Welcome to the Clearinghouse Project Library, where we highlight seminal and impactful articles focused on AI and its intersection with law, work, and society. Explore our searchable database of legal scholarly articles related to AI.
Featured Topics
- AI and Administrative Work
- AI and Criminal Justice
- AI and Education
- AI and Employment
- AI and Financial systems
- AI and Health
- AI, Immigration, and Human Rights
- AI Regulation and Strategies
- AI and Surveillance
- International/Comparative Regulation
- Al and War
- Al Race Law
- AI and Business
- AI and Creative Work
- Al and ESG
- AI and Medicine
- AI and Police Work
- AI and Managerial Work
- AI and White Collar Work
- AI and Blue Collar Work
We also introduce books, documentary films, and other media that have been created to address legal issues stemming from the use of automated decision-making. We hope that this clearinghouse will serve as a useful resource for a wide array of stakeholders including: legal scholars, practitioners, media, and students of AI and the Law at every level.
Explore Our Collection
Use the search function to discover articles, books, documentary films, and other media related to AI and the Future of Work, exploring the legal challenges and implications in various sectors.
21.
Chang, Cheng-chi
When AI Remembers Too Much: Reinventing the Right to Be Forgotten for the Generative Age Journal Article
In: Washington Journal of Law, Technology & Arts, vol. 19, no. 3, 2024, ISSN: 2157-2534.
Abstract | Links | BibTeX | Tags: AI Regulation and Strategies
@article{chang_when_2024,
title = {When AI Remembers Too Much: Reinventing the Right to Be Forgotten for the Generative Age},
author = {Cheng-chi Chang},
url = {https://digitalcommons.law.uw.edu/wjlta/vol19/iss3/2},
issn = {2157-2534},
year = {2024},
date = {2024-06-01},
journal = {Washington Journal of Law, Technology & Arts},
volume = {19},
number = {3},
abstract = {The emergence of generative artificial intelligence (AI) systems poses novel challenges for the right to be forgotten. While this right gained prominence following the 2014 Google Spain v. Gonzalez case, generative AI’s limitless memory and ability to reproduce identifiable data from fragments threaten traditional conceptions of forgetting. This Article traces the evolution of the right to be forgotten from its privacy law origins towards an independent entitlement grounded in self-determination for personal information. However, it contends the inherent limitations of using current anonymization, deletion, and geographical blocking mechanisms to prevent AI models from retaining personal data render forgetting infeasible. Moreover, the technical costs of forgetting—including tracking derivations and retraining models—could undermine enforceability. Therefore, this article advocates for a balanced legal approach that acknowledges the value of the right to forget while considering the constraints of implementing the right for generative AI. Although existing frameworks like the European Union’s GDPR provide a foundation, continuous regulatory evolution through oversight bodies and industry collaboration is imperative. This article underscores how the right to be forgotten must be reconceptualized to address the reality of generative AI systems. It provides an interdisciplinary analysis of this right’s limitations and proposes strategies to reconcile human dignity and autonomy with the emerging technological realities of AI. This Article’s original contribution lies in its nuanced approach to integrating legal and technical dimensions to develop adaptive frameworks for the right to be forgotten in the age of generative AI.},
keywords = {AI Regulation and Strategies},
pubstate = {published},
tppubtype = {article}
}
The emergence of generative artificial intelligence (AI) systems poses novel challenges for the right to be forgotten. While this right gained prominence following the 2014 Google Spain v. Gonzalez case, generative AI’s limitless memory and ability to reproduce identifiable data from fragments threaten traditional conceptions of forgetting. This Article traces the evolution of the right to be forgotten from its privacy law origins towards an independent entitlement grounded in self-determination for personal information. However, it contends the inherent limitations of using current anonymization, deletion, and geographical blocking mechanisms to prevent AI models from retaining personal data render forgetting infeasible. Moreover, the technical costs of forgetting—including tracking derivations and retraining models—could undermine enforceability. Therefore, this article advocates for a balanced legal approach that acknowledges the value of the right to forget while considering the constraints of implementing the right for generative AI. Although existing frameworks like the European Union’s GDPR provide a foundation, continuous regulatory evolution through oversight bodies and industry collaboration is imperative. This article underscores how the right to be forgotten must be reconceptualized to address the reality of generative AI systems. It provides an interdisciplinary analysis of this right’s limitations and proposes strategies to reconcile human dignity and autonomy with the emerging technological realities of AI. This Article’s original contribution lies in its nuanced approach to integrating legal and technical dimensions to develop adaptive frameworks for the right to be forgotten in the age of generative AI.
22.
Maroudas, Vasileios P.
In: Review of European and Comparative Law, vol. 57, no. 2, pp. 135–169, 2024, ISSN: 2545-384X, (Number: 2).
Abstract | Links | BibTeX | Tags: AI and Medicine
@article{maroudas_faultbased_2024,
title = {Fault–Based Liability for Medical Malpractice in the Age of Artificial Intelligence: Α Comparative Analysis of German and Greek Medical Liability Law in View of the Challenges Posed by AI Systems},
author = {Vasileios P. Maroudas},
url = {https://czasopisma.kul.pl/index.php/recl/article/view/17223},
doi = {10.31743/recl.17223},
issn = {2545-384X},
year = {2024},
date = {2024-06-01},
urldate = {2024-11-22},
journal = {Review of European and Comparative Law},
volume = {57},
number = {2},
pages = {135–169},
abstract = {The rapid developments in the field of AI pose intractable problems for the law of civil liability. The main question that arises in this context is whether a fault-based liability regime can provide sufficient protection to victims of harm caused by the use of ΑΙ. This article addresses this question specifically in relation to medical malpractice liability. Its main purpose is to outline the problems that autonomous systems pose for medical liability law, but more importantly, to determine whether and to what extent a fault-based system of medical liability can adequately address them. In order to approach this issue, a comparative examination of German and Greek law will be undertaken. These two systems, while similar in substantive terms, differ significantly at the level of the burden of proof. In this sense, their comparison serves as a good example to “test” the adequacy of the fault principle in relation to AI systems in the field of medicine, but also to illustrate the practical importance that rules on the allocation of the burden of proof can have in cases of damage caused by the use of AI. As will eventually become apparent, the main problem appears to lie not in the fault principle itself, which, for the time being, at least in the form of objectified negligence, seems to protect the patient adequately, but mainly in the general rule for the allocation of the burden of proof, which is precisely why the fault principle ends up working to the detriment of the patient.},
note = {Number: 2},
keywords = {AI and Medicine},
pubstate = {published},
tppubtype = {article}
}
The rapid developments in the field of AI pose intractable problems for the law of civil liability. The main question that arises in this context is whether a fault-based liability regime can provide sufficient protection to victims of harm caused by the use of ΑΙ. This article addresses this question specifically in relation to medical malpractice liability. Its main purpose is to outline the problems that autonomous systems pose for medical liability law, but more importantly, to determine whether and to what extent a fault-based system of medical liability can adequately address them. In order to approach this issue, a comparative examination of German and Greek law will be undertaken. These two systems, while similar in substantive terms, differ significantly at the level of the burden of proof. In this sense, their comparison serves as a good example to “test” the adequacy of the fault principle in relation to AI systems in the field of medicine, but also to illustrate the practical importance that rules on the allocation of the burden of proof can have in cases of damage caused by the use of AI. As will eventually become apparent, the main problem appears to lie not in the fault principle itself, which, for the time being, at least in the form of objectified negligence, seems to protect the patient adequately, but mainly in the general rule for the allocation of the burden of proof, which is precisely why the fault principle ends up working to the detriment of the patient.
