Pub Date : 2024-06-24DOI: 10.1109/mis.2024.3398190
Przemysław Kazienko, Erik Cambria
Recommender systems have transformed our digital experiences in many regards. We enumerate six of their positive effects on the economy and humans, such as greater user satisfaction, time savings, broadening user horizons, and positive behavioral nudging. However, it is crucial to acknowledge the potential downsides inherent in their design. One significant concern is that these algorithms often prioritize the interests of the company deploying them, aiming to maximize profits and user engagement rather than solely focusing on enhancing user experience. Therefore, we also list and consider two use cases and six negative long-term impacts on humans, including addiction, reduced ability to think critically, less autonomy, and weakened human relationships caused by more and more human-like virtual assistants. Despite the undeniable utility of recommender systems, it is imperative to approach them critically, advocating for transparency, ethical considerations, and user empowerment to ensure that they serve as tools for enrichment rather than exploitation. To accomplish this, the idea and challenges of responsible recommender systems (RRSs) are presented. RRSs extend common recommender systems with components related to individual human values and goals as well as widely accepted well-being and lifestyle guidelines.
{"title":"Toward Responsible Recommender Systems","authors":"Przemysław Kazienko, Erik Cambria","doi":"10.1109/mis.2024.3398190","DOIUrl":"https://doi.org/10.1109/mis.2024.3398190","url":null,"abstract":"Recommender systems have transformed our digital experiences in many regards. We enumerate six of their positive effects on the economy and humans, such as greater user satisfaction, time savings, broadening user horizons, and positive behavioral nudging. However, it is crucial to acknowledge the potential downsides inherent in their design. One significant concern is that these algorithms often prioritize the interests of the company deploying them, aiming to maximize profits and user engagement rather than solely focusing on enhancing user experience. Therefore, we also list and consider two use cases and six negative long-term impacts on humans, including addiction, reduced ability to think critically, less autonomy, and weakened human relationships caused by more and more human-like virtual assistants. Despite the undeniable utility of recommender systems, it is imperative to approach them critically, advocating for transparency, ethical considerations, and user empowerment to ensure that they serve as tools for enrichment rather than exploitation. To accomplish this, the idea and challenges of responsible recommender systems (RRSs) are presented. RRSs extend common recommender systems with components related to individual human values and goals as well as widely accepted well-being and lifestyle guidelines.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"207 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-24DOI: 10.1109/mis.2024.3396371
Daniel E. O’Leary
This article compares numeric assessments generated by ChatGPT and Claude along four dimensions of novelty, feasibility, impact, and disruption, to study their ability to rate ideas. We find that those chatbots make numeric assessments that are consistent with the expected relationships between those dimensions, for example, novelty is negatively correlated with feasibility. We also find that the two chatbots make statistically significantly different numeric assessments of the same idea information. We suggest that this type of analysis can also be used to provide a type of validation of underlying chatbot capabilities. In addition, we suggest that, as part of their chatbot requirements analysis, enterprises use this approach to ensure that the chatbot appropriately “understands” concepts, in which they are directly interested.
本文比较了 ChatGPT 和 Claude 从新颖性、可行性、影响力和破坏性四个维度生成的数字评估，以研究它们对想法进行评级的能力。我们发现，这些聊天机器人做出的数字评估符合这些维度之间的预期关系，例如，新颖性与可行性呈负相关。我们还发现，两个聊天机器人对相同创意信息的数字评估在统计学上存在显著差异。我们认为这种分析也可以用来验证聊天机器人的基本能力。此外，我们建议，作为聊天机器人需求分析的一部分，企业可以使用这种方法来确保聊天机器人能够恰当地 "理解 "他们直接感兴趣的概念。
{"title":"A Comparison of Numeric Assessments of Ideas From Two Large Language Models: With Implications for Validating and Choosing LLMs","authors":"Daniel E. O’Leary","doi":"10.1109/mis.2024.3396371","DOIUrl":"https://doi.org/10.1109/mis.2024.3396371","url":null,"abstract":"This article compares numeric assessments generated by ChatGPT and Claude along four dimensions of novelty, feasibility, impact, and disruption, to study their ability to rate ideas. We find that those chatbots make numeric assessments that are consistent with the expected relationships between those dimensions, for example, novelty is negatively correlated with feasibility. We also find that the two chatbots make statistically significantly different numeric assessments of the same idea information. We suggest that this type of analysis can also be used to provide a type of validation of underlying chatbot capabilities. In addition, we suggest that, as part of their chatbot requirements analysis, enterprises use this approach to ensure that the chatbot appropriately “understands” concepts, in which they are directly interested.","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"30 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141524008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-17DOI: 10.1109/mis.2024.3408290
Trong-Nghia Nguyen, Soo-Hyung Kim, Bo-Gun Kho, Hyung-Jeong Yang
{"title":"Multi-Gradient Siamese Temporal Model for the Prediction of Clinical Events in Rapid Response Systems","authors":"Trong-Nghia Nguyen, Soo-Hyung Kim, Bo-Gun Kho, Hyung-Jeong Yang","doi":"10.1109/mis.2024.3408290","DOIUrl":"https://doi.org/10.1109/mis.2024.3408290","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-10DOI: 10.1109/mis.2024.3399053
Xu Bo, Gao Bin, Li Yunhu
{"title":"Improved Small Object Detection Algorithm Based on YOLOv5","authors":"Xu Bo, Gao Bin, Li Yunhu","doi":"10.1109/mis.2024.3399053","DOIUrl":"https://doi.org/10.1109/mis.2024.3399053","url":null,"abstract":"","PeriodicalId":13160,"journal":{"name":"IEEE Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":6.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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