Relevant bibliographies by topics / AI Governance Framework

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI Governance Framework'

Author: Grafiati
Published: 21 December 2024
Last updated: 11 June 2025

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Journal articles on the topic "AI Governance Framework"

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Miyamoto, Michiko. "Measuring AI Governance, AI Adoption and AI Strategy of Japanese Companies." International Journal of Membrane Science and Technology 10, no. 1 (2023): 649–57. http://dx.doi.org/10.15379/ijmst.v10i1.2627.

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Purpose: This study aims to measure the level of AI governance and AI adoption among Japanese companies. Theoretical Framework: The research investigates the extent to which Japanese companies have implemented AI governance frameworks and the degree of AI adoption in their operations. The study also explores the relationship between AI governance, AI adoption, and AI strategy, providing insights into the factors that influence successful AI implementation. Design / Methodology / Approach: a survey questionnaire was administered to a representative sample of Japanese companies across various industries. The questionnaire included items that assessed the presence and effectiveness of AI governance practices within the organizations. Findings: a positive correlation was observed between AI governance and AI adoption. Companies with well-established AI governance frameworks tended to have higher levels of AI adoption, suggesting that effective governance practices play a crucial role in facilitating successful AI implementation. These findings provide valuable insights into the current state of AI governance and AI adoption among Japanese companies. Conclusion: The results can assist organizations in benchmarking their AI initiatives against industry standards and identifying areas for improvement. Policymakers and regulators can also utilize these findings to develop guidelines and frameworks that promote responsible and effective AI implementation.
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Peckham, Jeremy B. "An AI Harms and Governance Framework for Trustworthy AI." Computer 57, no. 3 (2024): 59–68. http://dx.doi.org/10.1109/mc.2024.3354040.

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Rassolov, I. M., and S. G. Chubukova. "Artificial Intelligence and Effective Governance: Legal Framework." Kutafin Law Review 9, no. 2 (2022): 309–28. http://dx.doi.org/10.17803/2713-0525.2022.2.20.309-328.

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Artificial intelligence (AI) use in the state governance structures is obviously on the rise. Cognitive technologies have potential to transform the government sector — by reducing expenses, mundane chores, coping with resource limitations, making more accurate projections, and implementing AI into an array of organizational processes and systems. Methods. General research methods: analysis, synthesis, logical method were employed to study certain concepts and legal categories and their interrelations (artificial intelligence, artificial intelligence technologies, governance system, machine-readable law, digital state, automated decision-making, etc.) and develop insights into public relations amid proactive use of artificial intelligence systems and technologies in the governance system. Comparative legal research method was used to discern dynamics and further trends in legal relations, as well as to compare approaches of foreign countries to regulating AI systems and technologies. Prognostic method was applied to project the future of the Russian legislation as concerns building effective legal framework to regulate AI systems and technologies in the governance system. Technical legal (dogmatic) method helped develop legal foundation for the use of technologies and AI systems in the governance sphere. The analysis showed promising theoretical and practical avenues of modern law development in the aspect of artificial intelligence: the concept of artificial intelligence within the conceptual legal framework was described; legal regulation of administrative processes and its specifics were defined; ethics and principles of artificial intelligence application in governance were stressed, which involves restrictions of AI use in automated decision-making; stipulating the status of informed consent in the legislation in case an automated decision is made; the procedure which allows prohibiting the use of automated decision was established, as well as the procedure of AI risk assessment in the governance system, ensuring proper data protection and independent security monitoring.
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Gajjar, Vyoma. "AGENTIC GOVERNANCE: A FRAMEWORK FOR AUTONOMOUS DECISION-MAKING SYSTEMS." International Journal of Engineering Applied Sciences and Technology 09, no. 04 (2024): 73–75. http://dx.doi.org/10.33564/ijeast.2024.v09i04.008.

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The proliferation of fake news has become a significant concern in recent years, with far-reaching consequences for individuals, communities, and society. Artificial intelligence (AI) has the potential to play a crucial role in detecting and mitigating the spread of fake news. However, the use of AI in fake news detection also raises important governance considerations. In this paper, we propose a novel approach to AI governance in fake news detection, including a framework for responsible AI governance, a new algorithm for fake news detection, and a comprehensive evaluation of the proposed approach.
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Sentinella, Richard, Maël Schnegg, and Klaus Möller. "A Management Control oriented Governance Framework for Artificial Intelligence." Die Unternehmung 77, no. 2 (2023): 162–84. http://dx.doi.org/10.5771/0042-059x-2023-2-162.

