Transparency in Data Mining: From Theory to Practice - Discrimination and Privacy in the Information Society - page 301

transparency can bring knowledge from outside the government to improve the
underlying process. Generally, the level of expertise, time and attention available
outside the specific agency (and even government in general) are greater than the
knowledge available within. Therefore, greater exposure of information regarding
the inner works of government to a broader segment of the public will enhance the
chances to receive meaningful feedback. This will again lead to a more effective
outcome for governmental policy.
These arguments are closely linked to another facet of recent scholarship in IT
law. Such scholarship addresses peer-production - the mass participation by
individuals from varied walks of life and different skill sets, in joint projects. As
Yochai Benkler (Benkler, 2007) and others 16 explain, IT and especially the
internet led to the rise of a third collective/industrial force which matches and
even surpasses that of the firm and the market. Transparency can enable these
powerful dynamics, and thus promote governmental objectives and achieve
overall efficiency. In other words, this argument calls for engaging the crowds as a
source for achieving social objectives - or “crowdsourcing.”
The crowdsourcing argument pertains to almost all stages of the prediction
process. Experts and laymen from a variety of disciplines can provided meaningful
insights regarding methods of aggregating data, engaging in data mining analyses,
examining theories of causations and assessing the feedback. Above all, experts can
work through the code of the software operating these schemes, examining its
neutrality, and whether it indeed carries out the tasks it purports to doing. Therefore,
while this theory can apply to all the process's segments, it is usually linked to stage
(b) of the information flow. Here, the disparity between governmental knowledge
and freely-available external expertise is the greatest.
A discussion of crowdsourcing and its feasibility quickly leads to the question
of motivation - why should the crowd indeed act as a source for these activities,
especially when no direct compensation is provided. The incentive structure for
external participation in a voluntary venture to assist government in predictive
modeling is a complex issue. Indeed, some of the motivations transpiring in other
contexts will not play out here. 17 However, several other incentives are extremely
16 A great deal of popular writing has flourished in these fields - such as Crowdsourcing,
Wikinomics. See C LAY S HRIKY , H ERE C OMES E VERYBODY : T HE P OWER OF O RGANIZING
W ITHOUT O RGANIZATIONS (Penguin Group, 2008), D ON T APSCOTT & A NTHONY D.
W ILLIAMS , W IKINOMICS : H OW M ASS C OLLABORATION C HANGES E VERYTHING (Portfolio,
2006); J AMES S UROWIECKI , T HE W ISDOM OF C ROWDS (First Anchor Books Edition,
August 2006).
17 In other contexts (such as open software source and content projects) scholars indicate
that individuals might be motivated by spite (to “get back” at a bad employee or vendor,
and in that way inform the public of their wrongdoings). They are also motivated by an
aspiration to generate a reputation which will promote the individual within a
community or even assist in seeking future employment. Lior J. Strahilevitz, 'How's My
Driving?' for Everyone (and Everything?) , 81 N.Y.U. L. R EV . 1699 (2006, Eric
Raymond, T HE C ATHEDRAL AND THE B AZAAR (1997). For a different perspective, which
plays down the current level of contribution to open source projects which is
altruistically motivated, see Jonathan Barnett, The Host's Dilemma , Harv. L. Rev.
( forthcoming , 2011).