In this study, we examine investor and firm response to the California Transparency in Supply Chains Act of 2010. The CTSCA requires large retail and manufacturing firms to disclose efforts to eradicate slavery and human trafficking from their supply chains and is a rare example of mandated corporate social responsibility disclosure. Based on a sample of 105 retail companies subject to the CTSCA, we find a significant negative market reaction to the passing of the CTSCA. Furthermore, we find that the (...) reaction is significantly more negative for larger firms and companies facing greater supply chain risks, suggesting that investors place a negative value on exposure to legitimacy threats in the social domain. With respect to company disclosure response, we document relatively high compliance with the legislation, although we also find that the disclosure response appeared to be more symbolic than substantive in nature. Finally, our analysis indicates that both disclosure choice and disclosure extensiveness were significantly higher for the high-supply chain risk companies, suggesting that the response was influenced by concerns with strategic legitimation. Overall, the limited quality of disclosure suggests that, without additional rules and guidance, mandates alone may not lead to meaningful social disclosure. (shrink)

Decision-makers in governments, enterprises, businesses and agencies or individuals, typically, make decisions according to various regulations, guidelines and policies based on existing records stored in various databases, in particular, relational databases. To assist decision-makers, an expert system, encompasses interactive computer-based systems or subsystems to support the decision-making process. Typically, most expert systems are built on top of transaction systems, databases, and data models and restricted in decision-making to the analysis, processing and presenting data and information, and they do not provide (...) support for the normative layer. This paper will provide a solution to one specific problem that arises from this situation, namely the lack of tool/mechanism to demonstrate how an expert system is well-suited for supporting decision-making activities drawn from existing records and relevant legal requirements aligned existing records stored in various databases.We present a Rule-based reporting systems architecture, which is intended to integrate databases, in particular, relational databases, with a logic-based reasoner and rule engine to assist in decision-making or create reports according to legal norms. We argue that the resulting RuleRS provides an efficient and flexible solution to the problem at hand using defeasible inference. To this end, we have also conducted empirical evaluations of RuleRS performance. (shrink)

SummaryThe nutritional status of under-five children is a sensitive sign of a country's health status as well as economic condition. This study investigated the differential impact of some demographic, socioeconomic, environmental and health-related factors on the nutritional status among under-five children in Bangladesh using Bangladesh Demographic and Health Survey 2007 data. Two-level random intercept binary logistic regression models were used to identify the determinants of under-five malnutrition. The analyses revealed that 16% of the children were severely stunted and 25% were (...) moderately stunted. Among the children under five years of age 3% were severely wasted and 14% were moderately wasted. Furthermore, 11% of the children were severely underweight and 28% were moderately underweight. The main contributing factors for under-five malnutrition were found to be child's age, mother's education, father's education, father's occupation, family wealth index, currently breast-feeding, place of delivery and division. Significant community-level variations were found in the analyses. (shrink)

Purpose Along with the various beneficial uses of artificial intelligence, there are various unsavory concomitants including the inscrutability of AI tools, the fragility of AI models under adversarial settings, the vulnerability of AI models to bias throughout their pipeline, the high planetary cost of running large AI models and the emergence of exploitative surveillance capitalism-based economic logic built on AI technology. This study aims to document these harms of AI technology and study how these technologies and their developers and users (...) can be made more accountable. Design/methodology/approach Due to the nature of the problem, a holistic, multi-pronged approach is required to understand and counter these potential harms. This paper identifies the rationale for urgently focusing on human-centered AI and provide an outlook of promising directions including technical proposals. Findings AI has the potential to benefit the entire society, but there remains an increased risk for vulnerable segments of society. This paper provides a general survey of the various approaches proposed in the literature to make AI technology more accountable. This paper reports that the development of ethical accountable AI design requires the confluence and collaboration of many fields and that lack of diversity is a problem plaguing the state of the art in AI. Originality/value This paper provides a timely synthesis of the various technosocial proposals in the literature spanning technical areas such as interpretable and explainable AI; algorithmic auditability; as well as policy-making challenges and efforts that can operationalize ethical AI and help in making AI accountable. This paper also identifies and shares promising future directions of research. (shrink)

Non-communicable diseases disproportionately affect low and middle-income countries where nearly three quarters of NCD deaths occur. Bangladesh is also in NCD burden. This cross-sectional study was done on 50 health facilities centres at Gazipur district in Bangladesh from July 2015 to December 2015 to introduce SARA for better monitoring and evaluation of non-communicable diseases health service delivery. The General Service readiness index score was 61.52% refers to the fact that about 62% of all the facilities were ready to provide general (...) services like basic amenities, basic equipment, standard precautions for infection prevention, and diagnostic capacity and essential medicines to the patients. But in case of non-communicable diseases, among all the health facilities 40% had chronic respiratory disease and cardiovascular diseases diagnosis/ management and only 32% had availability of diabetes diagnosis/management. Overall readiness score was 52% in chronic respiratory disease, 73% in cardiovascular disease and 70% in diabetes. Therefore, service availability and readiness of the health facilities to provide NCD related health services were not up to the mark for facing future targets. A full-scale census survey of all the facilities of the study area would give a better understanding of the availability and service readiness. (shrink)

Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts. Such interruptions may impact on the work attention not only for the mobile phone owners, but also for the surrounding people. Decision tree is the most popular machine-learning classification technique that is used in existing context-aware mobile intelligent interruption management model to overcome such issues. However, a single-decision tree-based context-aware model may cause over-fitting problem and thus (...) decrease the prediction accuracy of the inferred model. Therefore, in this paper, we propose an ensemble machine-learning-based context-aware mobile telephony model for the purpose of intelligent interruption management by taking into account multi-dimensional contexts and name it “E-MIIM”. The experimental results on individuals’ real-life mobile telephony data sets show that our E-MIIM model is more effective and outperforms existing MIIM model for predicting and managing individual’s mobile telephony interruptions based on their relevant contextual information. (shrink)