Data Governance, Large Language Models and a Quest to Avoid Bias and Inaccuracy

Today, many industries manage large amounts of data and, as their requirements grow, the technology that supports them seeks to keep pace. Large language models (LLMs) such as ChatGPT step up to the challenge by providing a user-friendly way for businesses to interact with their data at the speed of business to automate processes, provide valuable data insights and create customized content based on their data. LLMs are a form of artificial intelligence (AI) trained on massive sets of text-based data to perform various tasks ranging from detecting patterns to crafting original content.

Adopting LLMs has been facilitated by modern cloud technologies and data frameworks. In fields such as healthcare, LLMs are proving to be incredibly useful. They help enhance patient outcomes through data labeling and coding, data recovery and patient communication. Organizations across industries can improve nearly every area of their business using LLMs for tasks such as finding patterns in what products customers are most/least interested in, crafting tailored messages and predicting client behavior.

Excitement about the potential of this technology is high, but it’s met with equal measures of concern. Across industries, a consensus exists that data is a precious asset organizations should handle with care. As a result, worries about patient or customer privacy, inaccurate data, biases and leaks of sensitive information are at the center of the conversation.

Data governance plays a crucial role in addressing these challenges. By establishing clear rules, processes and responsibilities for managing data, industries can ensure that the large volumes of data they deal with are appropriately secured and utilized.

People are increasingly aware of the dangers of identity theft, data leaks and data breaches. At home, in offices and in large organizations, we have mastered the paper shredder and the identity theft stamp roller to hide sensitive information such as bank information and personal identification numbers. When handling vast quantities of sensitive and virtual data, organizations rely on solutions such as data tokenization to help keep them compliant with safety regulations in their industries while benefiting from the insights provided by LLMs.

Data tokenization is a process that swaps out sensitive information with a randomly generated data string called a token. This token holds no mathematical connection to the original data it replaced; instead, it functions as a reference for that data. Because of this, hackers cannot reverse it without access to the original tokenization system's mapping, rendering these random and meaningless tokens useless to potential hackers.

This tactic is an essential part of data governance practices that help promote the secure use of LLMs and is part of a greater comprehensive strategy crucial for creating more fair and unbiased language models.

Masking and obfuscation are also data governance practices in the LLM training pipeline that contribute to responsible AI development, ethical data usage and compliance with data protection regulations.

Masking and obfuscation are similar to data tokenization. They allow organizations to train LLMs without risking the exposure of private data. This process is essential in healthcare, finance and legal industries where patient or customer confidentiality is critical. Masking involves replacing portions of data with placeholders that render the information unreadable, while obfuscation intentionally substitutes similar data in place of the original.

Data masking and obfuscation can help prevent bias in training data by altering specific data points. For instance, masking or obfuscating sensitive demographic information prevents language models from learning and perpetuating biases related to gender, ethnicity or other protected characteristics.

They can also help support safe collaboration by allowing organizations to share insights within their companies and with multiple stakeholders without revealing sensitive information. In scenarios where LLM-generated content is made public, such as when a healthcare organization uses an LLM to create patient communication materials, masking can ensure that patient information isn't exposed.

Customers rely on organizations to provide high-quality service, and bad data can directly affect their confidence in a business. Data experts can help ensure data quality by evaluating data and working to unlock its full potential. Data quality, a component of data governance, is essential when training LLMs that are intended to revolutionize decision-making in your organization, where accuracy and reliability are important.

Data tagging is the process of assigning labels to data. These labels provide context, categorization and other information that help AI, machine learning models, and software solutions understand, learn and identify patterns in data. A robust data governance program necessitates meticulous data tagging, which serves as a cornerstone of broader AI strategy.

With a basic understanding of how language models undergo training and how data experts work to minimize bias, we can stop being wary of these technological advancements and instead find ways to improve how we work. Organizations have always been responsible for their data, and this hasn't changed with the introduction of LLMs. The only sensible way forward is establishing data governance practices within our organizations that can support innovative technologies while maintaining compliance with safety standards and regulations. At CDW, our team of Data and LLM experts are committed to assisting organizations of all sizes in preparing their data to train trustworthy large language models. With data governance at the heart of our approach, we are committed to helping organizations extract and maximize the incalculable value of their data.

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