How can AI regulation be effectively enforced? : comparing compliance mechanisms for AI regulation with a multiple-criteria decision analysis

Award date: 17 June 2022. Supervisor: Professor Andrea Renda (European University Institute)Newly emerging AI regulations need effective and innovative enforcement and compliance mechanisms to assure that fundamental and human rights are protected when using an AI system. This study compares four different compliance mechanisms namely ‘Real-Time and Automated Conformity Assessment’, ‘Standardization and Certification’, ‘Algorithmic Impact Assessment’ and ‘Algorithmic Auditing’ as well as three different assurers of compliance namely deployers, notified bodies and civil society organisations. With an MCDA, this research has shown that civil society-based compliance mechanisms are believed to be less effective, less feasible and more costly compared to all other compliance mechanisms. Second, external compliance mechanisms (by notified bodies) were rated to be more effective but also more difficult to implement compared to internal compliance mechanisms. Third, algorithmic auditing scored highest among all policy options. Fourth, despite its experimental nature, automated and real-time compliance mechanisms are not scored significantly lower than other compliance mechanisms

Neural correlates of cue‐induced changes in decision‐making distinguish subjects with gambling disorder from healthy controls

In addiction, there are few human studies on the neural basis of cue-induced changes in value-based decision making (Pavlovian-to-instrumental transfer, PIT). It is especially unclear whether neural alterations related to PIT are due to the physiological effects of substance abuse or rather related to learning processes and/or other etiological factors related to addiction. We have thus investigated whether neural activation patterns during a PIT task help to distinguish subjects with gambling disorder (GD), a nonsubstance-based addiction, from healthy controls (HCs). Thirty GD and 30 HC subjects completed an affective decision-making task in a functional magnetic resonance imaging (fMRI) scanner. Gambling-associated and other emotional cues were shown in the background during the task. Data collection and feature modeling focused on a network of nucleus accumbens (NAcc), amygdala, and orbitofrontal cortex (OFC) (derived from PIT and substance use disorder [SUD] studies). We built and tested a linear classifier based on these multivariate neural PIT signatures. GD subjects showed stronger PIT than HC subjects. Classification based on neural PIT signatures yielded a significant area under the receiver operating curve (AUC-ROC) (0.70,p= 0.013). GD subjects showed stronger PIT-related functional connectivity between NAcc and amygdala elicited by gambling cues, as well as between amygdala and OFC elicited by negative and positive cues. HC and GD subjects were thus distinguishable by PIT-related neural signatures including amygdala-NAcc-OFC functional connectivity. Neural PIT alterations in addictive disorders might not depend on the physiological effect of a substance of abuse but on related learning processes or even innate neural traits