(1) Background: Cognitive failures, including lapses in attention, memory, and executive functioning, can negatively affect daily performance and well-being. Negative and positive affectivity have been implicated in cognitive functioning, yet their relationship with cognitive failures remains underexplored. This study investigates the impact of positive and negative affect on cognitive failures, using daily diary methods to examine both within-person and between-person associations in a sample of younger adults from Singapore and adults across the lifespan from the United States (US). (2) Methods: Participants (Singapore: N = 253, US: N = 1726) completed daily diaries over seven (Singapore) or eight (US) consecutive days. Multilevel modelling was used to analyse both within- and between-person relationships between affect and cognitive failures, controlling for demographic and socioeconomic variables. (3) Results: In both the Singapore and US samples, negative affect was consistently positively associated with cognitive failures at both levels (SG within-person: β = 0.21, p < 0.001; SG between-person: β = 0.58, p < 0.001; US within-person: β = 0.08, p < 0.001; US between-person: β = 0.28, p < 0.001), supporting the influence of negative affective experiences on cognitive lapses. However, positive affect showed no significant associations with daily cognitive failures in the Singapore sample (within-person: β = 0.01, p = 0.683; between-person: β = −0.04, p = 0.484) and only a between-person negative association in the US sample (within-person: β = 0.02, p = 0.157; between-person: β = −0.11, p < 0.001). (4) Conclusion: These findings suggest that positive and negative affect differentially influence individual differences and intra-individual changes in daily cognitive failures among both younger and older adults.

Artificial intelligence (AI) is becoming increasingly embedded within forensic psychological practice, shaping how criminal risk, legal responsibility and public safety are assessed. AI tools are now used in recidivism prediction, behavioural analysis, deception detection and investigative support, high‐stakes domains where errors can have profound consequences. Despite this rapid adoption, the existing literature remains fragmented, with most reviews confined to narrow subdomains and offering limited integrated synthesis of AI′s broader role in forensic psychology. Thus, this umbrella review addresses this gap by synthesising findings from 43 reviews obtained from five major databases, namely EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus and Web of Science. Across the reviewed literature, our umbrella review suggests that AI is positioned as offering several potential benefits for forensic psychological work, including improved predictive accuracy, real‐time threat monitoring, more optimised resource allocation, enhanced analysis of complex behavioural data and more objective decision‐making. At the same time, the present review highlights persistent pitfalls that may constrain the reliability and defensibility of current applications. These include the reinforcement of bias and disparities, limited transparency and explainability, over‐reliance that may undermine human judgement, data constraints and overfitting, ethical and legal risks and substantial implementation and computational demands that may inhibit real‐world application. Overall, this review underscores the need for rigorous safeguards to ensure that AI enhances rather than compromises forensic decision‐making.