23.
Koshiyama, Adriano; Kazim, Emre; Treleaven, Philip; Rai, Pete; Szpruch, Lukasz; Pavey, Giles; Ahamat, Ghazi; Leutner, Franziska; Goebel, Randy; Knight, Andrew; Adams, Janet; Hitrova, Christina; Barnett, Jeremy; Nachev, Parashkev; Barber, David; Chamorro-Premuzic, Tomas; Klemmer, Konstantin; Gregorovic, Miro; Khan, Shakeel; Lomas, Elizabeth; Hilliard, Airlie; Chatterjee, Siddhant
Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms Journal Article
In: Royal Society Open Science, vol. 11, no. 5, pp. 230859, 2024, (Publisher: Royal Society).
Abstract | Links | BibTeX | Tags: AI and Financial Systems
@article{koshiyama_towards_2024,
title = {Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms},
author = {Adriano Koshiyama and Emre Kazim and Philip Treleaven and Pete Rai and Lukasz Szpruch and Giles Pavey and Ghazi Ahamat and Franziska Leutner and Randy Goebel and Andrew Knight and Janet Adams and Christina Hitrova and Jeremy Barnett and Parashkev Nachev and David Barber and Tomas Chamorro-Premuzic and Konstantin Klemmer and Miro Gregorovic and Shakeel Khan and Elizabeth Lomas and Airlie Hilliard and Siddhant Chatterjee},
url = {https://royalsocietypublishing.org/doi/10.1098/rsos.230859},
doi = {10.1098/rsos.230859},
year = {2024},
date = {2024-05-01},
urldate = {2024-10-21},
journal = {Royal Society Open Science},
volume = {11},
number = {5},
pages = {230859},
abstract = {Business reliance on algorithms is becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include Google’s AI algorithm for photo classification mistakenly labelling a black couple as gorillas in 2015 (Gebru 2020 In The Oxford handbook of ethics of AI, pp. 251–269), Microsoft’s AI chatbot Tay that spread racist, sexist and antisemitic speech on Twitter (now X) (Wolf et al. 2017 ACM Sigcas Comput. Soc. 47, 54–64 (doi:10.1145/3144592.3144598)), and Amazon’s AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies and the judiciary discussing potentially making algorithms artificial ‘persons’ in law. As with financial audits, governments, business and society will require algorithm audits; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms. The stakeholders range from those working on policy/regulation to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance with regulation/standards. Our goal in this article is to survey the key areas necessary to perform auditing and assurance and instigate the debate in this novel area of research and practice.},
note = {Publisher: Royal Society},
keywords = {AI and Financial Systems},
pubstate = {published},
tppubtype = {article}
}
Business reliance on algorithms is becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include Google’s AI algorithm for photo classification mistakenly labelling a black couple as gorillas in 2015 (Gebru 2020 In The Oxford handbook of ethics of AI, pp. 251–269), Microsoft’s AI chatbot Tay that spread racist, sexist and antisemitic speech on Twitter (now X) (Wolf et al. 2017 ACM Sigcas Comput. Soc. 47, 54–64 (doi:10.1145/3144592.3144598)), and Amazon’s AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies and the judiciary discussing potentially making algorithms artificial ‘persons’ in law. As with financial audits, governments, business and society will require algorithm audits; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms. The stakeholders range from those working on policy/regulation to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance with regulation/standards. Our goal in this article is to survey the key areas necessary to perform auditing and assurance and instigate the debate in this novel area of research and practice.
24.
Zhang, Dongyang
The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence Journal Article
In: Energy Economics, vol. 133, pp. 107562, 2024, ISSN: 0140-9883.
Abstract | Links | BibTeX | Tags: AI and ESG
@article{zhang_pathway_2024,
title = {The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence},
author = {Dongyang Zhang},
url = {https://www.sciencedirect.com/science/article/pii/S0140988324002706},
doi = {10.1016/j.eneco.2024.107562},
issn = {0140-9883},
year = {2024},
date = {2024-05-01},
urldate = {2024-11-22},
journal = {Energy Economics},
volume = {133},
pages = {107562},
abstract = {Artificial Intelligence (AI) can improve production efficiency and general quality of life through assisting human labor, potentially leading to the conversion of employment types, enhancing industrialization, and upgrading energy structure. This paper enriches the role of AI in improving sustainable growth by curbing hypocritical sustainable and greenwashing behaviors. By accessing the panel data from Chinese listed-firms for the period 2014–2021, we have shown that AI can significantly mitigate the existence of greenwashing behaviors by raising the disclosure quality of ESG rating scores. Moreover, the role of AI in mitigating greenwashing behaviors performs significantly in SOEs, less pollution-intensive industries, high environmental regulation and less developed green finance regions. Furthermore, the potential mechanisms of AI in mitigating greenwashing behaviors are displayed, including alleviating financial constraints, easing management cost, improving green innovations.},
keywords = {AI and ESG},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence (AI) can improve production efficiency and general quality of life through assisting human labor, potentially leading to the conversion of employment types, enhancing industrialization, and upgrading energy structure. This paper enriches the role of AI in improving sustainable growth by curbing hypocritical sustainable and greenwashing behaviors. By accessing the panel data from Chinese listed-firms for the period 2014–2021, we have shown that AI can significantly mitigate the existence of greenwashing behaviors by raising the disclosure quality of ESG rating scores. Moreover, the role of AI in mitigating greenwashing behaviors performs significantly in SOEs, less pollution-intensive industries, high environmental regulation and less developed green finance regions. Furthermore, the potential mechanisms of AI in mitigating greenwashing behaviors are displayed, including alleviating financial constraints, easing management cost, improving green innovations.
