In the digital age, where data and artificial intelligence (AI) reign supreme, ensuring responsible and effective management of these assets is paramount. Two concepts that often come into play in this realm are data governance and AI governance. While they share similarities and are interconnected, they also possess distinct characteristics and purposes. Understanding the differences between these two is essential for organizations looking to harness the power of 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 is about establishing rules and processes to ensure that data is treated as a valuable organizational asset and is used effectively to drive business outcomes while mitigating risks associated with its misuse or mishandling.

AI governance, on the other hand, deals specifically with the ethical, legal and societal implications of AI systems’ development, deployment and use. As AI technologies become more pervasive in various sectors, concerns surrounding fairness, transparency, accountability and bias have come to the forefront.

While data governance and AI governance are distinct concepts, they are interconnected and complementary in nature. Organizations must establish robust governance frameworks for both data and AI to leverage these assets effectively while mitigating associated risks and ensuring ethical and responsible use. By doing so, organizations can unlock the full potential of data and AI technologies to drive innovation, competitiveness and societal benefit while upholding ethical principles and values.

The biggest challenge to AI governance is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

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.