In today’s digital era, where data and artificial intelligence (AI) are paramount, the responsible and effective management of these resources is crucial. Two key concepts that emerge in this context are data governance and AI governance. While they share common ground and are interconnected, they also have distinct characteristics and objectives. Understanding the differences between these two is vital for organizations aiming to leverage data and AI responsibly and ethically. This interesting topic came to us from datanami in their article, “Making the Leap From Data Governance to AI Governance.”

Data governance focuses on establishing rules and processes to ensure that data is treated as a valuable organizational asset. It emphasizes using data effectively to drive business outcomes while mitigating risks related to misuse or mishandling.

AI governance, on the other hand, specifically addresses the ethical, legal and societal implications of AI systems’ development, deployment and use. As AI technologies become more widespread across various sectors, issues such as fairness, transparency, accountability and bias have become increasingly significant.

Despite their distinctions, data governance and AI governance are interconnected and complementary. Organizations must implement robust governance frameworks for both data and AI to utilize these assets effectively while mitigating associated risks and ensuring ethical and responsible use. By doing so, they can unlock the full potential of data and AI technologies to drive innovation, competitiveness and societal benefit while upholding ethical principles and values.

One of the biggest challenges in AI governance is that many organizations lack understanding of how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI addresses this issue by making the processes and outcomes of AI models understandable and trustworthy. It describes an AI model, its expected impact and its potential biases. Explainability is crucial, especially when results can impact data security or safety.

In conclusion, the interplay between data governance and AI governance is essential for organizations to manage their data and AI assets effectively and responsibly. By establishing comprehensive governance frameworks, organizations can ensure they are leveraging data and AI ethically, ultimately driving innovation and societal benefit.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.