25.
Bell, Raoul; Menne, Nicola Marie; Mayer, Carolin; Buchner, Axel
On the advantages of using AI-generated images of filler faces for creating fair lineups Journal Article
In: Scientific Reports, vol. 14, no. 1, pp. 12304, 2024, ISSN: 2045-2322, (Publisher: Nature Publishing Group).
Abstract | Links | BibTeX | Tags: AI and Police Work
@article{bell_advantages_2024,
title = {On the advantages of using AI-generated images of filler faces for creating fair lineups},
author = {Raoul Bell and Nicola Marie Menne and Carolin Mayer and Axel Buchner},
url = {https://www.nature.com/articles/s41598-024-63004-z},
doi = {10.1038/s41598-024-63004-z},
issn = {2045-2322},
year = {2024},
date = {2024-05-01},
urldate = {2024-11-22},
journal = {Scientific Reports},
volume = {14},
number = {1},
pages = {12304},
abstract = {Recent advances in artificial intelligence (AI) enable the generation of realistic facial images that can be used in police lineups. The use of AI image generation offers pragmatic advantages in that it allows practitioners to generate filler images directly from the description of the culprit using text-to-image generation, avoids the violation of identity rights of natural persons who are not suspects and eliminates the constraints of being bound to a database with a limited set of photographs. However, the risk exists that using AI-generated filler images provokes more biased selection of the suspect if eyewitnesses are able to distinguish AI-generated filler images from the photograph of the suspect’s face. Using a model-based analysis, we compared biased suspect selection directly between lineups with AI-generated filler images and lineups with database-derived filler photographs. The results show that the lineups with AI-generated filler images were perfectly fair and, in fact, led to less biased suspect selection than the lineups with database-derived filler photographs used in previous experiments. These results are encouraging with regard to the potential of AI image generation for constructing fair lineups which should inspire more systematic research on the feasibility of adopting AI technology in forensic settings.},
note = {Publisher: Nature Publishing Group},
keywords = {AI and Police Work},
pubstate = {published},
tppubtype = {article}
}
Recent advances in artificial intelligence (AI) enable the generation of realistic facial images that can be used in police lineups. The use of AI image generation offers pragmatic advantages in that it allows practitioners to generate filler images directly from the description of the culprit using text-to-image generation, avoids the violation of identity rights of natural persons who are not suspects and eliminates the constraints of being bound to a database with a limited set of photographs. However, the risk exists that using AI-generated filler images provokes more biased selection of the suspect if eyewitnesses are able to distinguish AI-generated filler images from the photograph of the suspect’s face. Using a model-based analysis, we compared biased suspect selection directly between lineups with AI-generated filler images and lineups with database-derived filler photographs. The results show that the lineups with AI-generated filler images were perfectly fair and, in fact, led to less biased suspect selection than the lineups with database-derived filler photographs used in previous experiments. These results are encouraging with regard to the potential of AI image generation for constructing fair lineups which should inspire more systematic research on the feasibility of adopting AI technology in forensic settings.
26.
Hinks, Tim
Navigating technological shifts: worker perspectives on AI and emerging technologies impacting well-being Journal Article
In: AI & SOCIETY, 2024, ISSN: 1435-5655.
Abstract | Links | BibTeX | Tags: AI and White Collar Work
@article{hinks_navigating_2024,
title = {Navigating technological shifts: worker perspectives on AI and emerging technologies impacting well-being},
author = {Tim Hinks},
url = {https://doi.org/10.1007/s00146-024-01962-8},
doi = {10.1007/s00146-024-01962-8},
issn = {1435-5655},
year = {2024},
date = {2024-05-01},
urldate = {2024-11-24},
journal = {AI & SOCIETY},
abstract = {This paper asks whether workers’ experience of working with new technologies and workers’ perceived threats of new technologies are associated with expected well-being. Using survey data for 25 OECD countries we find that both experiences of new technologies and threats of new technologies are associated with more concern about expected well-being. Controlling for the negative experiences of COVID-19 on workers and their macroeconomic outlook both mitigate these findings, but workers with negative experiences of working alongside and with new technologies still report lower expected well-being.},
keywords = {AI and White Collar Work},
pubstate = {published},
tppubtype = {article}
}
This paper asks whether workers’ experience of working with new technologies and workers’ perceived threats of new technologies are associated with expected well-being. Using survey data for 25 OECD countries we find that both experiences of new technologies and threats of new technologies are associated with more concern about expected well-being. Controlling for the negative experiences of COVID-19 on workers and their macroeconomic outlook both mitigate these findings, but workers with negative experiences of working alongside and with new technologies still report lower expected well-being.
27.
Yaiprasert, Chairote; Hidayanto, Achmad Nizar
AI-powered ensemble machine learning to optimize cost strategies in logistics business Journal Article
In: International Journal of Information Management Data Insights, vol. 4, no. 1, pp. 100209, 2024, ISSN: 2667-0968.
