Document AI combines machine learning disciplines to unlock information locked in documents, sift through a vast library of data and provide meaningful insights. This technology can automate time-intensive, repetitive tasks and free up humans to focus on more valuable work.

However, despite the enormous potential of AI, it faces challenges when it comes to accountability due to its black-box character. This is especially true in regulated use cases, where software needs to be certified or validated before deployment.

What is Documentation?

Documentation is a key aspect of software engineering that serves several purposes. It documents the decisions that are made during the development and maintenance of a software application. This documentation helps ensure that software is well-designed and complies with the requirements of the system’s users (Clements et al., 2011).

As AI and machine learning have become a staple in businesses’ everyday operations, they are also increasingly used for document understanding. This process captures text and paper data from emails, PDFs, scanned documents, and more to extract valuable insights.

What is Documentation for AI?

Documentation is the process of capturing, organizing, and delivering information from a digital source. It’s a critical aspect of business and operations, and utilizing artificial intelligence (AI) for document processing helps organizations automate repetitive tasks and streamline workflows for efficiency and profitability.

AI technology for document automation is used to pull out crucial pieces of information from a digital source, freeing up employees to focus on more value-added work. This is especially important for organizations that rely on forms and documents to capture key data or manage operational processes, such as law firms, insurance underwriters, and enterprises that process a high volume of invoices, contracts, vendor agreements, and other documents.

However, as with any new technology, a degree of trust and transparency around the process of developing AI is needed to ensure that it is used responsibly. In particular, the training data that ML algorithms use to train their models is a major source of risk.

Documentation for AI Applications

Documentation is a key component for AI applications. It enables auditors and regulators to inspect how human influence impacts the business processes. It also helps AI developers to understand how to make their applications more robust.

Moreover, it ensures that new employees can learn how to use the AI quickly and effectively without being distracted from their work. The documentation approach presented here is very hands-on and includes extensive descriptions of the development process and the performance tests of the AI model.

With Document AI, enterprises can automate their processes and reduce human intervention in a variety of critical business applications. Document AI solutions are designed to digitize documents, analyze the data extracted from them, and provide a structured understanding of the document contents for consumption. They also support human-in-the-loop (HITL) verification and corrections of the extracted data. By eliminating manual sifting through contracts, invoices, vendor agreements and other documents, businesses can improve productivity and streamline workflows in the long run.

Documentation for AI Use Cases

Documentation is essential for AI use cases because it helps developers and their QA and compliance counterparts follow best practices, policies, and regulations. This documentation can also help them detect and mitigate risks early, before they cause financial or reputational harm.

To meet this goal, vendors should include documentation tools that are simple and transparent. These tools provide clear understanding of the steps and models involved in AI decisions, allowing teams to discover where an answer came from and why it didn’t choose a different one.

This approach also ensures that AI systems behave ethically, as it can be used to prove to an auditor that potentially discriminatory patterns in data and the decision-making process have been taken care of. This documentation is a crucial component of AI risk management (MRM) in industries that operate under strict regulatory requirements and ethical standards.