Search results

Along with the various beneficial uses of artificial intelligence (AI), there are various unsavory concomitants including the inscrutability of AI tools (and the opaqueness of their mechanisms), 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.

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.

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 (ethical, philosophical, legal, political and technical) and that lack of diversity is a problem plaguing the state of the art in AI.

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.

Fully autonomous self-driving cars not only hold the potential for significant economic and environmental advantages but also introduce complex ethical dilemmas. One of the highly debated issues, known as the “trolley problems,” revolves around determining the appropriate actions for a self-driving car when faced with an unavoidable crash. Currently, the discourse on autonomous vehicle (AV) crash algorithms is primarily shaped by Western ethical traditions, resulting in a Eurocentric bias due to the dominant economic and political influence of the West. However, considering that AV technology will be deployed across diverse cultural and religious contexts, this paper aims to contribute to the discourse by providing an Islamic perspective on programming the response of AVs in the event of an imminent crash.

This study proposes a novel methodology based on the Islamic concept of maṣlaḥa for the normative assessment of ethical decisions related to AV programming.

Drawing upon the works of classic Islamic jurists, this study highlights two distinct normative visions within Islamic traditions (akin to deontology and consequentialism) concerning the preservation of human lives in the context of AVs. This study explores the shared and divergent elements between Islamic and Western ethical approaches proposed for AVs.

This pioneering work examines AV crash algorithms from an Islamic perspective, filling a void in the global ethical discourse. This work will also serve an important role to bridge the gap between the theoretical Islamic ethical principles and their practical application in the realm of AVs.

This article aims to explore the advantages of integrating a new generative artificial intelligence (AI) technology in education. It investigates the use of ChatGPT in personalized learning, assessment and content creation and examines ways to manage its limitations and some ethical considerations. The purpose is to stimulate discussion on the effective application of ChatGPT as a tool for learning and skill development while remaining mindful of the ethical issues involved.

The methodology in this article includes four steps: a literature search, screening and selection, analysis and synthesis. The literature was thoroughly screened and selected on the basis of its relevance to the research question, before selected material were carefully read and analyzed. The insights gained from this analysis were then synthesized to identify key considerations in integrating ChatGPT in education.

The study concludes that ChatGPT can be effectively integrated into education to automate routine tasks and enhance the learning experience for students, ultimately increasing productivity and efficiency and fostering adaptive learning. However, the limitations of ChatGPT, even when updated, must be borne in mind, including factual inconsistencies, potential bias promotion, lack of in-depth understanding and safety concerns. The study nevertheless highlights the benefits of responsibly integrating ChatGPT within the field of education.

This study has practical implications for educators and policymakers who are interested in the integration of AI technology in education. The study provides insights of using ChatGPT in education.

This article contributes to the existing literature by specifically examining the advantages of integrating ChatGPT in higher education and offering recommendations for its responsible use. Moreover, the article emphasizes ethical considerations in the context of ChatGPT integration.

The purpose of this paper is to measure the unidimensional and multidimensional inequality in the case of Pakistan and compare their results at the provincial as well as regional (urban and rural areas) level. The authors collected data from Pakistan Social and Living Standard Measurement and Household Integrated Economic Survey for fiscal years of 1998–1999 and 2013–2014.

The authors used Gini coefficient for unidimensional inequality and multidimensional indexing approach of Araar (2009) for multidimensional inequality.

The findings predicted that unidimensional inequality is relatively high in the urban area due to uneven dissemination of income, but multidimensional inequality is quite high in rural areas because of higher disparities among all dimensions. At the provincial level, Punjab has relatively high-income inequality followed by Sindh, KPK and Baluchistan.

This study is a pioneering effort to compare two time periods to explore unidimensional and multidimensional inequality in all provinces of Pakistan and their representative rural-urban regions by applying Araar and Duclos’s (2009) approach. Further, this study opens some new insights for policy makers.

Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.

The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.

Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.

The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.

This study attempts to recognize the effects of the Pakistan–China free trade agreements (PCFTA) on promoting trade between the two economies.

Following the concept of revealed comparative advantage (RCA) and free trade agreements, the study first identifies those commodities in which Pakistan and China have a robust RCA and then analyze the effect of PCFTA on the export value of those commodities for the bilateral trade between Pakistan and China. The study used the panel data in which more than the top 150 importers (j) have been selected for each case of Pakistan and China for the period of 2003–2015.