Abstract | Links | BibTeX | Tags: AI and Business
@article{yaiprasert_ai-powered_2024,
title = {AI-powered ensemble machine learning to optimize cost strategies in logistics business},
author = {Chairote Yaiprasert and Achmad Nizar Hidayanto},
url = {https://www.sciencedirect.com/science/article/pii/S2667096823000551},
doi = {10.1016/j.jjimei.2023.100209},
issn = {2667-0968},
year = {2024},
date = {2024-04-01},
urldate = {2024-11-22},
journal = {International Journal of Information Management Data Insights},
volume = {4},
number = {1},
pages = {100209},
abstract = {This research investigates the potential advantages of using artificial intelligence (AI) to drive ensemble machine learning (ML) for enhancing cost strategies and maximizing profits. This study aims to explore the ability of AI-powered ensemble ML to optimize cost strategies by simulating business threshold cost data to determine optimal mitigation strategies. The dataset comprises 6561 potential tuples, and three ensemble ML methods are employed as ML algorithms to identify patterns and relationships in the cost data for strategic decisions. The originality of this project lies in its demonstration of the capacity of simulated data to enhance cost-saving strategies for businesses. This research contributes to the existing literature on AI and ML applications in business by revealing the potential of ML applications for business owners and personnel involved in production and marketing. The findings of this research have significant implications for a wide range of industries, including transportation, logistics, and retail.},
keywords = {AI and Business},
pubstate = {published},
tppubtype = {article}
}
This research investigates the potential advantages of using artificial intelligence (AI) to drive ensemble machine learning (ML) for enhancing cost strategies and maximizing profits. This study aims to explore the ability of AI-powered ensemble ML to optimize cost strategies by simulating business threshold cost data to determine optimal mitigation strategies. The dataset comprises 6561 potential tuples, and three ensemble ML methods are employed as ML algorithms to identify patterns and relationships in the cost data for strategic decisions. The originality of this project lies in its demonstration of the capacity of simulated data to enhance cost-saving strategies for businesses. This research contributes to the existing literature on AI and ML applications in business by revealing the potential of ML applications for business owners and personnel involved in production and marketing. The findings of this research have significant implications for a wide range of industries, including transportation, logistics, and retail.
28.
Wingström, Roosa; Hautala, Johanna; Lundman, Riina
Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists Journal Article
In: Creativity Research Journal, vol. 36, no. 2, pp. 177–193, 2024, ISSN: 1040-0419, (Publisher: Routledge _eprint: https://doi.org/10.1080/10400419.2022.2107850).
Abstract | Links | BibTeX | Tags: AI and Creative Work
@article{wingstrom_redefining_2024,
title = {Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists},
author = {Roosa Wingström and Johanna Hautala and Riina Lundman},
url = {https://doi.org/10.1080/10400419.2022.2107850},
doi = {10.1080/10400419.2022.2107850},
issn = {1040-0419},
year = {2024},
date = {2024-04-01},
urldate = {2024-11-22},
journal = {Creativity Research Journal},
volume = {36},
number = {2},
pages = {177–193},
abstract = {Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/10400419.2022.2107850},
keywords = {AI and Creative Work},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.
29.
Jo, Hyeon; Park, Do-Hyung
Effects of ChatGPT’s AI capabilities and human-like traits on spreading information in work environments Journal Article
In: Scientific Reports, vol. 14, no. 1, pp. 7806, 2024, ISSN: 2045-2322, (Publisher: Nature Publishing Group).
Abstract | Links | BibTeX | Tags: AI and White Collar Work
@article{jo_effects_2024,
title = {Effects of ChatGPT’s AI capabilities and human-like traits on spreading information in work environments},
author = {Hyeon Jo and Do-Hyung Park},
url = {https://www.nature.com/articles/s41598-024-57977-0},
doi = {10.1038/s41598-024-57977-0},
issn = {2045-2322},
year = {2024},
date = {2024-04-01},
urldate = {2024-11-24},
journal = {Scientific Reports},
volume = {14},
number = {1},
pages = {7806},
abstract = {The rapid proliferation and integration of AI chatbots in office environments, specifically the advanced AI model ChatGPT, prompts an examination of how its features and updates impact knowledge processes, satisfaction, and word-of-mouth (WOM) among office workers. This study investigates the determinants of WOM among office workers who are users of ChatGPT. We adopted a quantitative approach, utilizing a stratified random sampling technique to collect data from a diverse group of office workers experienced in using ChatGPT. The hypotheses were rigorously tested through Structural Equation Modeling (SEM) using the SmartPLS 4. The results revealed that system updates, memorability, and non-language barrier attributes of ChatGPT significantly enhanced knowledge acquisition and application. Additionally, the human-like personality traits of ChatGPT significantly increased both utilitarian value and satisfaction. Furthermore, the study showed that knowledge acquisition and application led to a significant increase in utilitarian value and satisfaction, which subsequently increased WOM. Age had a positive influence on WOM, while gender had no significant impact. The findings provide theoretical contributions by expanding our understanding of AI chatbots' role in knowledge processes, satisfaction, and WOM, particularly among office workers.},
note = {Publisher: Nature Publishing Group},
keywords = {AI and White Collar Work},
pubstate = {published},
tppubtype = {article}
}
The rapid proliferation and integration of AI chatbots in office environments, specifically the advanced AI model ChatGPT, prompts an examination of how its features and updates impact knowledge processes, satisfaction, and word-of-mouth (WOM) among office workers. This study investigates the determinants of WOM among office workers who are users of ChatGPT. We adopted a quantitative approach, utilizing a stratified random sampling technique to collect data from a diverse group of office workers experienced in using ChatGPT. The hypotheses were rigorously tested through Structural Equation Modeling (SEM) using the SmartPLS 4. The results revealed that system updates, memorability, and non-language barrier attributes of ChatGPT significantly enhanced knowledge acquisition and application. Additionally, the human-like personality traits of ChatGPT significantly increased both utilitarian value and satisfaction. Furthermore, the study showed that knowledge acquisition and application led to a significant increase in utilitarian value and satisfaction, which subsequently increased WOM. Age had a positive influence on WOM, while gender had no significant impact. The findings provide theoretical contributions by expanding our understanding of AI chatbots' role in knowledge processes, satisfaction, and WOM, particularly among office workers.
30.
Savulescu, Julian; Giubilini, Alberto; Vandersluis, Robert; Mishra, Abhishek
Ethics of artificial intelligence in medicine Journal Article
In: Singapore Medical Journal, vol. 65, no. 3, pp. 150, 2024, ISSN: 0037-5675.
