SD Times has published an article by Yuri Gubin, CIO at DataArt, about how poor data quality undermines AI adoption and why building strong data foundations is critical for reliable, bias-free AI systems.
“Accessibility created a false sense of simplicity. While AI models can handle natural language and unstructured data more easily than previous technologies, they remain fundamentally dependent on data quality for reliable outputs.”
“The classic programming principle “garbage in, garbage out” takes on new urgency with AI systems that can influence real-world decisions. Poor data quality can perpetuate harmful biases and lead to discriminatory outcomes that trigger regulatory scrutiny.“
“Addressing AI data quality requires more human involvement, not less. Organizations need data stewardship frameworks that include subject matter experts who understand not just technical data structures, but business context and implications.”
“Future AI systems will need “data entitlement” capabilities that automatically understand and respect access controls and privacy requirements. This goes beyond current approaches that require manual configuration of data permissions for each AI application.”
Read the full article here.
