How Redaction Supports Safer AI Training Datasets

Summary: AI uses large training datasets comprised of text and other content to learn. The source data for these training datasets could contain sensitive information which can impact the AI output, cause privacy issues, and more. Redacting this sensitive information creates safer datasets and minimizes risk. This blog discusses the importance of protecting sensitive information through secure and permanent redaction before creating training datasets and how the Apryse SDK can help.

Artificial Intelligence (AI) applications and the models behind them rely on large training datasets as a base of information to formulate answers and perform tasks. Datasets, comprised of text and other content, are analyzed by the AI as it looks for relationships and patterns.

In certain regulated industries, smaller or more focused AI models are preferred. These are usually custom-built or fine-tuned for specific tasks, datasets, or industry domains. Regardless of whether these organizations use large, commercial LLMs or develop their own internal, domain-specific ones, the source data used to create the training datasets could contain sensitive information that must be protected. Redacting sensitive information like Personally Identifiable Information (PII) before creating the training datasets is essential to prevent the risk of exposure, biased outcomes, and more.

In this blog, we’ll look at how redaction supports safer AI training data and the benefits of using data extraction and pre-processing capabilities that Apryse SDK offers to automate the process.

So how does redaction fit into creating safer AI datasets? Let’s explore five areas where redaction can help.

Privacy and Regulatory Compliance: Businesses can comply with the strict requirements of data privacy regulations like GDPR, HIPAA, and PIPEDA by removing personal information that can identify an individual.

Data Breaches: Personal information must also be protected from unauthorized access or exposure through a data breach, safeguarding individual privacy and protecting the reputation of the organization.

Insider Threats: Protecting sensitive data using redaction reduces the potential of insider threats where information may be misused by unauthorized users. So, even if security measures fail, sensitive data is still protected.

Ethical AI Development: Relevancy and diversity of the source data can impact the AI’s output. Biases can be introduced if the data is imbalanced, leading to unfair or inaccurate results. For example, a healthcare study having data from mostly male participants. The data should also be diverse and represent real-world scenarios the AI model will encounter to be able to properly recognize relationships and patterns in the data.

Data Sharing: Sharing data with other organizations for research or analysis is common in industries like healthcare and finance. Redacting sensitive information allows other valuable information to be shared without the risk of the sensitive or identifiable information being exposed.

We’ll now explore two highly regulated industries where AI adoption is accelerating, but efforts are often hindered by the challenge of unstructured data spread across complex documents. To apply AI safely and effectively, these industries require clean, compliant data tailored to their specific use cases.

Healthcare: AI in healthcare rely on sensitive documents, including patient records containing Personal Identification Information (PII) and Personal Health Information (PHI). By redacting this information before it’s extracted to create the training dataset allows the now anonymous data to be used safely without the risk of exposure or bias while meeting regulations such as HIPAA.

Finance: Financial institutions using AI to detect fraud or assess credit risk can redact sensitive client details such as names, credit card numbers, and addresses from source data before creating the dataset. This helps ensure regulatory compliance but still allows the AI to recognize patterns and make accurate predictions based on anonymous transaction data.

These are just two of the many use cases that warrant redaction when creating AI training datasets.

Redaction is an essential tool for maintaining data security as it protects information for privacy and compliance purposes by removing or obscuring confidential data and keeping non-sensitive content usable.

In an AI workflow, redaction is one of several pre-processing steps supported by the Apryse SDK to help ensure the extraction of clean, compliant data.

Features and Benefits

We’ve just looked at some of the key factors to consider when preparing source data for AI training datasets and how redaction can help make them safer. Whether it’s to comply with privacy regulations or ensure a non-biased AI output, removing sensitive information using redaction is a must.

With data extraction and pre-processing capabilities that Apryse SDK offers, you can programmatically redact the information, ensuring it is done correctly, securely, and permanently.

Try it out for yourself with our free trial.

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