The study concludes that even with the higher convergence rate, the good RCA does not guarantee a positive effect of the free trade agreement on the commodities.

The study contributes to the existing literature by integrating RCA with the gravity model by adopting a sequential mode for Pakistan–China free trade agreement.

This study mainly aims to explore the causal nexus between big data-driven organizational capabilities (BDDOC) and supply chain innovation capabilities (SCIC) and innovation performance (IP), then explore the indirect effect of SCIC and also test the moderating effects for both internal supply chain integration (ISCI) and external supply chain integration (ESCI) into the relationship between BDDOC and SCIC.

In order to test the conceptual model and the hypothesized relationships between all the constructs, the data were collected using a self-reported questionnaire by workers in Jordanian small and medium manufacturing enterprises. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the model.

The paper reached a set of interesting results where it was confirmed that there is a positive and statistically significant relationship between BDDOC, SCIC and IP in addition to confirming the indirect effect of SCIC between BDDOC and IP. The results also showed that there is a moderating role for both ESCI and ISCI.

This study can be considered the first study in the current literature that investigates these constructs as shown in the research model. Therefore, the paper presents an interesting set of theoretical and managerial contributions that may contribute to covering part of the research gap in the literature.

To reason whether the ethical–moral cum economic coalition among the different sets of economy is more efficient with objectives of Islamic Shariah.

This is a theoretical paper based on the modified model of layers of economic coalition which was initially developed by W. Leontief and P.N. Mathur and tries to evaluate the impact of ethical–moral cum economic coalition among different sets of an economy within the framework of Islamic political economy system.

This study suggests that endogenoising the ethical–moral cum economic coalitions will comparatively enhance the efficiency level of the economy, and will also increase the social welfare level.

A dynamic cum marginal input–output table can be constructed on the basis of this framework.

This research is beneficial to the researchers, policy makers and social scientists for the enhancement of the level of social welfare through this coalition.

Research has shown that business model innovation can facilitate most ventures to innovate and remain competitive, yet there has been limited work on how digital leadership capabilities influence business model innovation. Building on the dynamic capabilities view, we address this gap by linking digital leadership capabilities with business model innovation via managerial decision-making through provision of grants received by new ventures.

The study is cross-sectional research. Data have been collected utilizing purposive sampling from 313 founding members of new ventures in high-velocity markets, i.e. from Pakistan. SPSS has been used to conduct the moderated mediation analysis.

Digital leadership capabilities foster the business model innovation of the new ventures because they enable new ventures to capitalize on digital technologies and create new ways of generating value for the customers and themselves. Moreover, managerial decision-making mediates digital leadership capabilities and business model innovation relationship, whereas, grants moderate the indirect positive effect of digital leadership capabilities on business model innovation via managerial decision-making. The study generates initial evidence on the impact of digital leadership capabilities on business model innovation via managerial decision-making for new ventures. We advance knowledge on new ventures’ business model innovation by deep-diving into dynamic capabilities view and emphasizing digital leadership capabilities as a significant driver for business model innovation.

With the help of dynamic capabilities theory, this study analyzes how new ventures make use of digital leadership capabilities to promote business model innovation.

The current study examines the impact of big data analytics capabilities (BDAC) on supply chain performances of the manufacturing firms. Based on the underpinning of resource-based view (RBV) theory, the current study will highlight the significance of BDAC on green dynamic capabilities (GDC), supply chain agility (SCA) and green competitive advantage (GCA). Furthermore, the study examines the moderating effect of supply chain innovativeness (SCI) on the relationship between GCA and firm performance (FP).

Online survey method was employed for the data collection from the 331 managers employed in Pakistan Stock Exchange (PSX)-listed manufacturing firms. The hypothesized model was tested using partial least squares structural equation modeling (PLS-SEM) technique.

The study results indicate that BDAC has a positive influence on both GDC and SCA, leading to enhanced GCA. Furthermore, the results demonstrate that GCA significantly and positively impacts FP, and the relationship between them is positively moderated by SCI.

This study developed a novel theoretical perspective based on RBV theory and provided empirical evidence that manufacturing firms' performances are significantly influenced by BDAC, GDC and SCA. The study results provide valuable practical implications top management regarding the effectiveness of BDAC and SCA in the supply chain. The findings further highlight the significance of SCI strengthening relationship between GCA and FP.