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The future of intelligence analysis A task-level view of the impact of artificial intelligence on intel analysis
Nearly 20 percent of workers report experiencing a change in roles, tasks, or ways of working as a result of implementing AI, yet nearly 50 percent of companies have not measured how workers are being impacted by AI implementation.3 This article begins to tackle those questions, offering a tasks-level look at how AI may change work for intel analysts.
Technologies such as unmanned aerial systems, remote sensors, advanced reconnaissance airplanes, the internet, computers, and other systems have supercharged the collection process to such an extent that analysts often have more data than they can process.4 Complicating matters, the data collected often resides in different systems and comes in different mediums, requiring analysts to spend time piecing together related information—or fusing data—before deeper analysis can begin.
The director of the National Geospatial Intelligence Agency said that if trends hold, intelligence organizations could soon need more than 8 million imagery analysts alone, which is more than five times the total number of people with top secret clearances in all of government.5 In the modern digitized age, where success in warfare depends on a nation’s ability to analyze information faster and more accurately than adversaries, data cannot go unanalyzed.6 But given the pace at which humans operate, there simply isn’t enough time to make sense of all the data and perform the other necessary intelligence cycle tasks.
Similarly, the Joint Artificial Intelligence Center (the Department of Defense’s focal point for AI) is already working to develop products across “operations intelligence fusion, joint all-domain command and control, accelerated sensor-to-shooter timelines, autonomous and swarming systems, target development, and operations center workflows.” Our analysis suggests AI operating in these capacities can save analysts’ time and enhance output.
Indeed, research on industries from banking to logistics shows that the greatest benefit of automation comes from when human workers use technology to “move up the value chain.”7 Put another way, they spend more time performing tasks that have greater benefit to the organization and/or customer.
For example, when automation freed supply chain workers from tasks such as measuring stock or filling in order forms, they could redeploy that time to create new value by matching specific customer needs to supplier capabilities.8 For intelligence analysis, leveraging AI to instantly pull otherwise hard-to-spot indications and warning (I&W) leads out of messy data could allow human analysts to do the higher-value work of determining if a given I&W lead represents a valid threat.
A child can carry on a much more sensible conversation about a much wider range of topics than any computer program today, and operate more effectively in an unpredictable physical environment.”9 So while machines are better than humans at handling large volumes of data or working to extreme levels of precision, humans are better at tasks that change dramatically with context or those that involve high levels of interpersonal interaction.
By automating detail-oriented tasks such as writing corporate earnings reports, the AP found that use of bots reduced journalists’ workload by 20 percent, allowing them to focus on reducing errors and spotting larger trends.11 As a result, even as output increased, there were fewer errors in corporate earnings stories.
Using the adoption of other advanced technologies as a guide, we expect many of the new tasks will likely fall into one of these three categories: The fact that AI could require new tasks just to make sure it is operating correctly does highlight a potential danger: AI could eat up more time than it gives to analysts.
While EHR promised to reduce health care professionals’ workloads, recent research has shown that EHR has, in fact, increased the amount of time it takes doctors to document patient visits.16 Doctors using EHR spend more time typing during patient visits, which reduces the amount of face-to-face time they have with patients.
Having an interface that allowed the analyst to easily scan the data underpinning a simulated outcome, for example, or to view a representation of how the model came to its conclusion, would go a long way toward that analyst incorporating the technology as part and parcel of his or her workflow.
For many decades, intelligence leaders have been aware of the phenomenon where adding data to an analyst’s judgments increases the analyst’s confidence that they are right without actually improving the work’s overall accuracy.21 In other words, more data played into analysts’ confirmation bias—they used the new evidence to support their preconceived conclusions instead of helping create more accurate analysis.
The latter could be especially dangerous: Many aviation accidents have shown that mismatch between human trust in automation and human understanding and supervision of itcan lead to tragedies.23 Conversely, there are promising ways in which AI could actually help analysts combat confirmation bias and other human cognitive limitations.
Machines would be very good at continuously conducting key assumptions checks, analyses of competing hypotheses, and quality of information checks.24 Senior analytic managers could also leverage AI to alert them to mismatches between evidence coming in and their teams’ assessments, giving them an opportunity to direct analytic line reviews and focus their attention on problem areas.
With 83 percent of enterprise AI in the cloud, organizations can find it easier to develop AI tools in-house, purchase from external vendors, or even find an existing solution already in use elsewhere in the cloud.26 At a division or team level, the first steps shift from strategic alignment to analyst adoption.
Similarly, AI could help managers evaluate performance and screen job applicants for aptitude in a particular skill or even identify all-around stars, much like Special Operations Command is exploring with Marine Raider applicants.27 The benefit to these nonanalytical uses of AI is that when analysts see AI aid them in their work, rather than competing with them, they would likely become more comfortable working with AI as it moves into more analytical tasks.
Roundtable on "Changing Relationship between Artificial Intelligence and Humans"
The second roundtable on the Ethics of Artificial Intelligence (AI), entitled 'Changing Relationship between Artificial Intelligence and Humans” is part of a series of events organized by UNESCO since 2018, with the financial support of the Ministry of Education, Culture, Sports, Science and Technology of Japan.
This roundtable aims to address the issues of AI assisted decision-making and the impact of AI on human communications in order to raise awareness and sensitize policy-makers, researchers and the general public.
From judicial cases to access to health insurance, medical diagnosis and job applications, examples will be given to explore and illustrate the underlying ethical challenges of data privacy, data biases, equity and justice, among other issues.
Eminent experts in the fields of science, law, neurosciences, engineering, ethics and philosophy will address these questions in a multidisciplinary discussion, under the moderation of Peter-Paul Verbeek (The Netherlands), Professor of Philosophy of Technology at the University of Twente, and Chairperson of UNESCO World Commission on the Ethics of Scientific Knowledge and Technology (COMEST).
- On 6. juli 2020
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