IMPLEMENTING CROWD-SOURCING

The developments of new information technologies introduce new challenges for innovation diffusion within the US intelligence community. As the amount of information is growing exponentially, the USIC is looking to innovate their means to analyse and collect information. One of these developments is the field of crowd-sourcing. Tapping into a crowd to harvest their wisdom had been under considerable interest of the US intelligence community.

In the previous two sections the internal and external factors identified by the data was discussed. These factors are argued to have a significant effect on the

64 innovation diffusion and implementation within the US intelligence community. Understanding the relationship between these factors and the concept of interest crowd-sourcing is key.

This section discusses the findings concerning the developments of crowd- sourcing in more depth. There have been considerable attempts by the US intelligence community to investigate the usefulness of crowd-sourcing (Atkins, 2014; Atwood, 2015; Drummond, 2010; Halman, 2015; Ryan & Biltgen, 2016; Weinberge, 2014). Furthermore, this part will discuss the overall change factors bound to innovation implementation within the US intelligence community. CROWD-SOURCING

In popular media outlets, such as WIRED and the Guardian, crowd-sourcing within the intelligence community has gained popular interest over the past few years (Weinberge, 2014). The idea of using the information a large group can produce is seen as an interesting future development for the intelligence services. One major factor behind this notion is the success of Wikipedia, which shows that crowd-souring initiatives can be very successful in building online knowledge databanks.

Within the US intelligence community, the notion of crowd-sourced intelligence is currently being researched by the IARPA. The United States intelligence research facility is looking into the possible future applications of crowd-sourced intelligence, but has not yet published a public report on this issue.

The idea of crowd-sourced intelligence finds its origins in the works of Surowieki (2005). His research into the “wisdom of the crowd” has sparked further interest into this contemporary phenomenon. Especially the question how companies are able to implement such practices into their overall business model is studied intensively.

For the intelligence community, the main aim of crowd-sourced intelligence is to improve intelligence estimates: tapping into the wisdom of the crowd is supposed to improve the overall analytic accuracy. In earlier studies, the wisdom of the crowd has proven to be an accurate estimator when dealing with numeric questions (Dickenson, 2013).

Literature suggests the wisdom of the crowd and thus crowd-sourcing can prove a powerful driver for more accurate estimates (Surowiecki, 2005; Munn, 2012). In the field of open-source intelligence, crowd-sourcing can be an innovative analytic method and information collector. It should also be noted that in other

65 fields, such as human- or open-source intelligence, crowd-sourcing can prove to be an important innovator.

This example shows the great possibilities of combining crowd-sourced and open- source intelligence. In this pilot study, both of these sub-disciplines were combined by the amateur group, instead of using clandestine information.

Nowadays, in a field with many estimates circulating online, combining these efforts can prove effective to enhance the accuracy of intelligence estimates. Key to this development is refining the accuracy and effectiveness of crowd-sourcing techniques. To do so, the US intelligence community uses crowd-sourcing and online platforms to calculate new improved estimates for intelligence purposes.

One of the most significant remarks is that the field of crowd-sourced intelligence scored high on more factual intelligence issues. Questions about the price of gold or who will be the King of Saudi-Arabia have only one correct answer. Yet, questions faced by the intelligence community are usually of a more complex nature, with a set of possible answers and scenarios. Navigating these uncertain waters has been identified as one of the key issues concerning crowd-sourced intelligence: there has to be a well-considered balance between factual and political understanding for the intelligence community to function effectively. This was already argued by Olcott (2013, pp. 64-67), who already identified the importance of gaining insights into complex political situations, instead of having only a factual understanding.

During the Soviet period, the US intelligence community invested considerable means to obtain as much information about the Soviet Union as possible (Brzezinski, 2003, p. 275). Despite these efforts, there was too little understanding about the political reality and details concerning key policies of the Soviet Union. Even though they had cutting-edge technology and factual analysis, the USIC lacked some important skills: there was not enough analytical depth to fully understand the politically important signals that could be derived from the available information. To gain substantial situational awareness, these signals need to be combined with factual information. This can offer policy officials satisfactory awareness about the actual situation and their role in the events. Crowd-sourcing techniques allow intelligence community to collect information in a more decentralized manner. It helps intelligence agencies to analyse situations from multiple perspectives, thereby gaining better understanding concerning the issue at hand. Engaging with crowd-dynamics therefore involves

66 more local actors in the collection process, making crowd-sourcing an important addition to the complete toolbox of the US intelligence community.