Abstract | Links | BibTeX | Tags: AI and Medicine
@article{savulescu_ethics_2024,
title = {Ethics of artificial intelligence in medicine},
author = {Julian Savulescu and Alberto Giubilini and Robert Vandersluis and Abhishek Mishra},
url = {https://journals.lww.com/smj/fulltext/2024/03000/ethics_of_artificial_intelligence_in_medicine.5.aspx},
doi = {10.4103/singaporemedj.SMJ-2023-279},
issn = {0037-5675},
year = {2024},
date = {2024-03-01},
urldate = {2024-11-22},
journal = {Singapore Medical Journal},
volume = {65},
number = {3},
pages = {150},
abstract = {This article reviews the main ethical issues that arise from the use of artificial intelligence (AI) technologies in medicine. Issues around trust, responsibility, risks of discrimination, privacy, autonomy, and potential benefits and harms are assessed. For better or worse, AI is a promising technology that can revolutionise healthcare delivery. It is up to us to make AI a tool for the good by ensuring that ethical oversight accompanies the design, development and implementation of AI technology in clinical practice.},
keywords = {AI and Medicine},
pubstate = {published},
tppubtype = {article}
}
This article reviews the main ethical issues that arise from the use of artificial intelligence (AI) technologies in medicine. Issues around trust, responsibility, risks of discrimination, privacy, autonomy, and potential benefits and harms are assessed. For better or worse, AI is a promising technology that can revolutionise healthcare delivery. It is up to us to make AI a tool for the good by ensuring that ethical oversight accompanies the design, development and implementation of AI technology in clinical practice.
31.
Cuéllar, Mariano-Florentino; Larsen, Benjamin; Lee, Yong Suk; Webb, Michael
Does Information About AI Regulation Change Manager Evaluation of Ethical Concerns and Intent to Adopt AI? Journal Article
In: The Journal of Law, Economics, and Organization, vol. 40, no. 1, pp. 34–75, 2024, ISSN: 8756-6222.
Abstract | Links | BibTeX | Tags: AI and Managerial Work
@article{cuellar_does_2024,
title = {Does Information About AI Regulation Change Manager Evaluation of Ethical Concerns and Intent to Adopt AI?},
author = {Mariano-Florentino Cuéllar and Benjamin Larsen and Yong Suk Lee and Michael Webb},
url = {https://doi.org/10.1093/jleo/ewac004},
doi = {10.1093/jleo/ewac004},
issn = {8756-6222},
year = {2024},
date = {2024-03-01},
urldate = {2024-11-22},
journal = {The Journal of Law, Economics, and Organization},
volume = {40},
number = {1},
pages = {34–75},
abstract = {We examine the impacts of potential artificial intelligence (AI) regulations on managers’ perceptions on ethical issues related to AI and their intentions to adopt AI technologies. We conduct a randomized online survey experiment on more than a thousand managers in the United States. We randomly present managers with different proposed AI regulations, and ask about ethical issues related to AI and their intentions related to AI adoption. We find that information about AI regulation increases manager perception of the importance of safety, privacy, bias/discrimination, and transparency issues related to AI. However, there is a tradeoff; regulation information reduces manager intent to adopt AI technologies. Moreover, information about regulation increases manager intent to spend on developing AI strategy including ethical issues at the cost of investing in AI adoption, such as providing AI training to current employees or purchasing AI software packages. (JEL: K24, L21, L51, O33, O38)},
keywords = {AI and Managerial Work},
pubstate = {published},
tppubtype = {article}
}
We examine the impacts of potential artificial intelligence (AI) regulations on managers’ perceptions on ethical issues related to AI and their intentions to adopt AI technologies. We conduct a randomized online survey experiment on more than a thousand managers in the United States. We randomly present managers with different proposed AI regulations, and ask about ethical issues related to AI and their intentions related to AI adoption. We find that information about AI regulation increases manager perception of the importance of safety, privacy, bias/discrimination, and transparency issues related to AI. However, there is a tradeoff; regulation information reduces manager intent to adopt AI technologies. Moreover, information about regulation increases manager intent to spend on developing AI strategy including ethical issues at the cost of investing in AI adoption, such as providing AI training to current employees or purchasing AI software packages. (JEL: K24, L21, L51, O33, O38)
32.
Lim, Tristan
Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways Journal Article
In: Artificial Intelligence Review, vol. 57, no. 4, pp. 76, 2024, ISSN: 1573-7462.
Abstract | Links | BibTeX | Tags: AI and ESG
@article{lim_environmental_2024,
title = {Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways},
author = {Tristan Lim},
url = {https://doi.org/10.1007/s10462-024-10708-3},
doi = {10.1007/s10462-024-10708-3},
issn = {1573-7462},
year = {2024},
date = {2024-02-01},
urldate = {2024-11-22},
journal = {Artificial Intelligence Review},
volume = {57},
number = {4},
pages = {76},
abstract = {The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.},
keywords = {AI and ESG},
pubstate = {published},
tppubtype = {article}
}
The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.
33.
Zhang, Yuefang
The Practice of Refined Management of Office Reception Work in the Era of Artificial Intelligence Journal Article
In: Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, 2024.
Abstract | Links | BibTeX | Tags: AI and White Collar Work
@article{zhang_practice_2024,
title = {The Practice of Refined Management of Office Reception Work in the Era of Artificial Intelligence},
author = {Yuefang Zhang},
url = {https://sciendo.com/article/10.2478/amns-2024-0406},
doi = {10.2478/amns-2024-0406},
year = {2024},
date = {2024-02-01},
urldate = {2024-11-24},
journal = {Applied Mathematics and Nonlinear Sciences},
volume = {9},
number = {1},
abstract = {While discussing the importance of office reception work, this article emphasizes the key role of fine management in improving work efficiency and service quality. Against the background of the artificial intelligence era, the article successfully constructs a framework for an office reception work management system by utilizing cutting-edge React and Express technologies. The system not only carries out meticulous module design based on functional requirements, but also proposes a personalized recommendation algorithm integrating clustering and collaborative filtering by optimizing the Similarity of user characteristics and item attributes for joint filtering recommendation to achieve more humanized reception services. To verify the system's effectiveness, this study conducted an application analysis using University Q as a case study. The analysis results show that from 2008 to 2022, the indices of reception preparation, knowledge of reception work, staffing, business skills of reception staff, work attitude and work detail control have been significantly improved, with an increase ranging from 32.0761 to 37.1677, indicating that the management level of office reception work is constantly optimized and shows a rapid growth. This innovative office reception work management system not only realizes refined management, but also comprehensively improves the service efficiency and comprehensive management level, which provides a valuable reference for the management of similar work.},
keywords = {AI and White Collar Work},
pubstate = {published},
tppubtype = {article}
}
While discussing the importance of office reception work, this article emphasizes the key role of fine management in improving work efficiency and service quality. Against the background of the artificial intelligence era, the article successfully constructs a framework for an office reception work management system by utilizing cutting-edge React and Express technologies. The system not only carries out meticulous module design based on functional requirements, but also proposes a personalized recommendation algorithm integrating clustering and collaborative filtering by optimizing the Similarity of user characteristics and item attributes for joint filtering recommendation to achieve more humanized reception services. To verify the system's effectiveness, this study conducted an application analysis using University Q as a case study. The analysis results show that from 2008 to 2022, the indices of reception preparation, knowledge of reception work, staffing, business skills of reception staff, work attitude and work detail control have been significantly improved, with an increase ranging from 32.0761 to 37.1677, indicating that the management level of office reception work is constantly optimized and shows a rapid growth. This innovative office reception work management system not only realizes refined management, but also comprehensively improves the service efficiency and comprehensive management level, which provides a valuable reference for the management of similar work.
