•  
  •  
 
Cornell Journal of Law and Public Policy

Keywords

Behavioral Ethics, Law Enforcement, Big Data, Bounded ethicality

Abstract

Wrongdoing is ubiquitous. Media outlets constantly report an endless stream of deleterious human behavior, from sexual harassment and fraud in financial markets to corporate and political corruption. Recent developments in behavioral ethics research suggest that these ills will forever accompany human interaction due to the phenomenon of "bounded ethicality," or people's limited ability to conduct an objective and candid moral examination of their own actions. When evaluating the ethical implications of their behavior, individuals have been shown to be biased and to systematically underestimate or ignore the magnitude and effect of their own misconduct. Such findings have troubling implications from a law enforcement perspective. That is, if wrongdoers are able to convince themselves they are doing nothing wrong, how can regulators and policy makers ever successfully reduce or prevent misconduct? Essentially, recognizing the power of bounded ethicality reinforces the idea that destructive human behavior may be unavoidable, and that it may never be possible to reduce the systematic wrongdoing currently observed throughout society.

In response to the challenges that bounded ethicality poses, this Article explores how using big data analytics contributes to curbing both ethical bias and the results of bounded ethicality. The Article is breaking new ground in being the first to explore the intersections between the growing literature on behavioral ethics, highlighting the concept of bounded ethicality, and the scholarship and research on data-driven law enforcement.

Download

Share

COinS