Data Governance vs AI Governance

- Overview

Data governance and AI governance are both crucial for modern organizations, but they serve distinct purposes. Data governance ensures the accuracy, security, and trustworthiness of data feeding into AI models. AI governance ensures these models are deployed, operated, and monitored ethically, transparently, and aligned to societal values and organizational priorities. 

Data governance focuses on managing the availability, usability, integrity, and security of an organization's data. The goal is to ensure data is used accurately, consistently and responsibly, and in compliance with regulations and internal policies. Key functions include data quality management, data security, metadata management, data management and data lifecycle management.

AI governance oversees the processes, policies, and controls for the development and deployment of AI projects. It coordinates and enforces rules, processes, and requirements to align AI initiatives with organizational goals. Key activities include model documentation, risk management, bias and fairness assessment, auditability and accountability of AI systems.

Understanding and implementing these two governance frameworks is critical for organizations to maintain trust and compliance as they scale their AI and data initiatives. AI governance is broader in nature, covering goals beyond data protection compliance, such as bias prevention and model explainability.

- Regulatory Landscape

The regulatory landscape for AI governance and data governance differs in several ways:

• AI Governance: AI governance is governed by regulations that are specific to AI, such as the EU AI Act, which focuses on risk management and ethical considerations. Other regulations include the NYC Bias Law, which requires annual audits of automated employment decision tools to prevent bias in hiring. AI governance also involves creating standards and practices for data collection, storage, and use to prevent misuse and mishandling. These standards can directly impact the fairness, accuracy, and security of AI models.
• Data Governance: Data governance is driven by regulations that focus on data security and privacy, such as GDPR, DPA, CCPA, and PIPEDA. Data governance policies should include procedures for managing data quality and integrity to prevent errors and inconsistencies.

As AI technologies continue to advance, governments and regulatory bodies are developing new regulations to address emerging challenges. Some common themes in the regulatory landscape include: transparency, human agency and oversight, accountability, and technical robustness and safety.

- The Future of Data and AI Governance

The future of data and AI governance is linked and will shape future business practices. AI governance frameworks can influence the development of responsible and sustainable AI solutions that prioritize data privacy and security. This integration can promote the ethical and responsible use of data-driven technologies. 

Here are some trends in the future of data and AI governance: 

• Human-centric AI design: AI systems should augment human capabilities instead of replacing them. This can involve incorporating human oversight mechanisms, feedback loops, and user-friendly interfaces.
• Automatic data tagging: AI functions can help people tag data and set policies on it as it comes in.
• Skill sets: Skill sets will need to be continuously upgraded and adapted as the world changes.

However, there are also challenges to consider, such as:

• Data privacy: AI can compound the challenges of data privacy.
• Accountability, transparency, and fairness: As AI systems make decisions that impact individuals and societies, questions about these concepts become important. For example, who is responsible when an AI system makes a mistake?
• Societal biases: AI systems can perpetuate or exacerbate societal biases because they are human creations and are subject to the same biases as people.

[More to come ...]