AI in Government: A Study on Explainability of High-Risk AI-Systems in Law Enforcement & Police Service

Law enforcement and police service are, related to the proposed AI Act of the European Commission, part of the high-risk area of artificial intelligence (AI). As such, in the area of digital government and high-risk AI systems exists a particular responsibility for ensuring ethical and social aspects with AI usage. The AI Act also imposes explainability requirements on AI, which could be met by the usage of explainable AI (XAI). The literature has not yet addressed the characteristics of the high-risk area law enforcement and police service in relation to compliance with explainability requirements. We conducted 11 expert interviews and used the grounded theory method to develop a grounded model of the phenomenon AI explainability requirements compliance in the context of law enforcement and police service. We discuss how the model and the results can be useful to authorities, governments, practitioners and researchers alike

Artificial Intelligence Explainability Requirements of the AI Act and Metrics for Measuring Compliance

Explainability in artificial intelligence (AI) is crucial for ensuring transparency, accountability, and risk mitigation, thereby addressing digital responsibility, social, ethical and ecological aspects of information system usage. AI will be regulated in the European Union (EU) through the AI Act. This regulation introduces requirements for explainable AI (XAI). This paper examines which requirements for XAI are regulated and which metrics could be used for measuring compliance. For this purpose, legal texts from the European Parliament and Council were analyzed in order to ascertain XAI requirements. Additionally, XAI taxonomies and metrics were collected. The results reveal, that the AI Act provides abstract regulations for explainability, making it challenging to define specific metrics for achieving explainability. As a solution, we propose a socio-technical metric classification for measuring compliance. Further studies should analyze forthcoming explainability requirements to make AI verifiable and minimize risks arising from AI

TRIM27 Negatively Regulates NOD2 by Ubiquitination and Proteasomal Degradation

NOD2, the nucleotide-binding domain and leucine-rich repeat containing gene family (NLR) member 2 is involved in mediating antimicrobial responses. Dysfunctional NOD2 activity can lead to severe inflammatory disorders, but the regulation of NOD2 is still poorly understood. Recently, proteins of the tripartite motif (TRIM) protein family have emerged as regulators of innate immune responses by acting as E3 ubiquitin ligases. We identified TRIM27 as a new specific binding partner for NOD2. We show that NOD2 physically interacts with TRIM27 via the nucleotide-binding domain, and that NOD2 activation enhances this interaction. Dependent on functional TRIM27, ectopically expressed NOD2 is ubiquitinated with K48-linked ubiquitin chains followed by proteasomal degradation. Accordingly, TRIM27 affects NOD2-mediated pro-inflammatory responses. NOD2 mutations are linked to susceptibility to Crohns disease. We found that TRIM27 expression is increased in Crohns disease patients, underscoring a physiological role of TRIM27 in regulating NOD2 signaling. In HeLa cells, TRIM27 is partially localized in the nucleus. We revealed that ectopically expressed NOD2 can shuttle to the nucleus in a Walker A dependent manner, suggesting that NOD2 and TRIM27 might functionally cooperate in the nucleus. We conclude that TRIM27 negatively regulates NOD2-mediated signaling by degradation of NOD2 and suggest that TRIM27 could be a new target for therapeutic intervention in NOD2-associated diseases.Funding Agencies|German Research Foundation (DFG)|SFB670-NG01|Swedish Society of Medicine||Regional Research Council of South-East Sweden (FORSS)||Swedish Research Council division of Medicine||Gustav V 90th anniversary foundation||Italian Telethon Foundation||DFG|SE 1122/2-1|</p