BCS Foundation Certificate in Artificial Intelligence

1.1 Identify the key definitions of key artificial intelligence terms.

• Indicative content

1. Human intelligence
2. Artificial intelligence
3. Machine learning
4. Scientific method

1.2 Describe key milestones in the development of artificial intelligence

• Indicative content

1. Asilomar principles.
2. Dartmouth conference of 1956.
3. AI winters.
4. Big data and the internet of things (IoT).
5. Large language models (LLMs).

1.3 Describe different types of AI.

• Indicative content

1. Narrow/weak AI.
2. General/strong AI.

1.4 Explain the impact of AI on society

• Indicative content

1. Ethical principles.
2. Social impact .
3. Economic impact.
4. Environmental impact.
5. UN 17 Sustainable Development Goals (SDGs). f. EU AI Act (2024)

1.5 Describe sustainability measures to help reduce the environmental impact of AI

• Indicative content

1. Green IT initiatives.
2. Data centre energy and efficiency.
3. Sustainable supply chain.
4. Choice of algorithm.
5. Low-code/no-code programming.
6. Monitoring and reporting environmental impact.

2.1 Describe ethical concerns, including bias and privacy, in AI

• Indicative content

1. What is ethics?
2. Differences between ethics and law.
3. Ethical concerns:

• potential for bias, unfairness, and discrimination.
• data privacy and protection.
• impact on employment and the economy.
• autonomous weapons.
• autonomous vehicles and liability framework.

2.2  Describe the importance of guiding principles in ethical AI development.

• Indicative content
• UK AI Principles and other relevant legislation.
• Safety, security and robustness.
• Transparency and explainability.
• Fairness.
• Accountability and governance.
• Contestability and redress.

1. What is ethics?

2.3 Explain strategies for addressing ethical challenges in AI projects.

• Indicative content

1. Challenges:

• Self-interest.
• Self-review.
• Conflict of interest.
• Intimidation.
• Advocacy.

1. Strategies:

• Dealing with bias.
• Openness.
• Transparency.
• Trustworthiness.
• Explainability.

2.4 Explain the role of regulation in AI.

• Indicative content

1. The need for regulation.
2. The AI regulation landscape, e.g. WCAG.
3. Data Protection Act 2018 and UK GDPR.
4. International Standards Organisation (ISO, NIST).
5. The consequences of unregulated AI.

2.5 Explain the process of risk management in AI.

• Indicative content

1. Risk:

• Risk definition
• Risk management

1. Techniques:

• Risk analysis.
• SWOT analysis.
• PESTLE.
• Cynefin.

1. Navigate AI-related regulations and standards:

• UK AI Principles.

1. Risk mitigation strategies:

• Ownership and accountability.
• Stakeholder involvement.
• Subject matter experts

3.1 List common examples of AI.

• Indicative content

1. Human compatible.
2. Wearable.
3. Edge.
4. Internet of Things.
5. Personal care.
6. Self-driving vehicles.
7. Generative AI tools.

3.2 Describe the role of robotics in AI.

• Indicative content

1. Definition of robotics
2. Intelligent or non-intelligent.
3. Types of robots:

• Industrial.
• Personal.
• Autonomous.
• Nanobots.
• Humanoids.

1. Robotic process automation (RPA).

3.3 Describe machine learning.

• Indicative content

1. Machine learning
2. Neural networks
3. Deep learning
4. Large language models

3.4 Identify common machine learning concepts.

• Indicative content

1. Prediction.
2. Object recognition.
3. Classification including random decision forests.
4. Clustering.
5. Recommendations (e.g. Netflix, Spotify)

3.5 Describe supervised and unsupervised learning.

• Indicative content

1. Supervised learning.
2. Unsupervised learning.
3. Semi-supervised learning

4.1 Describe key data terms.

• Indicative content

1. Big data
2. Data visualisation
3. Structured data

4.2 Describe the characteristics of data quality and why it is important in AI.

• Indicative content

1. 5 data quality characteristics:

• Accuracy – is it correct?
• Completeness – is it all there?
• Uniqueness – is it free from duplication?
• Consistency – is it free from conflict?
• Timeliness – is it current and available?