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In an age of increasing access to and power of artificial intelligence (AI), ethical concerns, such as fairness, transparency, and human well-being have come to the attention of regulators, standard setting bodies, and organizations alike. In order to build AI-based systems that comply with new rules, organizations will have to adopt systems of governance. This study develops, based on existing frameworks and a multiple case study, a governance framework specifically designed with these challenges in mind: The St. Gallen Governance Framework for Artificial Intelligence focuses on identifying stakeholder concerns and strategic goals, building a management control system, assigning roles and responsibilities, and incorporating dynamism into the system of governance.
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Sentinella, Richard, Victoria Honsel, and Mael Schnegg. "AI-Governance als strategisches Instrument." Controlling 36, no. 6 (2024): 37–40. https://doi.org/10.15358/0935-0381-2024-6-37.

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In ihrer AI-Anwendung müssen Unternehmen ethische Anforderungen wie Fairness, Transparenz und Verantwortlichkeiten mitdenken. Auch Regulierungsbehörden definieren immer striktere Regeln für die Nutzung der wirtschaftlichen und technologischen Vorteile von AI. Das St. Galler Governance Framework skizziert Schritte, um eine AI-Governance mit ethischen und rechtlichen Standards in Einklang zu bringen.
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Sistla, Swetha. "AI with Integrity: The Necessity of Responsible AI Governance." Journal of Artificial Intelligence & Cloud Computing 3, no. 5 (2024): 1–3. http://dx.doi.org/10.47363/jaicc/2024(3)e180.

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Responsible AI Governance has emerged as a critical framework for ensuring the ethical development and deployment of artificial intelligence systems. As AI technologies continue to advance and permeate various sectors of society, the need for robust governance structures becomes increasingly apparent. This document explores the key principles, challenges, and best practices in Responsible AI Governance, highlighting the importance of transparency, accountability, and fairness in AI systems. By examining current initiatives, regulatory landscapes, and industry standards, we aim to provide a comprehensive overview of the strategies organizations can employ to navigate the complex ethical terrain of AI development and implementation.
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Kolade, Titilayo Modupe, Nsidibe Taiwo Aideyan, Seun Michael Oyekunle, Olumide Samuel Ogungbemi, Dooshima Louisa Dapo-Oyewole, and Oluwaseun Oladeji Olaniyi. "Artificial Intelligence and Information Governance: Strengthening Global Security, through Compliance Frameworks, and Data Security." Asian Journal of Research in Computer Science 17, no. 12 (2024): 36–57. https://doi.org/10.9734/ajrcos/2024/v17i12528.

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This study examines the dual role of artificial intelligence (AI) in advancing and challenging global information governance and data security. By leveraging methodologies such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and Multi-Criteria Decision Analysis (MCDA), the study investigates AI-specific vulnerabilities, governance gaps, and the effectiveness of compliance frameworks. Data from the MITRE ATT&CK Framework, AI Incident Database, Global Cybersecurity Index (GCI), and National Vulnerability Database (NVD) form the empirical foundation for this analysis. Key findings reveal that AI-driven data breaches exhibit the highest regulatory scores (0.72) and dependency levels (0.81), underscoring the critical need for robust compliance frameworks in high-risk AI environments. PCA identifies regulatory gaps (45.3% variance) and AI technology type (30.2% variance) as significant factors influencing security outcomes. SEM highlights governance strength as a primary determinant of security effectiveness (coefficient = 0.68, p < 0.001), while MCDA underscores the importance of adaptability in governance frameworks for addressing AI-specific threats. The study recommends adopting quantum-resistant encryption, enhancing international cooperation, and integrating AI automation with human oversight to fortify governance structures. These insights provide actionable strategies for policymakers, industry leaders, and researchers to navigate the complexities of AI governance and align technological advancements with ethical and security imperatives in a rapidly evolving digital landscape.
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Wagner, Jennifer K., Megan Doerr, and Cason D. Schmit. "AI Governance: A Challenge for Public Health." JMIR Public Health and Surveillance 10 (September 30, 2024): e58358-e58358. http://dx.doi.org/10.2196/58358.

