Enabling AI Data Readiness in the Department of Defense

AI projects do not succeed without AI data. As the Department of Defense begins to transform itself through AI, creating this “AI ready” data will be a key determinant of success.

Today the DoD Joint Artificial Intelligence Center (JAIC) takes a strong step forward in this transformation journey with the public release of the Data Readiness for AI Data (DRAID) Acquisition Vehicle RFP. The DRAID will comprehensively enable the DoD to take its vast data resources and prepare it for use with AI. Using the DRAID, the DoD will be able to leverage the power of the American enterprise to create the troves of AI ready data that will power the transformation of the DoD through AI.

Using the DRAID, DoD components and Federal partners will be able to access commercial services needed to meet the complex technical challenges involved in preparing data for building AI systems. The services addressed by the DRAID span the entire AI data preparation lifecycle, from data ingestion, through labeling, right up to before model training begins. Through access to these services, the DoD will be positioned to effectively prepare AI data to support the full range of AI activities across the DoD and do so in a responsible manner.

With the release of the DRAID RFP, we want to take a moment to highlight some of the unique aspects of this acquisition vehicle.

Accessibility to Small Business, Startups, and Non-traditionals

Because AI is a rapidly emerging field, innovative and breakthrough technologies are well distributed across the entire commercial landscape. Innovation is just as likely to be found in the newest startups pioneering breakthrough approaches as it is in the largest traditional companies. In developing the DRAID, we have taken effort to ensure that the best providers— regardless if this is their 1st or 101st time interacting with the Federal government—will be able to participate in the RFP process.

To ensure the widest swath of businesses across the commercial spectrum can successfully participate in the RFP process, we have taken a number of key steps.

First, we are releasing an Accessibility Guide for responding to the DRAID RFP. The Accessibility Guide clearly and simply lays out the prerequisite steps businesses need to take to respond to the DRAID. While the guide can be useful for all responding businesses, it is particularly well suited to small businesses, startups, and non-traditional participants for whom responding to the DRAID may be their first interaction with the Federal acquisitions process. If you don’t know your DUNS number from your NAICS code (or even what a DUNS number of NAICS code is!), this guide will quickly walk you through the steps necessary to fully participate in the RFP process.

Second, after receiving almost 400 questions from industry on the DRAID’s previously released Draft RFP, we have leveraged industry knowledge to make concrete and substantial changes to the vehicle itself to ensure participation from across the enterprise. We reformed the experience requirements to allow newer non-traditional vendors—such as startups fostering the latest AI breakthroughs—to be able to compete. We enabled teaming in areas that help selected small and non-traditional vendors to execute on the requirements. Finally, we clearly noted our desire to accept non-government experience in responses in order to ensure companies without prior Federal experience can still participate in the RFP process.

Together, all of these steps signal that wherever innovation and expertise lives throughout the American enterprise, it can find a home in supporting the DoD’s critical AI mission.

Ethics: Front and Center

A cornerstone of the DoD’s AI transformation journey is to develop and field AI systems in a responsible and ethical manner. The DoD AI Ethical Principles, which dictate that DoD AI systems must be responsible, equitable, traceable, reliable, and governable, apply across the entire product lifecycle and for combat and non-combat application. The DoD recognizes that AI Ethics cannot be “bolted on” to an AI system after it is developed. Successfully embodying these principles in our systems requires integrating prompts, tools, and checkpoints to assess ethical risks across the AI product lifecycle, including directly into our technological processes. AI data preparation is a particularly important focal area in this regard.

This philosophy is directly integrated into the DRAID: for an AI system to be responsibly developed, the underlying AI data that is powering that system matters. To work toward our goal of fielding trustworthy and responsible systems, orders executed with the DRAID will explicitly include a task requiring the contractors to demonstrate how their products and solutions address or instantiate the DoD AI Ethical Principles, and/or aid in mitigating ethical risks throughout the AI product lifecycle. Additionally, we have explicitly included tasks to support ethical AI system development, such as providing technologies for identifying bias in data, and mechanisms for data management and data governance.

This point deserves repeating: every AI data preparation order executed with the DRAID will explicitly integrate AI ethics. This illustrates the DoD’s commitment to embedding ethics throughout the entire development process, including within this crucial process of data preparation.

Forward Looking

The services addressed by the DRAID span the full set needed to prepare “AI ready” data, from data ingestion right up to before model training begins. While many of these services are the core tasks in the AI data preparation process—including data ingestion, feature engineering, and labeling—we have shaped the DRAID to also include additional services that will become, and are already becoming, areas of critical interest to the DoD.

These forward-looking areas include topics such as AI security, synthetic data generation, and data representativeness. While many may not think of these areas as “core” AI data preparation steps, as one would of data labeling, these areas are critical to the DoD’s success in setting the standard for world-class AI military systems, including putting the DoD AI Ethical Principles into practice. AI security must be accounted for early in the process to ensure the data used to train AI systems has not been manipulated or poisoned in a way that will compromise AI system performance once the system is fielded. Synthetic data generation provides alternatives to having to collect, prepare, and label significant amounts of data, promising to substantially accelerate the development process. Checking for data representativeness, such as data bias or excluded entities, both serves to instantiate the DoD AI Ethical Principles as well as ensuring optimal system performance once the AI system is in the hands of the warfighter.

The inclusion of these technically-informed, forward-looking areas will help ensure the DoD is leveraging the newest commercial breakthroughs in a responsible manner and is consistently enabling the Department to meet and prepare for its strategic needs of both today and tomorrow.

Conclusion

The quality of the AI data determines the quality of the resulting AI system. With the DRAID, the JAIC seeks to leverage the American commercial enterprise to create a strong foundation of AI ready data for the DoD. We look forward to this partnership and the creation of the critical warfighting AI systems that will depend on its success.