Practical tips for the ethical use of AI and AI development tools

Understand the Data You Are Using

Ethics in AI begins with understanding the data you use for training models. Familiarise yourself with where your data comes from, how it was collected, and any potential biases it may contain. Be cautious with datasets that may be biased or unrepresentative of your target audience. Use diverse, representative datasets to avoid skewing results and producing biased outputs.
Avoid Over-Collecting Personal Data

When developing AI systems, collect only the data that is necessary for your model’s purpose. Avoid gathering excessive personal data that could compromise privacy. Ensure that any personal data you do collect is managed responsibly, with robust security measures and adherence to data privacy laws such as GDPR. Using anonymised or aggregated data where possible is a good practice to minimise risks to user privacy.
Regularly Test and Validate Models for Fairness

Ensure your AI models treat all groups fairly by conducting fairness and bias checks. Tools like IBM’s AI Fairness 360 and Microsoft’s Fairlearn can help you test for biased outcomes. Regular validation against diverse user groups will ensure your model performs equitably, with metrics that track fairness across different demographics.
Prioritise Explainability and Transparency

Choose AI models that offer transparency in their decision-making, especially if they influence important decisions, like hiring or credit scoring. Explainability tools, such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations), can help clarify why the AI makes certain decisions. Users should understand, at least at a basic level, how the AI operates and what factors influence its outputs.
Consider Potential Societal Impacts

Ethics in AI means considering the broader social impact of your system. Ask yourself how your AI application could affect people’s lives or society, especially in critical areas like healthcare, finance, or law enforcement. Ensure your AI systems are designed to minimise harm, avoid discrimination, and uphold the welfare of all users.
Maintain User Privacy and Data Security

Protecting user privacy and data security is essential in any AI system. Use encryption for data at rest and in transit, ensure secure access controls, and follow best practices for data storage. Transparency about data use, clear privacy policies, and providing users with control over their data (such as options to opt out) are all fundamental ethical practices.
Be Aware of Dark Patterns in AI Use

Avoid using AI in ways that manipulate or mislead users (known as dark patterns). These include techniques that subtly nudge users into decisions they may not want to make, such as hidden subscription renewals or overly aggressive personalisation. Design AI systems that respect user autonomy and promote informed decision-making.
Monitor and Review Models After Deployment

AI systems may change behaviour over time as data shifts, so continuous monitoring is critical. Regularly audit your AI systems for fairness, accuracy, and performance. This ongoing review helps you detect and correct any ethical issues that emerge post-deployment, such as biases or data drift.
Involve a Diverse Team for Broader Perspectives

Involve diverse team members in your AI development process. Different perspectives can help identify potential ethical issues that may go unnoticed by a homogeneous team. Collaboration with people from various backgrounds can reduce unintentional biases and promote inclusivity in AI design.
Stay Informed About AI Regulations and Ethical Standards

Familiarise yourself with AI regulations and ethical standards, such as the EU’s AI Act, UK AI guidance, or your organisation’s internal ethics policies. Keeping up with industry guidelines helps you align your work with current best practices and legal requirements. Following codes of ethics from professional bodies like the ACM or IEEE can also provide a strong ethical foundation for AI development.
Provide Transparency in AI Decision-Making

Clearly communicate the role of AI in your application, especially if it influences important user decisions. For example, if a recommendation system filters job candidates, inform users about how AI is involved and its limitations. This transparency can build trust and helps users understand the AI’s role in their experience.
Be Prepared to Take Responsibility for AI Decisions

AI is often perceived as an objective tool, but in reality, it reflects the data and parameters it’s built upon. As a developer, be prepared to take responsibility for your AI’s outputs, addressing and correcting unintended consequences or biases. This commitment to ethical responsibility will encourage you to continually improve your systems for the benefit of all users.