Data governance: The three essential considerations for responsible AI

Artificial Intelligence

Data is the lifeblood of AI models.

Photo credit: Courtesy | Reuters

Data is the lifeblood of AI models, providing the information they need to function effectively and improve over time. However, with great power comes great responsibility. Ensuring high-quality, secure, and private data is paramount for the effective and responsible use of AI technologies.

The world of AI is undergoing a revolution, and it’s bringing a powerful new asset to the table. AI’s ability to crunch massive data and automate tasks transforms industries, uncovering hidden business opportunities and streamlining operations.

This technological leap is granting organisations a competitive edge, boosting productivity, and paving the way for groundbreaking discoveries. By embracing AI, they can unlock immense business value and become leaders in this exciting new era.

But without data, there can be no AI. All algorithms and models use data to calculate a solution or generate an answer to a question. In essence, data is the lifeblood of AI models, providing the input they need to function effectively and improve over time.

Responsible AI (RAI) is all about using AI technology ethically, fairly, and responsibly to minimise risks. It involves putting guardrails in place to ensure AI is used for good. Data governance therefore plays a critical role in RAI because the data used to train AI systems is fundamental to their decision-making.

Data access in AI must be governed by strict privacy and security controls. AI models should be granted access only to data that is essential for their operation and for which they have the appropriate authorization.

Preparing high-quality, secure, and private data for AI is therefore paramount. Data quality is critical as it directly influences AI outcomes, while security and privacy are essential to protect sensitive information and comply with regulations.

Although all aspects of data governance are important, these three areas stand out because of their significant impact on the performance and trustworthiness of AI systems. A comprehensive governance strategy that addresses these areas will facilitate the effective and responsible use of AI.

Leaving your data unprotected against AI can be a costly gamble. Hackers can leverage AI to launch targeted attacks, leading to hefty fines, legal battles, and expensive recovery efforts. Even if a breach isn’t malicious, the reputational damage can be crippling.

Exposed data also puts you at risk of identity theft and manipulation by AI systems designed to exploit personal information. Investing in data protection is therefore an investment in your security and peace of mind.

When AI systems access incorrect data in the corporate world, it can set off a cascade of harmful outcomes. The reliability of the AI’s outputs may be compromised, raising questions about its trustworthiness.

For example, biases in the data can skew the AI’s judgments and violate an organisation’s commitment to impartiality and diversity. Such a breach of trust in the AI’s accuracy can have long-lasting negative effects on its acceptance and use.

Legal issues are a major concern, too, as the mishandling of data can result in regulatory breaches and noncompliance with ethical standards. And diverting resources to unproductive AI projects can lead to financial and operational losses.

Strict data governance will mitigate these risks and ensure that AI remains a reliable and efficient business tool. Implementing AI systems can be exciting, but navigating the ethical considerations is crucial. Here are some suggestions to keep you on the right track.

Align your AI goals with your organization’s values. Aim for a positive social impact. Transparency is key: you should be able to explain how your AI makes decisions. This builds trust and allows for human intervention if needed (and it is part of the European Union’s AI Act).

Data governance lays the foundation for RAI. Use diverse, high-quality data that reflects the real world to minimize bias and involve people from various backgrounds in the development process to ensure fair outcomes. Implement robust data-security measures to protect user privacy and comply with data-privacy regulations.

RAI is an ongoing process. Regularly monitor your AI system’s outputs for bias, and refine your data and algorithms as needed. Be open to learning and adapting as AI technology evolves and societal norms change.

Data governance is a key part of the responsible use of AI technologies. So, put some effort into the quality and protection of your data – it’s your most valuable asset, after all – to avoid negative consequences such as compromised AI outputs or legal and financial issues.

Karin Olivier, Principal Transformation Consultant at NTT DATA Middle East and Africa.

PAYE Tax Calculator

Note: The results are not exact but very close to the actual.