This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing the necessity of establishing policies, guidelines, and frameworks that prioritize human rights, fairness, and transparency. The paper calls for a multidisciplinary dialogue among policymakers, technologists, and researchers to ensure responsible AI development that conforms to societal values and ethical standards. It stresses the urgency of addressing these ethical concerns and advocates for the development of generative AI in a socially beneficial and ethically sound manner, contributing significantly to the discourse on managing AI’s ethical implications in the modern digital era. The study highlights the theoretical and practical implications of these challenges and suggests a number of future research directions.

Arabic sentiment analysis is a process that aims to extract the subjective opinions of different users about different subjects since these opinions and sentiments are used to recognize their perspectives and judgments in a particular domain. Few research studies addressed semantic-oriented approaches for Arabic sentiment analysis based on domain ontologies and features’ importance. In this paper, we built a semantic orientation approach for calculating overall polarity from the Arabic subjective texts based on built domain ontology and the available sentiment lexicon. We used the ontology concepts to extract and weight the semantic domain features by considering their levels in the ontology tree and their frequencies in the dataset to compute the overall polarity of a given textual review based on the importance of each domain feature. For evaluation, an Arabic dataset from the hotels’ domain was selected to build the domain ontology and to test the proposed approach. The overall accuracy and f-measure reach 79.20% and 78.75%, respectively. Results showed that the approach outperformed the other semantic orientation approaches, and it is an appealing approach to be used for Arabic sentiment analysis.

Automatic parallelization of sequential programs combined with autotuning is an alternative to manual parallelization. With wider research directions and the increased number of performance tuning tools that have been developed, it becomes harder to choose a particular tuning tool to use. This paper reviews the fundamentals of different performance optimization and tuning techniques. It also surveys many tuning frameworks and classifies them into different groups based on their criteria. Developing benchmarks for HPC and validating their accuracy have been demanding tasks for computer architects, researchers, and application developers. In addition to providing a survey of performance tuning tools, we also performed a detailed review of current benchmarks and discussed the requirements for future benchmarks. We performed a detailed comparison of these tuning tools based on other features such as speedup and infrastructure details. We believe that this paper will be a very useful resource for the parallel computing community, especially for early-stage parallel computing and performance researchers to gain exposure to the existing performance optimization options.