Hallucinations in AI: a data quality problem

In the last year, artificial intelligence (AI) has become a crucial technology, which is influencing various fields of work and revolutionising the way we manage and access information.

Used globally, AI represents a great added value in the lives of many people, but it must be kept in mind that there are also several problems associated with it. One of them is that of so-called 'hallucinations', i.e. the generation of information that seems plausible but is actually false or inaccurate. This is a widespread phenomenon that can have significant consequences, especially when it leads to making decisions based on incorrect data.

What are hallucinations in AI?

Hallucinations in AI occur when a generative model produces responses that seem coherent and believable but are, in fact, completely made-up or distorted. This problem is particularly insidious since the answers often appear to be truthful, making it difficult for users to distinguish between true and false data.

Data quality: a crucial factor

One of the main causes of hallucinations in AI is the quality of the data it offers. Many AI models are trained on large datasets that include information from different sources, some of which may not be accurate or verified. This leads to the generation of unreliable results each time the model attempts to produce answers based on this data.

The API market and data quality

In the context of APIs (Application Programming Interfaces), data quality is of paramount importance. APIs are used to exchange data between different applications and services; if this data is inaccurate or incomplete, it can cause serious problems for companies that rely on this kind of information for their daily operations.

For example, in the area of electronic invoicing, the use of APIs with inaccurate data can lead to errors in invoices, resulting in wasted time and resources to correct errors. In Italy, an invoice discrepancy can lead to penalties if not corrected within a certain time period, causing inconvenience and additional costs to businesses.

How to mitigate AI hallucinations

To address the problem of hallucinations, it is essential to improve the quality of the data used to train AI models. This can be done through:

• Data validation:  it is good practice to use only carefully verified and validated datasets to ensure the accuracy of the information;
• Context Specific: it is important to provide AI models with a specific and detailed context to increase the likelihood that the responses generated contain correct information;
• Continuous Feedback and Improvement: a constant feedback system with users should be implemented to identify and correct any errors in the AI generated data.

Case Study: Openapi and Data Quality

Openapi is the largest API marketplace in Italy and among the largest globally. It offers over 400 services on companies, people, real estate, cars, finance, postal services in a single environment.

With a rigorous approach to data verification and validation, Openapi has significantly reduced the problems associated with hallucinations. For example, the API for Italian and French business information uses official data from sources such as the Italian Chamber of Commerce and Infograph in France. This ensures that the information provided is accurate and reliable.

Furthermore, Openapi has developed structured processes to manage data quality, focusing on five crucial aspects: accuracy, completeness, reliability, relevance and topicality. The continuous cycle of API review and improvement has led the company to obtain quality certifications, distinguishing it from competitors in its market.

Conclusions

Hallucinations in AI represent a significant challenge that requires attention and concrete solutions. Improving the quality of training data and implementing rigorous validation strategies are key steps to tackle this problem. Companies such as Openapi are demonstrating that with a commitment to quality it is possible to provide reliable and accurate AI services while minimising the risks associated with hallucinations.

In fact, investing in data quality not only improves performance, but also increases user confidence in AI-based technologies, paving the way for safer and more effective use of these powerful resources.