YOU CAN’T TEACH AN OLD DOG NEW TRICKS

New additions, possibilities or changes to the intelligence toolbox of intelligence services can significantly improve their efforts and estimates. Implementing these innovations could prove a difficult endeavour, as changes were not always receiving a warm welcome within the US Intelligence community. However, since the intelligence landscape keeps changing rapidly, such innovations are necessary to keep the intelligence community ahead.

The first reason why intelligence innovation and implementation is difficult is the political layer underneath it. The US intelligence community works in difficult situations where the stakes are very high. The work of the USIC can have implications on a global scale and can directly influence the political affairs of other countries.

To fulfil these tasks, the US intelligence community needs to produce timely and adequate intelligence products. This helps to support their superiors in their policy decisions. If mistakes are made during this intelligence process, it can have far- reaching consequences for both the United States and other actors.

For this reason, the US intelligence community is under constant supervision and scrutiny. By means of congressional hearings, the US politicians seek to steer the intelligence community in the right directions and uncover possible mistakes and wrongdoings. As a consequence of this practice, the intelligence community has to be very careful in their efforts to produce good intelligence products.

The blame-culture running through the oversight bodies creates the incentive for the intelligence community not to embrace change. It can even impede the implementation of profound changes within the field of the US intelligence services. Since new technologies are more often subject to failure or incorrect use, its puts the USIC in direct danger of being blamed for failure (Tetlock & Mellers, 2011).

The culture of blame-game politics has several implications for individuals within in US intelligence community. They are liable to be blamed for the failure of the USIC and could even be fired for their involvement. This creates an ingrained caution with regard to new uncertainties or innovations among the individuals within the intelligence community.

67 Secondly, organisational change is often viewed as damaging for current operations within the US intelligence community. New directions can have negative implications for the continuation of ongoing operations. Since these operations are internally viewed as critical for national security, operators of these projects will probably resist change. The absence of far-reaching change and innovation will uphold the established organisational culture and habits within the US intelligence community (Gill, Marrin, & Phythian, 2009).

Thirdly, the organisation of the US intelligence community is described as littered with dilemmas and paradoxes. Every path taken by innovation implementation or changes to the organisational internal affairs, creates new trade-offs and weigh- offs to tackle. Betts (1978, p. 3) argues that the USIC is:

“resolving some pathologies with organizational reforms [which] often creates new pathologies or resurrects old ones”.

One example of these newly created pathologies is the constant improvement of collection capabilities of the intelligence community. By collecting more information, the actual processing and analysis of the information subsequently becomes more difficult. Gill, Marrin and Phythian (2009) argue that to understand organisational reform within the intelligence community, one should understand the different trade-offs within the IC. Only by identifying and weighing both external and internal factors, actual actions and motives can be uncovered and understood.

As described before, the work of intelligence agencies is packed with uncertainty and risks, varying from imminent exposure to unmanageable revenge attacks (Gill, Marrin, & Phythian, 2009). Within the intelligence community there are no guarantees to absolute success, although this is sometimes expected by policy officials. To fulfil these expectations, the US intelligence community tries to work with certain methods of collecting and analysing data. Relying on these conventional methods provides continuity of intelligence estimates, which is important for the policy-makers.

New tactics and organisational layouts are perceived to offer even less certainty for the intelligence community. Therefore, intelligence analysts rather work with the devil they know, rather than working with one they don’t know (Tetlock & Mellers, 2011). New technologies might even bring new –maybe unforeseeable- risks and potential problems.

68 These factors make it very difficult to implement innovation within the intelligence community. The combination of the perceived risks and the blame- game culture result in an organisational culture of holding back on change. The US intelligence community has been around for decades but is intrinsically hard to learn it some “new tricks”.

69 CHAPTER 6: