposed solutions to both technical and non-technical audiences.

  • Promote responsible AI development and use within organizations and broader society.

Why It Matters:

As AI becomes increasingly integrated into our society, the ability to think critically about its ethical implications is not just valuable—it's essential. This course will give you the tools to:

  • Lead AI strategy and governance efforts in your organization

  • Make ethically informed decisions when developing or deploying AI systems

  • Contribute meaningfully to the global conversation on responsible AI

By the end of this course, you'll have a robust understanding of AI ethics principles and practical skills to apply them in real-world scenarios. Join us in shaping an ethical future for AI—enroll today!

Syllabus

Unit 1: GenAI Promises and Risks

The promise and peril of generative AI

  • Distinctions between machine learning and generative AI
  • AI alignment: challenges in aligning AI with human values, interests, and rights
  • Transparency in AI systems: importance and challengesAI security: types of attacks and vulnerabilities
  • Bias in AI: sources, implications, and case studies
  • The problem of responsibility in AI-driven decisions

Unit 2: How Can We Trust AI?

  • Key elements of trustworthy AI systems
  • Safety and harm mitigation strategies
  • Privacy and data security considerations in AI
  • Explainability and interpretability in AI systems
  • Addressing potential biases in AI models
  • Case studies on AI trustworthiness (e.g., ChatGPT, facial recognition technologies)
  • Human-in-the-loop approaches to AI oversight

Unit 3: AI Governance and Regulation

  • Overview of AI governance approaches
  • The EU AI Act: risk-based classification and governance structures
  • U.S. federal efforts in AI governance: The Blueprint for an AI Bill of Rights
  • Executive Order on Safe, Secure, and Trustworthy AI
  • NIST AI Risk Management Framework
  • Comparing EU and U.S. approaches to AI regulation
  • AI governance in enterprises: case study of IBM's AI ethics structure
  • Implementing AI ethics in small and medium-sized businesses
  • Balancing top-down and bottom-up approaches to AI ethics
  • Challenges and considerations in organizational AI governance

Unit 4: Communicating AI Ethics

  • Purpose and importance of AI ethical statements
  • Analyzing and comparing organizational AI principles (e.g., Google, Anthropic)
  • Elements of effective ethical statements:
  • Communicating values and commitments
  • Specifying actionable policies and processes
  • Grounding ethical statements in broader ethical frameworks
  • Addressing specific ethical concerns (e.g., bias, privacy, safety)
  • Translating ethical principles into governance structures
  • Communicating AI ethics to various stakeholders (e.g., users, developers, policymakers)Practical exercise: Crafting and critiquing AI ethics statements

Taught by

Jayme Sponsel and Sara Copic

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