34.
Hallevy, Gabriel
The Basic Models of Criminal Liability of AI Systems and Outer Circles Proceedings Article
In: Vicente, Dário Moura; Pereira, Rui Soares; Leal, Ana Alves (Ed.): Legal Aspects of Autonomous Systems, pp. 69–82, Springer International Publishing, Cham, 2024, ISBN: 978-3-031-47946-5.
Abstract | Links | BibTeX | Tags: AI and Criminal Justice
@inproceedings{hallevy_basic_2024,
title = {The Basic Models of Criminal Liability of AI Systems and Outer Circles},
author = {Gabriel Hallevy},
editor = {Dário Moura Vicente and Rui Soares Pereira and Ana Alves Leal},
doi = {10.1007/978-3-031-47946-5_5},
isbn = {978-3-031-47946-5},
year = {2024},
date = {2024-01-01},
booktitle = {Legal Aspects of Autonomous Systems},
pages = {69–82},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The way humans cope with breaches of legal order is through criminal law operated by the criminal justice system. Accordingly, human societies define criminal offenses and operate social mechanisms to apply them. This is how criminal law works. Originally, this way has been designed by humans and for humans. However, as technology has developed, criminal offenses are committed not only by humans. The major development in this issue has occurred in the seventeenth century. In the twenty-first century criminal law is required to supply adequate solutions for commission of criminal offenses through artificial intelligent (AI) systems. Basically, there are three basic models to cope with this phenomenon within the current definitions of criminal law. These models are:
(1)
The Perpetration-by-Another Liability Model;
(2)
The Natural Probable Consequence Liability Model; and
(3)
The Direct Liability Model.
This paper was presented at the “International Conference on Autonomous Systems and the Law”, organized by CIDP, (Centro de Investigação de Direito Privado), University of Lisbon. I thank the organizers for inviting me to the conference and to the participants for their questions and interest in this issue. The Models are based on previous researches published around the world, including two of the author’s books: Hallevy (2013, 2015a, b).},
keywords = {AI and Criminal Justice},
pubstate = {published},
tppubtype = {inproceedings}
}
The way humans cope with breaches of legal order is through criminal law operated by the criminal justice system. Accordingly, human societies define criminal offenses and operate social mechanisms to apply them. This is how criminal law works. Originally, this way has been designed by humans and for humans. However, as technology has developed, criminal offenses are committed not only by humans. The major development in this issue has occurred in the seventeenth century. In the twenty-first century criminal law is required to supply adequate solutions for commission of criminal offenses through artificial intelligent (AI) systems. Basically, there are three basic models to cope with this phenomenon within the current definitions of criminal law. These models are:
(1)
The Perpetration-by-Another Liability Model;
(2)
The Natural Probable Consequence Liability Model; and
(3)
The Direct Liability Model.
This paper was presented at the “International Conference on Autonomous Systems and the Law”, organized by CIDP, (Centro de Investigação de Direito Privado), University of Lisbon. I thank the organizers for inviting me to the conference and to the participants for their questions and interest in this issue. The Models are based on previous researches published around the world, including two of the author’s books: Hallevy (2013, 2015a, b).
(1)
The Perpetration-by-Another Liability Model;
(2)
The Natural Probable Consequence Liability Model; and
(3)
The Direct Liability Model.
This paper was presented at the “International Conference on Autonomous Systems and the Law”, organized by CIDP, (Centro de Investigação de Direito Privado), University of Lisbon. I thank the organizers for inviting me to the conference and to the participants for their questions and interest in this issue. The Models are based on previous researches published around the world, including two of the author’s books: Hallevy (2013, 2015a, b).
35.
Gofman, Michael; Jin, Zhao
Artificial Intelligence, Education, and Entrepreneurship Journal Article
In: The Journal of Finance, vol. 79, no. 1, pp. 631–667, 2024, ISSN: 1540-6261, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jofi.13302).
Abstract | Links | BibTeX | Tags: AI and Education
@article{gofman_artificial_2024,
title = {Artificial Intelligence, Education, and Entrepreneurship},
author = {Michael Gofman and Zhao Jin},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.13302},
doi = {10.1111/jofi.13302},
issn = {1540-6261},
year = {2024},
date = {2024-01-01},
urldate = {2024-10-21},
journal = {The Journal of Finance},
volume = {79},
number = {1},
pages = {631–667},
abstract = {We document an unprecedented brain drain of Artificial Intelligence (AI) professors from universities from 2004 to 2018. We find that students from the affected universities establish fewer AI startups and raise less funding. The brain-drain effect is significant for tenured professors, professors from top universities, and deep-learning professors. Additional evidence suggests that unobserved city- and university-level shocks are unlikely to drive our results. We consider several economic channels for the findings. The most consistent explanation is that professors' departures reduce startup founders' AI knowledge, which we find is an important factor for successful startup formation and fundraising.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jofi.13302},
keywords = {AI and Education},
pubstate = {published},
tppubtype = {article}
}
We document an unprecedented brain drain of Artificial Intelligence (AI) professors from universities from 2004 to 2018. We find that students from the affected universities establish fewer AI startups and raise less funding. The brain-drain effect is significant for tenured professors, professors from top universities, and deep-learning professors. Additional evidence suggests that unobserved city- and university-level shocks are unlikely to drive our results. We consider several economic channels for the findings. The most consistent explanation is that professors' departures reduce startup founders' AI knowledge, which we find is an important factor for successful startup formation and fundraising.
