[co-author: Danielle Duff]

Crowdsourced data providers are revolutionizing data collection, analysis, and decision-making, and play a pivotal role in gathering information that can be used to inform business and government intelligence requirements to support decision making. In stark contrast to traditional Human Intelligence (HUMINT) practices, these commercial platforms offer unique and powerful methods of data and information aggregation that provides accurate and reliable local insights in the least intrusive way possible without the collection risks of interpersonal human tasking.

The critical differences between crowdsourced data collection and conventional HUMINT methodologies underscore the advantages of the former with respect to risk mitigation. While HUMINT relies on individual relationships, personalized instructions, and direct human-to-human interaction, crowdsourced data platforms thrive on a collective approach that removes the need for specific human sources and personalized tasking. This departure marks a significant paradigm shift in intelligence collection, one that emphasizes the power of the crowd and its ability to provide diverse, accurate, and reliable information.

HUMINT is broadly defined as intelligence derived from human sources. An overly broad application of an already broad definition would render almost all information HUMINT. For example, an article about a speech attended by a journalist, which is written by a human, based on information derived from a human, is not HUMINT. The characterization of the source is key. The source here is public, therefore; such publicly available news media is considered open-source intelligence rather than HUMINT.

The Office of the Director of National Intelligence has helpfully provided a more nuanced explanation to supplement the general definition of HUMINT, stating that HUMINT “is the only type of intelligence for which collectors speak directly to the sources of information, control the topic of discussion, and direct the source’s activities.”

This is not how crowdsourced data works. Crowdsourced data providers engage crowds via an indirect, aggregated platform. This collective approach highlights a fundamental departure from HUMINT, removing the individual human relationships and specific direction and tasking of individual human sources that are central to HUMINT collection. Crowdsourced data platforms avoid personalized instructions intended for a known individual. Customers instead provide interest areas and requirements, fostering an organic and responsive collection of information from a crowd. The anonymity of contributors, combined with the aggregation of information from diverse sources, results in less risk than the development and maintenance of interpersonal relationships foundational to traditional HUMINT collection.

A fundamental marker of good intelligence is diverse intelligence. Multiple sources and collection methods increase accuracy and reliability. There will be some government intelligence requirements that will likely continue to be better met by HUMINT collection, wherein interpersonal relationships with specific individuals specially placed with access no one else has (or very few have). But to understand where a point of interest is in a location remote to the customer, the information does not need to come from one particular individual. In fact, it is more accurate and reliable to glean this information from the crowd.

A fundamental marker of good intelligence is diverse intelligence.

Looking forward, as Artificial Intelligence (AI) continues to be integrated into crowdsourced collection platforms, crowdsourced information will be further distinguishable from HUMINT. AI advancements promise even greater insights and advantages for decision-makers across various domains, importantly, with less direct human to human direction. Customers will be able to set parameters and initiate changes in requirements to occur on an automatic basis in response to collection further removing human-to-human direction. Supported by AI platforms that facilitate universal data collaboration across diverse sources and software systems, crowdsourced data becomes an even more dynamic and powerful tool, enhancing its utility in all intelligence disciplines.

Commercial crowdsourced data companies exemplify how these platforms drive business growth and innovation by providing accurate and timely insights while reducing risk and the oversight requirements that come with managing the risks of developing, maintaining, and protecting direct human sources. The power of the crowd reduces these risks by aggregation and openness.

In conclusion, the convergence of aggregated human collaboration, technology, and data holds the potential to redefine our understanding and responses to complex challenges. The continued evolution of crowdsourced data platforms promises a future where information gathering is not just efficient but also ethically responsible and dynamically responsive to the ever-changing demands of decision making in the modern world.

In-house counsel at data driven technology companies need to be aware of the pitfalls of government mischaracterization of their products and services that could lead to termination or modification of previously authorized government acquisition. This article provides clarification of one such mischaracterization that continues to persist despite decades of use of crowdsourced data by the government.