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Abstract The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge. The benefits of public health engagement in AI governance could be extensive. Here, we describe how several public health concepts can enhance AI governance. Specifically, we explain how (1) harm reduction can provide a framework for navigating the governance debate between traditional regulation and “soft law” approaches; (2) a public health understanding of social determinants of health is crucial to optimally weigh the potential risks and benefits of AI; (3) public health ethics provides a toolset for guiding governance decisions where individual interests intersect with collective interests; and (4) a One Health approach can improve AI governance effectiveness while advancing public health outcomes. Public health theories, perspectives, and innovations could substantially enrich and improve AI governance, creating a more equitable and socially beneficial path for AI development.
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Adebola Folorunso, Kehinde Olanipekun, Temitope Adewumi, and Bunmi Samuel. "A policy framework on AI usage in developing countries and its impact." Global Journal of Engineering and Technology Advances 21, no. 1 (2024): 154–66. http://dx.doi.org/10.30574/gjeta.2024.21.1.0192.

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The rapid growth of Artificial Intelligence (AI) presents both significant opportunities and challenges for developing countries. A well-structured policy framework is crucial to maximize the benefits of AI while mitigating its risks. This review proposes a comprehensive AI policy framework tailored to developing countries, emphasizing the need for robust infrastructure, capacity building, ethical governance, and economic incentives. Key elements include the development of digital infrastructure, education and training programs to enhance AI literacy, and ethical guidelines to ensure fairness and transparency in AI applications. Data governance and privacy protections are critical, particularly in countries where regulatory frameworks are underdeveloped. Furthermore, international cooperation is highlighted as a necessity for aligning local policies with global AI standards, facilitating cross-border data sharing, and ensuring equitable access to AI innovations. The potential impact of AI on economic growth, job creation, healthcare, education, and public service delivery is profound, yet challenges such as workforce displacement, increased inequality, and the digital divide must be carefully managed. The proposed framework addresses these challenges, providing strategies to overcome barriers to AI adoption, including financial constraints, governance issues, and unequal access to technology. Moreover, it stresses the importance of fostering public-private partnerships and ensuring that AI development is inclusive, benefiting all segments of society. By implementing a comprehensive AI policy framework, developing countries can harness AI’s transformative power to drive sustainable development, improve social outcomes, and strengthen their economic standing in the global landscape. This review concludes by recommending continuous policy evaluation and adaptation to keep pace with AI's rapid evolution.
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Dissertations / Theses on the topic "AI Governance Framework"

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Haidar, Ahmad. "Responsible Artificial Intelligence : Designing Frameworks for Ethical, Sustainable, and Risk-Aware Practices." Electronic Thesis or Diss., université Paris-Saclay, 2024. https://www.biblio.univ-evry.fr/theses/2024/interne/2024UPASI008.pdf.

