AI is unFAIR!  And here is what you can do about it…  

April 12, 2024

John Apathy

Companies are searching for clean fuel for AI, and adhering to FAIR data principles is the key for successful implementation of AI. 

Artificial intelligence (AI) has the potential to become a transformative force across many industries, including Health and Life Sciences. From designing new medicines to revolutionizing customer service interactions or optimizing logistics and streamlining operations, AI promises significant advantages for those able to harness its power. However, for these promises to become reality, companies need to understand the crucial role that FAIR data plays in successful AI implementation. 

AI will only be as reliable and trustworthy of the underlying data…and it will be no substitute or shortcut to making an organization’s data FAIR.

FAIR stands for Findable, Accessible, Interoperable, Reusable - and is a set of principles that emphasize the importance of organizing data in a way that allows AI systems to effectively utilize it.

Just like a powerful engine needs high-quality fuel to function optimally, AI systems rely on FAIR data to learn, adapt, and generate valuable insights.

FAIR data is the bedrock of successful AI projects because:

  • The Garbage In, Garbage Out Principle:  AI systems are essentially complex algorithms trained on massive datasets. The quality of the data directly impacts the quality of the AI's output. Imagine feeding a language model poorly written, grammatically incorrect text. The model will likely learn these errors and generate outputs riddled with the same issues.  FAIR data ensures the information used for training is accurate, complete, and consistent. This minimizes biases and errors, leading to more reliable and trustworthy AI models. 

  • Findability and Accessibility: Unlocking the Data's Potential - The very first step in utilizing data for AI is finding the relevant information. Imagine a vast data warehouse with no organization or labeling. It would be incredibly time-consuming, if not impossible, to locate the specific data points needed for an AI project.  FAIR data emphasizes the importance of proper metadata – detailed information about the data itself. This metadata allows for efficient searching and retrieval, ensuring that the right data is readily available for AI projects, saving valuable time and resources.

  • Interoperability: Breaking Down Data Silos - Many companies struggle with data fragmentation, where information is scattered across different departments and systems. This creates data silos, making it difficult to access and integrate data for AI projects.  FAIR data promotes the use of standardized formats and protocols. This allows data from different sources to be seamlessly combined and analyzed, providing AI systems with a more comprehensive picture for learning and generating insights.

  • Reusability: Getting the Most Value from Data Assets - Data collection and preparation are often the most time-consuming and expensive aspects of AI projects. FAIR data principles encourage data reusability. By ensuring data is properly documented, cleaned, and stored, companies can leverage it for multiple AI projects, saving time and effort in future endeavors.

    A high-value example of how FAIR data benefits AI implementation in Life Sciences (something we are working on and a topic for a future blog post!)  

Profiling risk and trust in the efficacy/safety claims, the evidence tied to those claims, and the supporting data that drive decision-making:  Life Sciences companies have the opportunity to speed decision-making by profiling the supporting and contradictory evidence for a given scientific hypothesis and/or claim of efficacy or safety about a new medicinal product.  AI can provide a streamlined approach for connecting and exposing the lineage from clinical claims for efficacy and safety with the underlying evidence and ultimately the source data.  This "evidence lineage" strengthens confidence for decision-making, and ultimately speeding-up the pace of R&D…but requires data to be findable, accessible, and reusable in training models and driving trust in any given claim.  Profiling this lineage will drive increased confidence and the ability to move a drug candidate forward with more speed and confidence. 

Other Industry Examples:
  • Fraud Detection: Financial institutions can leverage FAIR customer data to develop AI models that identify fraudulent transactions with higher accuracy.

  • Predictive Maintenance: Manufacturing companies can utilize FAIR sensor data from machinery to build AI models that predict equipment failures, allowing for preventive maintenance and minimizing downtime.

  • Personalized Recommendations: Retailers can use FAIR customer purchase history data to train AI models that recommend products with greater relevance, leading to increased customer satisfaction and sales.   

Building a FAIR Data Foundation for AI Success: Practical Steps 
  • Understanding the importance of FAIR data is just the first step. Companies need to take concrete actions to create a data environment conducive to successful AI implementation. Here are some key steps to consider: 

  • Data Governance Framework and Community of Practice: Establish clear guidelines and processes for data management, ownership, access, and quality control; act upon data through a community of practice dedicated to building a learning system to continuously improve data quality. 

  • Data Catalog and Metadata Management: Implement a system for cataloging data assets and maintaining detailed metadata to facilitate searchability and understanding. 

  • Data Quality Management: Invest in data cleansing techniques to identify and address errors, inconsistencies, and missing data points.

  • Data Product Architecture through Standardization and Interoperability - Adopt semantic capabilities through standardized data formats and data conformance protocols to streamline data integration from various sources, aligning data to an interoperable “metamodel”.  This ultimately leads to a network effect of connected, interoperable products.

  • Data Security and Privacy: Implement robust security measures to protect sensitive data while ensuring compliance with data privacy regulations. 

Conclusion: The Road to AI Success is Paved with FAIR Data 

Building successful AI solutions requires a strong data foundation. FAIR data principles is the cornerstone of that foundation. By prioritizing data quality, accessibility, and interoperability, companies can unlock the true potential of AI, driving innovation, optimizing operations, and gaining a competitive edge.   As AI continues to evolve, the importance of FAIR data will only increase. Companies that embrace FAIR data principles will be well-positioned to harness the power of AI and achieve tangible business results – becoming the AI-haves (vs. the have-nots). Remember, AI is a powerful tool, but like any tool, its effectiveness depends on the quality of the fuel that powers it. Invest in FAIR data and watch your AI initiatives take flight.  

Want to learn more?  Please contact either John Apathy - or Arturo Morales, Ph.D.