Figure 1 - uploaded by Emre bayamlıoğlu
Content may be subject to copyright.
Data-driven DM concerns

Data-driven DM concerns

Source publication
Preprint
Full-text available
The paper intends to identify certain "rule of law" implications of Big Data analysis from a techno-regulatory perspective-namely, (i) the collapse of the normative enterprise, (ii) the erosion of moral enterprise and (iii) replacing of causative basis with correlative calculations. Although these implications are not completely specific to Big Dat...

Contexts in source publication

Context 1
... this Part, before moving on to the "rule of law implications of data-driven DM", we identify three types of concerns/challenges that are inherent in data-driven practices, namely: informational asymmetries, epistemological flaws, and biases in machine learning (see Figure 1). These mutually reinforcing and inextricably intertwined dynamics/traits/features may be regarded as the root of certain consequences which materialise as unfair, discriminatory results, and invasiveness impinging on privacy-further raising concerns from the point of human autonomy as higher values of the European order since the enlightenment. ...
Context 2
... rough taxonomy in Figure 1 is an attempt to identify certain characteristics of data-driven practices that eventually give rise to harms. Although it is not possible to fully develop each and every item included, the intended "mapping" attempts to systemize and theorize potentially problematic dynamics and properties inherent to data mining-independent of the possible legally addressable harms that they may give rise. ...
Context 3
... seen in Figure 1, the second major source of potential harms regarding automated decisions is the epistemological background. Machine learning is a problem-solving approach which implements algorithmic learning theory as a framework of computational strategies for discovering "truth" in empirical questions. ...
Context 4
... intertwined nature of these dynamics formulated as concerns and challenges -explained by a hybrid terminology compiled from many disciplines and different types of technical writing-makes them equally elusive, shifty, and constantly co-opting, overlapping and thus rendering a clear cut, precise picture almost impossible. 136 Accordingly, it is to be noted that the above construction in Figure 1 is far from being a one- to-one and airtight taxonomy, and it is doubtful whether one can be made. This is due to the fact that the terms borrowed from computer science and statistics-such as, data mining, machine learning, neural, networks, and the like-originate from the practical application of mathematics to specific problems, and inevitably lack the necessary consistency, generality 135 Goran Bolin and Jonas Andersson Schwarz, "Heuristics of the algorithm: Big Data, user interpretation and institutional translation". ...
Context 5
... is due to the fact that the terms borrowed from computer science and statistics-such as, data mining, machine learning, neural, networks, and the like-originate from the practical application of mathematics to specific problems, and inevitably lack the necessary consistency, generality 135 Goran Bolin and Jonas Andersson Schwarz, "Heuristics of the algorithm: Big Data, user interpretation and institutional translation". 136 For instance, although treated separately in Figure 1, "unpredictability" and "uncertainty" are not easy to distinguish in every case. and the rigour to perfectly fit into, or to smoothly interact with legal, sociological or philosophical concepts and narratives. ...

Similar publications

Preprint
Full-text available
Environmentally conscious supplier selection has become increasingly important in recent years. Green supplier selection is one of the vital decisions of supply chain management, as it is preferred for businesses in the market that adopt an environmental approach and green philosophy in line with material and moral benefits. In this context, the pr...