36.
Shivram, Vivek
Auditing with Ai: A Theoretical Framework for Applying Machine Learning Across the Internal Audit Lifecycle Journal Article
In: EDPACS, vol. 69, no. 1, pp. 22–40, 2024, ISSN: 0736-6981, (Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/07366981.2024.2312025).
Abstract | Links | BibTeX | Tags: AI and Financial Systems
@article{shivram_auditing_2024,
title = {Auditing with Ai: A Theoretical Framework for Applying Machine Learning Across the Internal Audit Lifecycle},
author = {Vivek Shivram},
url = {https://doi.org/10.1080/07366981.2024.2312025},
doi = {10.1080/07366981.2024.2312025},
issn = {0736-6981},
year = {2024},
date = {2024-01-01},
urldate = {2024-10-21},
journal = {EDPACS},
volume = {69},
number = {1},
pages = {22–40},
abstract = {Artificial Intelligence (hereinafter AI), and specifically, Machine Learning (hereinafter, ML), has shown tremendous potential to revolutionize the internal audit (hereinafter, IA) profession, from enabling audit coverage of entire test populations, to introducing objectivity in the analysis of key areas. However, prior literature shows that the multiplicity of innovation options can be overwhelming. This paper aims to offer a theoretical framework that would enable audit practitioners, within both industry and professional services, to consider how ML capabilities can be harnessed to their fullest potential across the internal audit lifecycle, from audit planning to reporting. The paper discusses how DA and ML capabilities relate to the internal audit function’s (hereinafter, IAF) remit, drawing from extant literature. In doing so, the paper identifies the most specific options available to IAFs to drive innovation across each segment of the audit lifecycle, leveraging various DA and ML techniques, and supports the assertion that auditors require a continuous innovation mind-set to be effective change agents. The paper also draws out the requirement for effective guardrails, especially with emerging technology, such as Generative AI. Finally, the paper discusses how the value arising from these efforts can be measured by IAFs.},
note = {Publisher: Taylor & Francis
_eprint: https://doi.org/10.1080/07366981.2024.2312025},
keywords = {AI and Financial Systems},
pubstate = {published},
tppubtype = {article}
}
Artificial Intelligence (hereinafter AI), and specifically, Machine Learning (hereinafter, ML), has shown tremendous potential to revolutionize the internal audit (hereinafter, IA) profession, from enabling audit coverage of entire test populations, to introducing objectivity in the analysis of key areas. However, prior literature shows that the multiplicity of innovation options can be overwhelming. This paper aims to offer a theoretical framework that would enable audit practitioners, within both industry and professional services, to consider how ML capabilities can be harnessed to their fullest potential across the internal audit lifecycle, from audit planning to reporting. The paper discusses how DA and ML capabilities relate to the internal audit function’s (hereinafter, IAF) remit, drawing from extant literature. In doing so, the paper identifies the most specific options available to IAFs to drive innovation across each segment of the audit lifecycle, leveraging various DA and ML techniques, and supports the assertion that auditors require a continuous innovation mind-set to be effective change agents. The paper also draws out the requirement for effective guardrails, especially with emerging technology, such as Generative AI. Finally, the paper discusses how the value arising from these efforts can be measured by IAFs.
37.
Sklavos, George; Theodossiou, George; Papanikolaou, Zacharias; Karelakis, Christos; Ragazou, Konstantina
Environmental, Social, and Governance-Based Artificial Intelligence Governance: Digitalizing Firms’ Leadership and Human Resources Management Journal Article
In: Sustainability, vol. 16, no. 16, pp. 7154, 2024, ISSN: 2071-1050, (Number: 16 Publisher: Multidisciplinary Digital Publishing Institute).
Abstract | Links | BibTeX | Tags: AI and ESG
@article{sklavos_environmental_2024,
title = {Environmental, Social, and Governance-Based Artificial Intelligence Governance: Digitalizing Firms’ Leadership and Human Resources Management},
author = {George Sklavos and George Theodossiou and Zacharias Papanikolaou and Christos Karelakis and Konstantina Ragazou},
url = {https://www.mdpi.com/2071-1050/16/16/7154},
doi = {10.3390/su16167154},
issn = {2071-1050},
year = {2024},
date = {2024-01-01},
urldate = {2024-11-22},
journal = {Sustainability},
volume = {16},
number = {16},
pages = {7154},
abstract = {The integration of artificial intelligence (AI) with environmental, social, and governance (ESG) factors is impacting the direction of enterprises and society in our swiftly expanding world. This collaboration has significant potential to tackle critical issues such as reducing the impact of climate change, fostering social integration, and improving corporate governance. Nevertheless, the implementation of AI gives rise to intricate matters and apprehensions, as it brings out a distinct array of hazards and ethical quandaries for ESG performance. The objective of the present research is to fill this gap by gathering and offering a contemporary evaluation of the influence of advancing technologies on the strategic leadership’s role in fulfilling the business goal within the context of ESG considerations. We used bibliometric analysis to investigate the study subject using R Studio version 4.2.0 and the bibliometric applications VOSviewer version 1.6.20 and Biblioshiny version 4.2.0. We obtained data from the Scopus database and used the PRISMA approach to suitably choose 205 research publications. The results suggest that it is essential to use AI and ESG to digitize the boardroom. Additionally, it is crucial to guarantee its security using an advanced detection system. Therefore, chief executive officers (CEOs) must give priority to the issues of transparency and cybersecurity to reduce risks and successfully inspire trust in business activities.},
note = {Number: 16
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {AI and ESG},
pubstate = {published},
tppubtype = {article}
}
The integration of artificial intelligence (AI) with environmental, social, and governance (ESG) factors is impacting the direction of enterprises and society in our swiftly expanding world. This collaboration has significant potential to tackle critical issues such as reducing the impact of climate change, fostering social integration, and improving corporate governance. Nevertheless, the implementation of AI gives rise to intricate matters and apprehensions, as it brings out a distinct array of hazards and ethical quandaries for ESG performance. The objective of the present research is to fill this gap by gathering and offering a contemporary evaluation of the influence of advancing technologies on the strategic leadership’s role in fulfilling the business goal within the context of ESG considerations. We used bibliometric analysis to investigate the study subject using R Studio version 4.2.0 and the bibliometric applications VOSviewer version 1.6.20 and Biblioshiny version 4.2.0. We obtained data from the Scopus database and used the PRISMA approach to suitably choose 205 research publications. The results suggest that it is essential to use AI and ESG to digitize the boardroom. Additionally, it is crucial to guarantee its security using an advanced detection system. Therefore, chief executive officers (CEOs) must give priority to the issues of transparency and cybersecurity to reduce risks and successfully inspire trust in business activities.
