Differences in Psychopathy and Associated Traits by Police Officer Rank

Most psychopathy research focuses on its manifestation in forensic populations, however these results may not generalize onto noncriminal, or “successful,” psychopaths. Lykken (1995) conjectured that socialization may enable “heroes,” like law enforcement, to utilize the interpersonal and affective aspects of psychopathy in a manner that benefits society. Previous research (Falkenbach et al., 2018a) suggests that psychopathy and its correlates differ between police recruits and individuals in the community. It is necessary to continue this work with other groups in the police force to see if the patterns found in these studies generalize to veteran officers who have worked in law enforcement for longer periods of time. The objective of the present study was to gain a broader understanding of how police rank relates to personality. Self-report measures were used to see how different traits, such as aggression, behavioral inhibition/activation, empathy, narcissism, affect, and anxiety, related to factors of psychopathy and how they differed between police ranks. Self-report measures were administered to 1459 police officers, including recruits, officers, sergeants, lieutenants, detectives, and executives. The results indicate that the nomological net of Coldheartedness in the current sample is consistent with previous studies (Berg et al., 2015), and that police recruits have higher Self-Centered Impulsivity and lower Fearless Dominance scores than higher ranks. By furthering the research on psychopathy in noncriminal and pro-social populations, a more nuanced depiction of it can be developed. This will help with assessment and treatment of noncriminal psychopathy, and assist departments with better accounting for individual capabilities in job assignments

Investigative Pattern Detection Framework for Counterterrorism

Law-enforcement investigations aimed at preventing attacks by violent extremists have become increasingly important for public safety. The problem is exacerbated by the massive data volumes that need to be scanned to identify complex behaviors of extremists and groups. Automated tools are required to extract information to respond queries from analysts, continually scan new information, integrate them with past events, and then alert about emerging threats. We address challenges in investigative pattern detection and develop an Investigative Pattern Detection Framework for Counterterrorism (INSPECT). The framework integrates numerous computing tools that include machine learning techniques to identify behavioral indicators and graph pattern matching techniques to detect risk profiles/groups. INSPECT also automates multiple tasks for large-scale mining of detailed forensic biographies, forming knowledge networks, and querying for behavioral indicators and radicalization trajectories. INSPECT targets human-in-the-loop mode of investigative search and has been validated and evaluated using an evolving dataset on domestic jihadism.Comment: 9 pages, 4 figure