The fairness of Artificial Intelligence (AI) has gained tremendous attention within the criminal justice system in recent years, mainly when predicting the risk of recidivism. The primary reason is attributed to evidence of bias towards demographic groups when deploying these AI systems. Many proposed fairness-improving techniques applied at each of the three phases of the fairness pipelines, pre-processing, in-processing and post-processing phases, are often ineffective in mitigating the bias and attaining high predictive accuracy. This paper proposes a novel approach by integrating existing fairness-improving techniques: Reweighing, Adversarial Learning, Disparate Impact Remover, Exponential Gradient Reduction, Reject Option-based Classification, and Equalized Odds optimization across the three fairness pipelines simultaneously. We evaluate the effect of combining these fairness-improving techniques on enhancing fairness and attaining accuracy. In addition, this study uses multi- and bi-objective optimization techniques to provide and to make well-informed decisions when predicting the risk of recidivism. Our analysis found that one of the most effective combinations (i.e., disparate impact remover, adversarial learning, and equalized odds optimization) demonstrates a substantial enhancement and balances achievement in fairness through various metrics without a notable compromise in accuracy.

Artificial Intelligence (AI) is applied in almost every public sector because of its positive impacts. However, AI’s ethical aspects and trustworthiness constitute a significant uproar and concern among different AI stakeholders due to AI’s adverse effect on users when the AI system lacks cautionary measures. AI is used in the criminal justice system for predicting recidivism risk. However, AI’s negative impact translates into bias and high incarceration towards a group of defendants in a population assessed for recidivism risk. This paper focuses on fairness as a requirement of a trustworthy AI framework previously proposed to ascertain the appropriate application of AI systems in predicting recidivism. This paper aims to raise awareness about the fairness of AI models and stimulate further research and deployment of efficient and effective exploitation of fair and trustworthy AI models in the criminal justice system when predicting recidivism. Fairness has been a significant concern for criminal justice system stakeholders and has received considerable attention with more theoretical and practical studies than other trustworthy AI requirements. Hence, this paper reviews state-of-the-art fairness, outlines valuable findings, and proposes future directions to achieve fair AI systems for predicting recidivism risk. In addition, this paper ensures mapping existing technical works in the literature to the fairness pipeline corresponding to the criminal justice system’s AI development phases.

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide with major concerns around privacy. COVID Tracker app used in the Republic of Ireland was praised in general for the way it addressed privacy and was used as baseline for other contact tracing apps worldwide. The success of the app is dependent on the general public uptake, hence their voice and attitude is the one that really matters. This paper focuses on developing a survey and the methods aiming to examine the attitudes toward privacy during COVID-19 of the general public in the Republic of Ireland and their impact on the uptake of the COVID tracker app. Various privacy models are used and health belief model as well in this purpose. A pilot study with 286 participants show a change in attitude towards privacy during COVID-19 pandemic, with more people willing to share their data in the interest of saving lives. However, privacy attitudes are shown to have impacted the adoption of the app in Ireland.

The MIDAS consortium is a partnership involving health authorities, and technical big data experts from universities, research institutions, MNCs and SMEs across six EU countries (UK (NI and England), Ireland, Belgium, Finland, Spain and Slovenia), and the USA. The management of big data for 'health in all' poses a significant challenge for health policy makers. This challenge is addressed by the MIDAS project through the development of an integrated solution enabling knowledge liberation from data silos and unification of heterogeneous big data sources that can provide evidence-based actionable information and transform the way care is provided.

This paper applies the technology acceptance model to explore the digital divide and transformational government (t-government) in the United States. Successful t-government is predicated on citizen adoption and usage of e-government services. The contribution of this research is to enhance our understanding of the factors associated with the usage of e-government services among members of a community on the unfortunate side of the divide. A questionnaire was administered to members, of a techno-disadvantaged public housing community and neighboring households, who partook in training or used the community computer lab. The results indicate that perceived access barriers and perceived ease of use (PEOU) are significantly associated with usage, while perceived usefulness (PU) is not. Among the demographic characteristics, educational level, employment status, and household income all have a significant impact on access barriers and employment is significantly associated with PEOU. Finally, PEOU is significantly related to PU. Overall, the results emphasize that t-government cannot cross the digital divide without accompanying employment programs and programs that enhance citizens’ ease in using such services.

This paper describes an ongoing study into the quality of service provided by the Irish Revenue Commisioners' on-line tax filing and collection system. The Irish Revenue On-Line Service (ROS) site has won several awards. In this study, a version of the widely used SERVQUAL measuring instrument, adapted for use with on-line services, has been modified for the specific case of ROS. The theory behind this instrument is set out, the particular problems of evaluating revenue collecting on-line are examined and the rationale for this approach is explained.

Online taxation systems have been among the most successful of e-government applications both in terms of citizen take-up and savings to the taxpayer. Understanding the factors that lead to high take-up is of potential interest to other public sector online service providers. This paper examines the quality of the online service provided by the Irish Revenue Commissioners’ tax filing and collection system, Revenue Online Service (ROS). A modified version of the recently operationalized E-S-QUAL instrument is used to examine online service quality from the point of view of the citizens and tax practitioners who use this eGovernment system. The findings show that efficiency and ease of completion are the dimensions of website service quality that most influence ROS users’ perceptions of value and convenience as well as their intentions to use and recommend the website to their peers. The practical implications of these findings include the fact that providers of public sector e-services should concentrate on communicating the functionality of their e-services. In addition, they should focus on reducing citizen concerns regarding misuse or mismanagement of personal data.