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Ethical frameworks in AI and ML

Instruction and application
Complete

Every day, huge amounts of data are processed using AI/ML techniques to create insights for organisations. The challenge is making sure this work is done responsibly. In this lesson, you will explore the foundational frameworks and principles that guide ethical decision-making in AI projects.

What do we mean by ethics?​

In data and AI, ethics is about evaluating how systems and inputs are designed, then making value-based judgements in a personal and professional context.

Definitions from leading organisations​

  • Alan Turing Institute (2025): "Data ethics is a branch of ethics that studies and evaluates moral problems related to data, algorithms (including AI/ML) and corresponding data practices."
  • Open Data Institute (2019): "Data ethics evaluates data practices that can adversely impact people and society in collection, sharing and use."
  • UK Government (2024): "Data ethics refers to principles and moral obligations that guide how an organisation collects, shares and uses data, especially personal data, to ensure fairness and non-discrimination."

Why ethical frameworks matter​

Ethics is not just about compliance. It is about ensuring AI systems positively contribute to society while minimising harm. AI can influence critical outcomes such as hiring decisions, medical prioritisation and risk assessment.

Key point​

Without ethical oversight, AI systems can perpetuate or amplify existing bias, producing unfair or discriminatory outcomes.

For example, Amazon's AI-powered hiring tool was found to discriminate against female candidates because it learned from historical hiring data that reflected prior bias.

Like all models, a tool is only as strong as its training data. If the underlying data is biased, model outputs will be biased too. Switching to a different model alone will not fix this.

Without proactive safeguards, AI can reinforce systemic discrimination, violate privacy and reduce public trust. Ethical frameworks help teams evaluate risks, increase transparency and ensure decisions are fair and accountable.

Key ethical principles in AI​

According to the European Commission, responsible AI design and governance relies on four foundational principles:

Fairness​

AI should not disproportionately harm or benefit groups based on protected characteristics such as race, gender or socioeconomic background.

What fairness requires

  • Bias detection and correction in training datasets.
  • Algorithmic audits to test for disparate impact.
  • Diverse and representative data.

What you can do

  • Use high-quality datasets representative of the broader population.
  • Include fairness checks throughout model development and monitoring.

Accountability​

Developers, organisations and stakeholders must take responsibility for the impact of AI decisions.

What accountability requires

  • Clear ownership for AI outcomes.
  • Appeal mechanisms for people negatively affected by AI.

What you can do

  • Define escalation processes so users can contact a human when an issue occurs.

Transparency​

AI systems must be understandable to users, stakeholders and regulators. Opaque "black-box" systems reduce trust and limit governance.

What transparency requires

  • Clear documentation on how systems work.
  • User-friendly explanations for AI-driven decisions.
  • Inspectability for auditors and regulators.

What you can do

  • Maintain clear process documentation that can be shared when requested.

Explainability​

Explainability ensures AI decisions can be interpreted and justified by humans. This is often associated with Explainable AI (XAI).

What explainability requires

  • Simpler models where practical.
  • Interfaces and visual tools to inspect AI reasoning.
  • Modelling choices that balance performance and interpretability.

What you can do

  • Explain key concepts in plain language.
  • Prefer the simplest model that meets the problem requirements.

Action item: Quiz​

Let's check your understanding of these ethical principles.

Question 1 of 1
Which principle focuses on ensuring AI decisions can be understood and justified by humans?
  • A. Accountability
  • B. Fairness
  • C. Explainability
  • D. Transparency
Correct Answer: C

Feedback: Explainability is specifically about making AI outcomes understandable and justifiable to humans.