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L'intelligence artificielle (IA) transforme rapidement le monde, redéfinissant les relations entre technologie et société. Cette thèse explore le besoin essentiel de développer, de gouverner et d'utiliser l'IA et l'IA générative (IAG) de manière responsable et durable. Elle traite des risques éthiques, des lacunes réglementaires et des défis associés aux systèmes d'IA, tout en proposant des cadres concrets pour promouvoir une Intelligence Artificielle Responsable (IAR) et une Innovation Numérique Responsable (INR).La thèse commence par une analyse approfondie de 27 déclarations éthiques mondiales sur l'IA pour identifier des principes dominants tels que la transparence, l'équité, la responsabilité et la durabilité. Bien que significatifs, ces principes manquent souvent d'outils pratiques pour leur mise en œuvre. Pour combler cette lacune, la deuxième étude de la recherche présente un cadre intégrateur pour l'IAR basé sur quatre dimensions : technique, IA pour la durabilité, juridique et gestion responsable de l'innovation.La troisième partie de la thèse porte sur l'INR à travers une étude qualitative basée sur 18 entretiens avec des gestionnaires de secteurs divers. Cinq dimensions clés sont identifiées : stratégie, défis spécifiques au numérique, indicateurs de performance organisationnels, impact sur les utilisateurs finaux et catalyseurs. Ces dimensions permettent aux entreprises d'adopter des pratiques d'innovation durable et responsable tout en surmontant les obstacles à leur mise en œuvre.La quatrième étude analyse les risques émergents liés à l'IAG, tels que la désinformation, les biais, les atteintes à la vie privée, les préoccupations environnementales et la suppression d'emplois. À partir d'un ensemble de 858 incidents, cette recherche utilise une régression logistique binaire pour examiner l'impact sociétal de ces risques. Les résultats soulignent l'urgence d'établir des cadres réglementaires renforcés, une responsabilité numérique des entreprises et une gouvernance éthique de l'IA.En conclusion, cette thèse apporte des contributions critiques aux domaines de l'INR et de l'IAR en évaluant les principes éthiques, en proposant des cadres intégratifs et en identifiant des risques émergents. Elle souligne l'importance d'aligner la gouvernance de l'IA sur les normes internationales afin de garantir que les technologies d'IA servent l'humanité de manière durable et équitable<br>Artificial Intelligence (AI) is rapidly transforming the world, redefining the relationship between technology and society. This thesis investigates the critical need for responsible and sustainable development, governance, and usage of AI and Generative AI (GAI). The study addresses the ethical risks, regulatory gaps, and challenges associated with AI systems while proposing actionable frameworks for fostering Responsible Artificial Intelligence (RAI) and Responsible Digital Innovation (RDI).The thesis begins with a comprehensive review of 27 global AI ethical declarations to identify dominant principles such as transparency, fairness, accountability, and sustainability. Despite their significance, these principles often lack the necessary tools for practical implementation. To address this gap, the second study in the research presents an integrative framework for RAI based on four dimensions: technical, AI for sustainability, legal, and responsible innovation management.The third part of the thesis focuses on RDI through a qualitative study of 18 interviews with managers from diverse sectors. Five key dimensions are identified: strategy, digital-specific challenges, organizational KPIs, end-user impact, and catalysts. These dimensions enable companies to adopt sustainable and responsible innovation practices while overcoming obstacles in implementation.The fourth study analyzes emerging risks from GAI, such as misinformation, disinformation, bias, privacy breaches, environmental concerns, and job displacement. Using a dataset of 858 incidents, this research employs binary logistic regression to examine the societal impact of these risks. The results highlight the urgent need for stronger regulatory frameworks, corporate digital responsibility, and ethical AI governance. Thus, this thesis provides critical contributions to the fields of RDI and RAI by evaluating ethical principles, proposing integrative frameworks, and identifying emerging risks. It emphasizes the importance of aligning AI governance with international standards to ensure that AI technologies serve humanity sustainably and equitably
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Rusu, Anca. "Delving into AI discourse within EU institutional communications : empowering informed decision-making for tomorrow’s tech by fostering responsible communication for emerging technologies." Electronic Thesis or Diss., Université Paris sciences et lettres, 2023. https://basepub.dauphine.fr/discover?query=%222023UPSLD029%22.

