## 3 Data-Driven Challenges for Rule-Making and Policy Modelling
### Data-Driven 'Knowledge-Based' Policies  
A great deal has been written about the opportunity for data mining to support government decisions (see e.g., [62; 63; 64; 11; 6]). The idea often stressed is that Big Data could provide a sort of lens allowing phenomena otherwise impossible to detect or forecast by a human observer to surface [65]. The application of machine-learning techniques to large amounts of data, arising from car traffic, crime reports, school assessments, medical records and many other kinds of digital tracks, allow policy-makers to identify unexpected correlations, which can be useful in forecasting future scenarios. However, along with an interest in Big Data opportunities, a trend has also spread of enhancing the self-explanatory capacity of the data [27]. Policy-makers have been encouraged to rely on empirical evidence that turned out to be vague, as they overestimated the meaning of data as well as their ability to provide high-quality knowledge [66]. Forms of opaque data mining contributed, therefore, to discriminatory practices and put democratic principles at risk [66]. The discussion does not put into question the use of Big Data overall. It is likely to become unavoidable in the evolution of policy making to relying on data-driven models to design rules and public policies [64]. However, some of the issues reported above suggest not losing sight of the goals that lead to the use of Big Data in the public context, according to the collective outcomes to be achieved [5]. Relying on incorrect empirical premises can in fact foster the adoption of ineffective or even counterproductive policies. For instance, consider a real data study published in the _American Economic Review_ in 2006 [67]. The work reports the effects of a policy that had increased taxes for smokers in order to lower cigarette consumption and to incentivize a healthier lifestyle. The researchers found out that the policy had produced, along with a minimal reduction in sales, a smokers' propensity to make their experience with cigarettes even more intense and dangerous, spurring them to opt for higher-tar brands or to use cigarettes without the filter [67]. Understanding how Big Data analysis can support policymakers thus requires a reflection on the kind of knowledge arising from it. The development of effective rules and policies entail exploring the dynamics of the phenomenon to be assessed and identifying the elements that can foster a collective behavioural change, by deciding whether a policy will work or not [61]. The analysis of massive social data represents an opportunity against this backdrop, but seems unable to provide such knowledge by itself. Policymakers should take into account this issue, considering whether evidence used to support the policy modelling is able to provide a right knowledge with regard to the problem at hand, namely if it sheds light on the main factors involved in the phenomenon, and on how to interplay of them [68]. In a number of experiences, data-mining tools have been used with a naive approach to support policy decisions, without real understanding of the ways in which they process reality [66]. As pointed out by [PERSON] [66], one of the problems with using Big Data in policy decisions is that code errors are not borne by those that design the algorithms, but by everyone else. On the other hand, even when data are available in large amounts and can be processed by means of innovative techniques, predictions led by data correlations risk missing the chance of explaining the causal dynamics of a phenomenon, increasing the possibility of making rough errors [69]. As argued by [PERSON] and [PERSON] [36], \"[...] when the nature of behavior matters, accurate statistical analyses of social dynamics can maybe reach predictive power but cannot fully explain what is going on\". These issues underline the need to rethink the current approach to Big Data in rule-making and policy modelling.