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Introduction

Instruction and application
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Can you think of an ethical dilemma that could happen because an artificial intelligence (AI) or a machine learning (ML) process was not implemented responsibly? You might think of an AI system that unintentionally discriminates against certain groups, or an ML model that leaks personally identifiable information.

These scenarios are not just hypothetical. They are practical challenges you may face as you implement AI/ML techniques in your role. In this unit, we explore these ethical challenges and the steps needed to prevent them.

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Why does this unit matter?​

AI and ML models are increasingly becoming a core aspect of our roles. Using them ethically and responsibly is a responsibility we all share.

Ethical practice helps us build trustworthy solutions that improve operations and reduce risks like bias, privacy breaches and reputational damage. It also enables you to champion responsible practices within your organisation and make informed decisions that contribute to a more ethical and sustainable future for AI/ML.

Learning objectives​

By the end of this unit, you will be able to:

  • Analyse the ethical aspects associated with data collection and usage and ML model deployment in AI and ML projects.
  • Demonstrate integrity in decision-making processes for AI and ML projects, ensuring compliance with legal, ethical and regulatory requirements.
  • Evaluate concepts of data governance in AI and ML projects, including regulatory requirements, data privacy, security, trustworthiness and quality control.
  • Create guidelines for ethical data sharing and collaboration in AI and ML projects, balancing innovation with privacy and security concerns.

Action item: Self-reflection​

Before diving into the unit, take a moment to reflect on how ethical AI/ML practices intersect with real-world challenges you may have encountered.

Consider your prior knowledge:

• What do you consider to be best practices for handling data? • Have you ever heard of (or been in) a situation where your data was not handled responsibly?
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How do you think ML and AI ethics might already influence your daily work or business operations?
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Think about the real-world applications: Think of a scenario in your organisation where machine learning could be applied and consider:

What ethical concerns might arise? • Could the data introduce biases that affect outcomes unfairly? • How can user privacy be protected when handling data? • Are there potential unintended consequences of the model's predictions?
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In progress