38.
Sorell, Tom
AI-related data ethics oversight in UK policing Journal Article
In: Policing: A Journal of Policy and Practice, vol. 18, pp. paae016, 2024, ISSN: 1752-4520.
Abstract | Links | BibTeX | Tags: AI and Police Work
@article{sorell_ai-related_2024,
title = {AI-related data ethics oversight in UK policing},
author = {Tom Sorell},
url = {https://doi.org/10.1093/police/paae016},
doi = {10.1093/police/paae016},
issn = {1752-4520},
year = {2024},
date = {2024-01-01},
urldate = {2024-11-22},
journal = {Policing: A Journal of Policy and Practice},
volume = {18},
pages = {paae016},
abstract = {This paper considers the question of how police-related AI projects and data projects in general are normatively assessed in the UK. After locating data ethics in relation to policing ethics, I shall consider the workings of perhaps the leading regional data ethics committee in the UK. I go on to consider the approach of another committee that might in the future provide national data ethics advice for the police. Finally, I summarize the normative ethics frameworks in use in the two committees and their heavy reliance on the concepts of necessity and proportionality. I suggest that these concepts may have to be supplemented by systematic thinking about varieties of harm and the way in which severe harm may generate obligations to prevent it, where prevention may be assisted by AI models.},
keywords = {AI and Police Work},
pubstate = {published},
tppubtype = {article}
}
This paper considers the question of how police-related AI projects and data projects in general are normatively assessed in the UK. After locating data ethics in relation to policing ethics, I shall consider the workings of perhaps the leading regional data ethics committee in the UK. I go on to consider the approach of another committee that might in the future provide national data ethics advice for the police. Finally, I summarize the normative ethics frameworks in use in the two committees and their heavy reliance on the concepts of necessity and proportionality. I suggest that these concepts may have to be supplemented by systematic thinking about varieties of harm and the way in which severe harm may generate obligations to prevent it, where prevention may be assisted by AI models.
39.
Fernandez-Basso, Carlos; Gutiérrez-Batista, Karel; Gómez-Romero, Juan; Ruiz, M. Dolores; Martin-Bautista, Maria J.
An AI knowledge-based system for police assistance in crime investigation Journal Article
In: Expert Systems, vol. n/a, no. n/a, pp. e13524, 2024, ISSN: 1468-0394, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13524).
Abstract | Links | BibTeX | Tags: AI and Police Work
@article{fernandez-basso_ai_2024,
title = {An AI knowledge-based system for police assistance in crime investigation},
author = {Carlos Fernandez-Basso and Karel Gutiérrez-Batista and Juan Gómez-Romero and M. Dolores Ruiz and Maria J. Martin-Bautista},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13524},
doi = {10.1111/exsy.13524},
issn = {1468-0394},
year = {2024},
date = {2024-01-01},
urldate = {2024-11-22},
journal = {Expert Systems},
volume = {n/a},
number = {n/a},
pages = {e13524},
abstract = {The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al-Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms-related products.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13524},
keywords = {AI and Police Work},
pubstate = {published},
tppubtype = {article}
}
The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al-Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms-related products.
40.
Valeriya, Glazkova; John, Vivek; Singla, Atul; Devi, J. Yamini; Kumar, Kaushal
AI-Powered Super-Workers: An Experiment in Workforce Productivity and Satisfaction Journal Article
In: BIO Web of Conferences, vol. 86, pp. 01065, 2024, ISSN: 2117-4458, (Publisher: EDP Sciences).
Abstract | Links | BibTeX | Tags: AI and White Collar Work
@article{valeriya_ai-powered_2024,
title = {AI-Powered Super-Workers: An Experiment in Workforce Productivity and Satisfaction},
author = {Glazkova Valeriya and Vivek John and Atul Singla and J. Yamini Devi and Kaushal Kumar},
url = {https://www.bio-conferences.org/articles/bioconf/abs/2024/05/bioconf_rtbs2024_01065/bioconf_rtbs2024_01065.html},
doi = {10.1051/bioconf/20248601065},
issn = {2117-4458},
year = {2024},
date = {2024-01-01},
urldate = {2024-11-24},
journal = {BIO Web of Conferences},
volume = {86},
pages = {01065},
abstract = {In this paper, "AI-Powered Super-Workers," the revolutionary power of artificial intelligence (AI) on the workforce is empirically shown. Based on real data, the conclusions show significant shifts in work satisfaction and productivity. For example, up to 52% productivity benefits were seen in a variety of professions; one such function was that of a Sales Executive (John Smith, for example), whose productivity rose by 50% after AI integration. Job satisfaction soared, with a significant 46% improvement noted by Employee 1 (John Smith). The 20% boost in skill that Employee 2 (Sarah Johnson) demonstrated highlights the efficacy of AI-driven training. AI use patterns that highlight individual differences in AI adoption include Employee 4 (Emily Brown) using AI for 21 hours. This research may be summarized by the following keywords: AI use, workforce productivity, job satisfaction, skills advancement, and AI integration.},
note = {Publisher: EDP Sciences},
keywords = {AI and White Collar Work},
pubstate = {published},
tppubtype = {article}
}
In this paper, "AI-Powered Super-Workers," the revolutionary power of artificial intelligence (AI) on the workforce is empirically shown. Based on real data, the conclusions show significant shifts in work satisfaction and productivity. For example, up to 52% productivity benefits were seen in a variety of professions; one such function was that of a Sales Executive (John Smith, for example), whose productivity rose by 50% after AI integration. Job satisfaction soared, with a significant 46% improvement noted by Employee 1 (John Smith). The 20% boost in skill that Employee 2 (Sarah Johnson) demonstrated highlights the efficacy of AI-driven training. AI use patterns that highlight individual differences in AI adoption include Employee 4 (Emily Brown) using AI for 21 hours. This research may be summarized by the following keywords: AI use, workforce productivity, job satisfaction, skills advancement, and AI integration.