Data governance

This guideline is intended for parliamentary staff involved in the oversight and implementation of AI-based systems, including business managers, chief information officers, chief technology officers and IT managers. It will also be useful for senior parliamentary managers responsible for AI governance.

This guideline outlines the desirable characteristics of data for safe AI systems development. It highlights data-quality issues to avoid during the planning and development of AI systems, and explains how to implement privacy principles to protect personal data – an essential factor for AI development. These practices should be guided by data governance that aligns with the organization’s needs.

Since data exists independently of AI systems and is used by parliaments, data management practices should be established before any AI initiatives commence. This lays the foundation for trustworthy AI systems in legislative bodies.

The quality and reliability of AI system outputs depend heavily on two factors:

• The quality of data used to train the AI model
• The quality of data the AI system uses during its operation

It is crucial to protect this data from unauthorized access and misuse. Improving data quality and enhancing data protection are key components of an organization’s AI governance strategy. Achieving these improvements requires coordinated actions and agreements among various stakeholders involved in data-related decisions and processes.

Data governance plays a crucial role here. It involves coordinating and managing the efforts of all stakeholders to enhance data quality and protect privacy. By effectively implementing data governance practices, organizations can ensure they are developing AI systems that are reliable and trustworthy.

Data quality refers to certain features of data that make it accessible, useful and reliable to support effective decision-making. Data must be demonstrably accurate, complete, consistent, accessible, relevant and secure.

For further discussion of this subject, refer to the sub-guideline Data governance: Data quality.

Personal data protection refers to practices, policies and legislation designed to safeguard individuals’ personal data from unauthorized access, misuse or exposure. It encompasses various measures to ensure that personal data is collected, stored, processed and shared in a way that respects individuals’ privacy and complies with relevant laws and regulations.

For further discussion of this subject, refer to the sub-guideline Data governance: Personal data protection.

Data governance is crucial for coordinating efforts to enhance data quality and privacy. It encompasses policies, roles, responsibilities, processes and technology aimed at improving data quality and creating an optimal data environment. 

For further discussion of this subject, refer to the sub-guideline Data governance: Data governance in a parliamentary context.

Data management is a crucial factor in the implementation of AI systems in parliaments. Staff must understand the key steps for establishing effective data management practices, including creating governance policies, managing metadata, ensuring data quality and protecting personal information. 

For further discussion of this subject, refer to the sub-guideline Data governance: Data management for AI systems.

• Australian Government, Office of the Australian Information Commissioner: Australian Privacy Principles guidelines

• Government of Brazil: Guia de Elaboração de Programa de Governança em Privacidade (available in Portuguese only)

• Government of the United Kingdom: The Government Data Quality Framework

• Government of the United Kingdom: Using personal data in your business or other organisation

• Norwegian Data Protection Authority: Artificial intelligence and privacy

• Publications Office of the European Union: Data quality requirements for inclusive, non-biased and trustworthy AI: Putting science into standards

The Guidelines for AI in parliaments are published by the IPU in collaboration with the Parliamentary Data Science Hub in the IPU’s Centre for Innovation in Parliament. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence. It may be freely shared and reused with acknowledgement of the IPU. For more information about the IPU’s work on artificial intelligence, please visit www.ipu.org/AI or contact [email protected].

About the guidelines | The role of AI in parliaments | Introducing AI applications | Inter-parliamentary cooperation for AI | Strategic actions towards AI governance | Risks and challenges for parliaments | Generic risks and biases | Ethical principles | Risk management | Alignment with national and international AI frameworks and standards | Project portfolio management | Data governance | Systems development | Security management | Training for Data Literacy and AI Literacy | Glossary of terms