ChatGPT is an AI-powered chatbot platform that enables human users to converse with machines. It utilizes natural language processing and machine learning algorithms, transforming how people interact with AI technology. ChatGPT offers significant advantages over previous similar tools, and its potential for application in various fields has generated attention and anticipation. However, some experts are wary of ChatGPT, citing ethical implications. Therefore, this paper shows that ChatGPT has significant potential to transform marketing and shape its future if certain ethical considerations are taken into account. First, we argue that ChatGPT-based tools can help marketers create content faster and potentially with quality similar to human content creators. It can also assist marketers in conducting more efficient research and understanding customers better, automating customer service, and improving efficiency. Then we discuss ethical implications and potential risks for marketers, consumers, and other stakeholders, that are essential for ChatGPT-based marketing; doing so can help revolutionize marketing while avoiding potential harm to stakeholders.

With the increasing influence of machine learning algorithms in decision-making processes, concerns about fairness have gained significant attention. This area now offers significant literature that is complex and hard to penetrate for newcomers to the domain. Thus, a mapping study of articles exploring fairness issues is a valuable tool to provide a general introduction to this field. Our paper presents a systematic approach for exploring existing literature by aligning their discoveries with predetermined inquiries and a comprehensive overview of diverse bias dimensions, encompassing training data bias, model bias, conflicting fairness concepts, and the absence of prediction transparency, as observed across several influential articles. To establish connections between fairness issues and various issue mitigation approaches, we propose a taxonomy of machine learning fairness issues and map the diverse range of approaches scholars developed to address issues. We briefly explain the responsible critical factors behind these issues in a graphical view with a discussion and also highlight the limitations of each approach analyzed in the reviewed articles. Our study leads to a discussion regarding the potential future direction in ML and AI fairness.

The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.

In this paper, we present a routing algorithm useful in the realm of centralized range-based localization schemes. The proposed method is capable of estimating the distance between two non-neighboring sensors in multi-hop wireless sensor networks. Our method employs a global table search of sensor edges and recursive functions to find all possible paths between a source sensor and a destination sensor with the minimum number of hops. Using a distance matrix, the algorithm evaluates and averages all paths to estimate a measure of distance between both sensors. Our algorithm is then analyzed and compared with classical and novel approaches, and the results indicate that the proposed approach outperforms the other methods in distance estimate accuracy when used in random and uniform placement of nodes for large-scale wireless networks. Furthermore, the proposed methodology is suitable for the implementation in centralized localization schemes, such as multi-dimensional scaling, least squares, and maximum likelihood to mention a few.