Using Data’s Potential

Artificial intelligence (AI) applications depend on vast amounts of data to function effectively, but what happens when that data is of low quality? The consequences can be severe, leading to biased outcomes, inaccurate predictions, security vulnerabilities and operational inefficiencies. AiThority brought this topic to our attention in their article, “AI-Driven Product Insights: Tackling Data Inconsistencies and Gaps.”

AI models learn from the data they are trained on. If this data is biased, AI systems will produce biased results, reinforcing existing disparities. Low-quality data—whether incomplete, outdated, or incorrect—leads to poor decision-making by AI models.

AI systems relying on low-quality data are more susceptible to adversarial attacks. Malicious actors can exploit weak data integrity to manipulate AI behavior, leading to security breaches, misinformation propagation and fraudulent activities.

High-quality data is the backbone of reliable AI applications. Without it, AI-driven solutions can lead to biased, inaccurate, insecure and inefficient outcomes. By prioritizing data quality, organizations can unlock AI’s full potential while avoiding its most dangerous pitfalls.

The biggest challenge 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 Data Harmony, harmonizing knowledge for a better search experience.