Forensic child sexual abuse evaluations: assessing subjectivity and bias in professional judgements

Objectives

Evaluators examining the same evidence often arrive at substantially different conclusions in forensic assessments of child sexual abuse (CSA). This study attempts to identify and quantify subjective factors that contribute to such disagreements so that interventions can be devised to improve the reliability of case decisions.

Methods

Participants included 1106 professionals in the field of child maltreatment representing a range of professional positions or job titles and years of experience. Each completed the Child Forensic Attitude Scale (CFAS), a 28-item survey assessing 3 forensic attitudes believed to influence professional judgments about CSA allegations: emphasis-on-sensitivity (i.e., a focus on minimizing false negatives or errors of undercalling abuse); emphasis-on-specificity (i.e., a focus on minimizing false positives or errors of overcalling abuse); and skepticism toward child and adolescent reports of CSA. A subset of 605 professionals also participated in 1 of 3 diverse decision exercises to assess the influence of the 3 forensic attitudes on ratings of case credibility.

Results

Exploratory factor analysis identified 4 factors or attitude subscales that corresponded closely with the original CFAS scales: 2 subscales for emphasis-on-sensitivity and 1 each for emphasis-on-specificity and skepticism. Attitude subscale scores differed significantly by sample source (in-state trainings vs. national conferences), gender, years of experience, and professional position, with Child Protective Service workers unexpectedly more concerned about overcalling abuse and more skeptical of child disclosures than other professionals—a pattern of scores associated with an increased probability of disbelieving CSA allegations. The 3 decision exercises offered validation of the attitude subscales as predictors of professional ratings of case credibility, with adjusted R²s for the three exercises ranging from .06 to .24, suggesting highly variable effect sizes.

Conclusions

Evaluator disagreements about CSA allegations can be explained, in part, by individual differences in 3 attitudes related to forensic decision-making: emphasis-on-sensitivity, emphasis-on-specificity, and skepticism toward child reports of abuse. These attitudes operate as predispositions or biases toward viewing CSA allegations as likely true or likely false. Several strategies for curbing the influence of subjective factors are highlighted including self-awareness of personal biases and team approaches to assessment.     

利用胜任力模型提升图书馆中层管理者的执行力

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Populist Twitter Posts in News Stories

Research in information processing suggests that the journalistic practice of embedding right-wing populist Twitter posts in news may stimulate political polarization. To test this notion, we conducted an online experiment (N?=?379). We investigated how highlighted right-wing populist statements affected individuals from different ideological stances and whether potential effects were determined by Twitter-specific characteristics (Twitter frame, profile picture). We exposed participants to two articles, each including a statement by a politician of the Austrian Freedom Party. In the first group, the statements were not highlighted (control group). In the second and third group, the statements appeared as conventional block quotes either without (G2) or with (G3) a picture of the politician. In the fourth and fifth group, the statements were highlighted as Twitter posts, again either without (G4) or with (G5) a picture of the politician. Results revealed that all highlighting conditions increased statement recognition among left-wing individuals. However, the full Twitter post condition exerted the strongest effect. Higher recognition then decreased left-wing individuals’ anti-immigrant attitudes and sympathy toward the right-wing populist candidate. Thus, embedding right-wing populists’ Twitter posts may induce disconfirmation bias among left-wing voters and trigger a process in which they strengthen their initial attitudes.     

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岗位练兵是提升馆员信息素养的有效途径

文摘在对图书馆员信息素养现状的探析中,指出流通馆员应具备的信息素养,进而提出了岗位练兵是提升馆员信息素养的一条行之有效的好途径。     

利用美术课课堂训练如何培养学生思维能力

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信息技术与系统结构整合的创新实践

把信息技术与课程有机地结合,利用现代信息技术培养实用型人才是教育创新课题。适应时代主旋律,采用多媒体技术、CPLD技术和硬件描述语言等多元化教学方法提高计算机系统结构的实践教学,培养学生的创新实践能力,对此进行了论述。     

Algorithmic bias in data-driven innovation in the age of AI

Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.     

高职教师职业能力的结构、观测点、评价方法的研究

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A data quality approach to the identification of discrimination risk in automated decision making systems

Automated decision-making (ADM) systems may affect multiple aspects of our lives. In particular, they can result in systematic discrimination of specific population groups, in violation of the EU Charter of Fundamental Rights. One of the potential causes of discriminative behavior, i.e., unfairness, lies in the quality of the data used to train such ADM systems.Using a data quality measurement approach combined with risk management, both defined in ISO standards, we focus on balance characteristics and we aim to understand how balance indexes (Gini, Simpson, Shannon, Imbalance Ratio) identify discrimination risk in six large datasets containing the classification output of ADM systems.The best result is achieved using the Imbalance Ratio index. Gini and Shannon indexes tend to assume high values and for this reason they have modest results in both aspects: further experimentation with different thresholds is needed.In terms of policies, the risk-based approach is a core element of the EU approach to regulate algorithmic systems: in this context, balance measures can be easily assumed as risk indicators of propagation – or even amplification – of bias in the input data of ADM systems.