The Risks of Relying on AI for Semantic Technologies and Data Quality
Artificial intelligence (AI) is often praised for its potential, but its effectiveness is only as strong as the data it processes. While AI can improve efficiency and automate complex tasks, it still struggles with nuances, errors and biases. Without proper oversight, AI can reinforce misinformation, create unfair decision-making processes, and even degrade data quality over time. Search Engine Land brought this important subject to our attention in their article, “How AI will affect the future of search.”
Semantic technologies attempt to help machines understand data contextually through metadata, taxonomies and ontologies; however, they are far from foolproof. AI does not genuinely comprehend meaning; it identifies patterns. This can lead to false assumptions when the data lacks sufficient context.
Despite AI’s capabilities, poor data quality remains a major obstacle. AI models are only as reliable as the data they consume, and if that data is flawed, the results can be misleading or even harmful. Semantic technologies and AI should not be blindly trusted.
Everyone is looking at AI engines and organizations that produce content are not getting great results. Access Innovations knows information science and we know publishing. We are uniquely positioned to help you in your AI journey.
Melody K. Smith
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
