Data Integrity and Artificial Intelligence (AI) in Pharma Industry

Data integrity is the accuracy, completeness, and consistency of data across its lifecycle. It is essential for making informed decisions, ensuring compliance, and preventing errors and fraud. However, data integrity can be compromised by humanerrors, malicious attacks, system failures, or poor data management practices.

Artificial Intelligence Integrity (AII) is a set of practices that aims to eliminate risk throughout the AI lifecycle through data quality, model performance, fairness, security, and transparency.

It is necessary to think about AII at each stage of the AI lifecycle - from data preparation to model development to model operations. Failures in any one area can have critical downstream consequences for your business and customers.

Before AI can contribute to enterprise-level transformation, organizations must first address the problems with the integrity of the data that is driving AI outcomes. Companies need trusted data, not just big data. That’s why any discussion about AI is also a discussion about data integrity.

Companies that adopt AI also adopt AI risk.

AI has become an important tool in an engineer’s toolkit to meet a company’s automation requirements. Yet, despite its widespread use to make business-critical decisions, AI fails frequently and in surprising ways.

While it has become easier to develop and deploy AI systems, there is no standard way to validate them and ensure that they can handle bad inputs, edge cases, and unknowns.

Traditionally, data management has focused on making data accurate and consistent, but that doesn’t go far enough in making data meaningful.

In the context of data quality, it is important to consider data governance. Trust in data comes from being able to prove with total confidence how the data has been prepared, trace the provenance of data to its raw source and provide rights management and the ability to audit.

What are the three primary benefits of AI Integrity?

  • Reduced business risk
  • Accelerated model velocity
  • Saved engineering resources

Resource Person: BARBARA PIROLA

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