2. Data is money.

3. Data provides insight and supports decision making.

4. Implications of poor quality data can be:

• Errors and inaccuracies.
• Bias.

4.3 Explain the risks associated with handling data in AI and how to minimise them.

• Indicative content

1. Bias:

• Multiple sources.
• Diversity in people handling data and training AI.
• Fairness metrics.

2. Misinformation:

• Checking the reliability of sources.
• SME checks.

3. Processing restrictions:

• Organisational requirements.
• Frameworks and regulations.

4. Legal restrictions:

• UK GDPR.
• DPA 2018.
• Staying abreast of new requirements.

5. The scientific method

4.4 Describe the purpose and use of big data.

• Indicative content

1. Storage and use.
2. Understanding the user.
3. Improving prcoess.
4. Improving experience

4.5 Explain data visualisation techniques and tools.

• Indicative content

1. Written.
2. Verbal.
3. Pictoral.
4. Sounds.
5. Dashboards and infographics.
6. Virtual and augmented reality.

4.6 Describe key generative AI terms.

• Indicative content

1. Generative AI
2. Large languge models (LLMs)

4.7 Describe the purpose and use of generative AI including large language models.

• Indicative content

1. Trained on huge volumes of data.
2. Uses training to predict next word in text.
3. Generates coherent and human-sounding language.

1. Prompt engineering.
2. Natural language processing.
3. Image generation.

4.8 Describe how data is used to train AI in the Machine Learning process.

• Indicative content

1. Stages of the Machine Learning process:

• Analyse the problem.
• Data Selection.
• Data Pre-processing.
• Data Visualisation.
• Select a Machine Learning model (algorithm).

– Train the model.

– Test the model.

– Repeat (Learning from experience to improve results).

• Review.

5.1 Idenitfy opportunities for AI in your organisation.

• Indicative content

1. Opportunities for automation.
2. Repetitive tasks.
3. Content creation – generative AI.

5.2 List the contents and structure of a business case.

• Indicative content

1. Introduction.
2. Management or executive summary.
3. Description of current state.
4. Options considered.

• Option described.
• Analysis of costs and benefits.
• Impact assessment.
• Risk assessment.

1. Recommendations.
2. Appendices/supporting information.

5.3 Identify and categorise stakeholders relevant to an AI project.

• Indicative content

1. Stakeholder definition.
2. Stakeholder categorisation.

• Power/interest grid.
• Stakeholder wheel.

5.4 Describe project management approaches.

• Indicative content

1. Agile.
2. Waterfall.
3. Hybrid

5.5 Identify the risks, costs and benefits associated with a proposed solution.

• Indicative content

1. Risk analysis.

• Risk assessment.
• Risk owners.

1. Risk appetite.
2. Risk management strategies.

• Accept.
• Mitigate (including sharing, contingency planning).
• Avoid.
• Transfer.

1. Financial costs and benefits.

• Forecasting.
• Margin for error.

1. Socio-economic benefits.
2. Triple bottom line

5.6 Describe the ongoing governance activities required when implementing AI.

• Indicative content

1. Compliance.
2. Risk management.
3. Lifecycle governance.

• Manage.
• Monitor.
• Govern.

6.1 Describe the roles and career opportunities presented by AI.

• Indicative content

1. AI specific roles
2. Opportunities for existing roles.

• Additional training and knowledge.
• Improved efficiency.
• Automation

6.2 Identify AI uses in the real world.

• Indicative content

1. Marketing.
2. Healthcare.
3. Finance.
4. Transportation.
5. Education.
6. Manufacturing.
7. Entertainment.
8. IT

6.3 Explain AI’s impact on society, and the future of AI.

• Indicative content

1. Benefits of AI.
2. Challenges of AI.
3. Potential problems of AI.
4. Societal impact.
5. Environmental impact – sustainability, climate change and environmental issues.
6. Economic impact – job losses, retraining for new AI roles.
7. Potential future advancements and direction of AI.
8. Human plus machine

6.4 Describe consciousness and its impact on ethical AI.

• Indicative content

1. What is human consciousness?
2. What is AI consciousness?
3. Kurzwell Singularity
4. Functional capabilities v human consciousness.
5. AI projects in light of ethical considerations and consciousness.
6. Ethical challenges associated with artificial consciousness