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Get the article on our blog: What you need to know about your data before you launch your AI project.

Set Standards for Data Quality

Unless you can validate the quality of data intended for use in your AI model, you’re putting your organization at risk. You must be able to recognize and verify data creators, consumers, provenance, movement, and purpose of the binaries in your data set. Is it genuine data or synthetic? is it legally protected? Is it secure or has it been compromised elsewhere? Data that doesn’t meet your organization’s standard for quality and suitability for the AI purpose will poison your pond. Data tainting is also becoming a huge cyber risk as AI become used for more purposes. Data surveillance will be an essential defensive tool to ensure high data quality standards and baseline data provenance and usage.

Setting Standards for Data

When it comes to building an AI model, you can’t just put any data in it. Yet, in many AI projects, data is being scraped from the internet, gathered from external sources, and added from internal systems. The problem is that nobody knows exactly where the data originated. They don’t know who created it or why it was created. They don’t even know if it’s true. Even an established data set that is believed to be original and correct still contains numerous artifacts that can’t be explained. All of these factors matter much more with AI because they can influence—for better or worse, and usually worse—the quality and predictability of the results you want.

Before investing millions of dollars and resources into an AI initiative, organizations need to define and enforce data standards for AI usage. You also need granular visibility into every piece of data in order to enforce the standard. Now you can know where data is coming from and who adds it to the dataset. You’ll know when, where, by whom, and for what purpose specific data was created. You can track changes to the data over its lifecycle. You can analyze sources and originators to ensure that the data you’re including is not owned by someone else, is accurate, and verifiable. CrowsNest enables you to have binary-level visibility of data intended for AI, set policies for it, and enforce standards to control the quality of your dataset and improve the predictability of AI outcomes.

Identifying Synthetic Data

Today’s AI models are being trained on increasing amounts of AI-synthesized data because it’s often faster, cheaper, and easier to use for a variety of applications. However, repeating the use of synthetic data in succeeding generations of models creates different types of self-consuming loops that trade off quality (precision) and diversity (types of results). Generative AI models based only or on a majority of synthesized data will degrade over time. Without real, correct data, the AI program will actually lose its mind.

How do you know if the data you’re using is synthesized or real? CrowsNest synthetic data detection algorithms identify and alert on data anomalies characteristic of synthetic data—so you can decide whether to use it in your dataset. When you set a standard for how much synthetic vs. fresh data is allowed in your dataset, CrowsNest will continuously enforce it, enabling you to balance cost and speed versus acceptable outcomes for your AI model.