The Information Commissioner’s Office has published a new chapter on “Fairness in the AI Lifecycle”

The Information Commissioner’s Office (ICO) has published a new chapter on “Fairness in the AI Lifecycle” that explains how to ensure fairness in AI systems throughout their development and use. The ICO’s guidance covers data protection fairness considerations across the AI lifecycle, from project initiation to decommissioning.

The ICO’s guidance is aimed at individuals and organisations involved in AI development, such as data scientists, AI developers, and product managers. It provides an overview of potential sources of bias and how fairness can be impacted by fundamental aspects of AI development.

The AI lifecycle:

The AI lifecycle comprises several stages, from project initiation and design to decommissioning. The following sections provide an overview of how to use this fairness assessment across the different stages of the AI lifecycle.

1. Project initiation and design:

During the project initiation and design stage, you should consider the potential impact of your AI system on individuals and groups. You should also consider any legal and regulatory requirements and assess whether your AI system is likely to be biased or unfair.

2. Before you process personal data:

Before you process personal data, you should ensure that you have a lawful basis for doing so. You should also ensure that you have identified and assessed any potential risks associated with the processing of personal data and the use of AI.

3. Data collection and procurement:

During the data collection and procurement stage, you should consider the quality and diversity of the data you are collecting. You should also consider whether the data may contain bias, such as historical bias, and whether the data is representative of the population you are analysing.

4. Data analysis and pre-processing:

During the data analysis and pre-processing stage, you should consider the potential impact of any data pre-processing techniques on fairness. You should also ensure that any data pre-processing techniques used do not result in bias or discrimination.

5. Model development:

During the model development stage, you should consider the potential impact of your AI system on individuals and groups. You should also consider any potential sources of bias and ensure that your model is transparent and explainable.

6. Model evaluation:

During the model evaluation stage, you should assess the fairness of your AI system. You should also consider the potential impact of any model evaluation techniques on fairness and ensure that any evaluation techniques used do not result in bias or discrimination.

7. Model deployment and monitoring:

During the model deployment and monitoring stage, you should monitor the performance of your AI system to ensure that it continues to operate fairly. You should also consider any potential sources of bias that may arise during deployment and take appropriate steps to address them.

8. Retiring or decommissioning:

When retiring or decommissioning an AI system, you should ensure that any personal data is securely and appropriately disposed of. You should also ensure that any potential sources of bias or discrimination are appropriately addressed.

Conclusion:

Ensuring fairness in AI systems is critical to building trust and confidence in these systems. The ICO’s guidance on “Fairness in the AI Lifecycle” provides a framework for identifying and addressing potential sources of bias and discrimination throughout the AI development process. By following this guidance, individuals and organisations involved in AI development can ensure that their systems operate fairly and transparently.