Certificate of Achievement
has completed the following course:
AI and Ethics: Privacy, Surveillance, Bias and Transparency
This course explored how AI works, examined the ethical issues surrounding privacy, surveillance, bias, and transparency, and evaluated whether AI systems can be trusted to make important decisions. Through discussions, activities, and real-world examples, learners applied ethical reasoning to contemporary issues relating to data collection and a lack of transparency in AI systems. Learners also critically reflected on codes of ethics and the kinds of AI systems should be built and developed.
2 weeks, 2 hours per week
Dr Paula Satne
Lecturer in Applied Ethics
University of Leeds
Transcript
Learning outcomes
- Explain key concepts and forms of artificial intelligence.
- Discuss the ethical significance of privacy, consent, and personal data collection in AI systems.
- Evaluate contrasting philosophical approaches to privacy and surveillance and apply them to contemporary AI-related issues.
- Explain how unfair bias can enter AI systems and the ethical concerns this raises.
- Discuss the ‘black box’ problem and its implications for transparency, accountability, and decision-making.
- Evaluate real-world cases and ethical responses to bias and opacity in AI systems.
Syllabus
- Introduces the foundations of artificial intelligence, including key concepts, forms of AI, machine learning, neural networks, algorithms and the role of data in modern AI systems.
- Explores privacy, consent, data collection and surveillance, examining how AI technologies rely on personal data and why these practices raise ethical concerns.
- Engage with competing philosophical perspectives on privacy and surveillance, considering different views on data ownership, privacy violations and the social impact of digital technologies.
- Explore bias in AI systems, including how unfair outcomes can arise through data collection, algorithm design and the deployment of AI technologies in real-world settings.
- Investigates the ‘black box’ problem in AI, exploring questions of transparency, explainability, accountability and responsibility in automated decision-making.
- Through case studies and contemporary debates, learners consider ethical responses to AI challenges, including facial recognition, predictive algorithms in criminal justice, professional codes of ethics and proposals for more trustworthy AI systems.
Issued on 3rd November 2026
The person named on this certificate has completed the activities in the transcript above. For more information about Certificates of Achievement and the effort required to become eligible, visit futurelearn.com/proof-of-learning/certificate-of-achievement.
This certificate represents proof of learning. It is not a formal qualification, degree, or part of a degree.