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La prolifération des technologies émergentes, définies comme de nouvelles technologies ou de nouvelles utilisations de technologies anciennes (par exemple, l'intelligence artificielle (IA)), offre à la société à la fois des opportunités et des défis lors de leur utilisation. Ces technologies promettent de révolutionner divers secteurs en apportant de nouvelles efficacités, capacités et perspectives, ce qui les rend intéressantes à développer et à utiliser. Cependant, leur utilisation soulève également d'importantes préoccupations éthiques, environnementales et sociales. Les organisations communiquent par le biais de divers modes, dont l'un est le discours écrit. Un tel discours englobe non seulement la structure du message, mais aussi son contenu. En d'autres termes, le vocabulaire (la structure) est utilisé pour exprimer un point de vue spécifique (le contenu). Dans le domaine de l'utilisation des technologies, il existe un lien évident entre la communication et la prise de décision éclairée, car l'information sur la technologie (sa forme et sa substance) est diffusée par le biais de la communication, ce qui contribue à prendre des décisions éclairées. Cette thèse adopte une approche de gouvernance des risques, qui implique une perspective préventive visant à minimiser (ou à éviter) les risques potentiels futurs. Cette perspective reconnaît l'importance des individus prenant des décisions éclairées concernant l'acceptation ou l'action face aux risques futurs potentiels. Il convient de noter que les décisions des individus sont influencées par ce qu'ils savent sur une technologie et par leurs perceptions (ce qu'ils ne savent pas mais croient).Ainsi, notre recherche vise à explorer les perspectives théoriques sur les responsabilités de communication des organisations et les pratiques réelles employées par ces entités. Ce choix découle du manque apparent dans la littérature concernant la communication responsable et de la nécessité d'examiner ce sujet, en mettant l'accent sur les considérations pratiques pour définir davantage les modes de communication organisationnelle à analyser et à prendre des mesures proactives lors de la communication sur les technologies émergentes telles que l'IA. Lorsqu'une organisation communique sur une technologie émergente, on trouve dans la littérature des éléments mettant l'accent sur la responsabilité de partager des informations, mais aucun sur la responsabilité (considérée comme un comportement éthique) d'une organisation concernant l'impact de ce qui est communiqué sur le processus de prise de décision. Une certaine responsabilité est liée à la responsabilité sociale des entreprises (RSE), mais l'accent reste sur l'information. Nous proposons un concept qui aborde l'intersection entre trois domaines considérés : les technologies émergentes, la communication organisationnelle et la gouvernance des risques, à savoir celui de la Communication Organisationnelle Responsable sur les Technologies Émergentes (ROCET) pour aborder la responsabilité de ce qui est communiqué en tant que comportement éthique. Nous visons à approfondir ce concept en comblant le fossé entre la théorie et la pratique, en examinant les deux simultanément pour obtenir une compréhension globale. Deux analyses seront menées en parallèle : une revue critique de la littérature autour du concept de "communication responsable" et une analyse de discours de rapports autonomes publiés par des organismes gouvernementaux concernant l'utilisation d'une technologie émergente spécifique, à savoir l'intelligence artificielle (IA). La littérature se concentre soit sur la communication menée par les organisations dans le cadre de leur stratégie de responsabilité sociale, soit du point de vue de la théorie de la communication, en se concentrant sur la manière de transmettre efficacement un message<br>The proliferation of emerging technologies, which are defined as new technologies or new use of old technologies (for example, artificial intelligence (AI)), presents both opportunities and challenges for society when used. These technologies promise to revolutionize various sectors, providing new efficiencies, capabilities, and insights, which makes them interesting to develop and use. However, their use also raises significant ethical, environmental, and social concerns. Organizations communicate through various modes, one of which is written discourse. Such discourse encompasses not only the structure of the message but also its content. In other words, the vocabulary (the structure) is used to express a specific point of view (the content). Within technology usage, there is a clear connection between communication and informed decision-making, as the information about the technology (its form and substance) is spread through communication, which in turn aids in making well-informed decisions. This thesis adopts a risk governance approach, which involves taking a preventive perspective to minimize (or avoid) future potential risks. This viewpoint acknowledges the importance of people making informed decisions about accepting or acting in light of potential future risks. It is to be noted that people's decisions are influenced by what they know about a technology and their perceptions (what they do not know but believe). Hence, our research aims to explore the theoretical perspectives on organizations' communication responsibilities and the actual practices employed by these entities. This choice stems from the apparent gap in the literature concerning responsible communication and the necessity to examine the topic, emphasizing practical considerations for further defining modes of organizational communication to analyze and take proactive action when communicating about emerging technologies such as AI. When an organization communicates about an emerging technology, elements focusing on the responsibility of sharing information can be found in the literature, but none on the responsibility (seen as an ethical behavior) of one organization regarding the impact of what is communicated on the decision-making process. Some responsibility is linked to corporate social responsibility (CSR), but the focus remains on the information. We propose a concept that addresses the intersection between three considered fields: emerging technologies, organizational communication and risk governance, which is the one of Responsible Organizational Communication on Emerging Technologies (ROCET) to address the responsibility of what is communicated as an ethical behavior. We aim to delve into the concept by bridging the divide between theory and practice, examining both simultaneously to garner a comprehensive understanding. This approach will help construct an understanding that meets halfway, building on knowledge accumulated from both areas. Therefore, two analyses will be conducted in parallel: a critical literature review around the “responsible communication” concept and a discourse analysis of standalone reports published by governmental bodies regarding the use of a specific emerging technology, namely artificial intelligence (AI). Using a single case analysis approach, the analysis aims to problematize one's communication regarding a public discourse while challenging such constitutions by exploring models of responsible communication. There is a gap in the literature in referring to this term as this research does. The literature focuses either on the communication conducted by organizations as part of their corporate responsibility strategy or from a communication theory perspective, concentrating on how to convey a message effectively. Alternatively, it looks at the matter from the emerging technologies perspective, where the focus is on information communication referring to the technology
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Books on the topic "AI Governance Framework"

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Taddeo, Mariarosaria. The Ethics of Artificial Intelligence in Defence. Oxford University Press, 2024. http://dx.doi.org/10.1093/oso/9780197745441.001.0001.

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Abstract The volume establishes an ethical framework for the identification, analysis, and resolution of ethical challenges that arise from the uses of artificial intelligence (AI) in defence, ranging from intelligence analysis to cyberwarfare and autonomous weapon systems. It does so with the goal of advancing the relevant debate and to inform the ethical governance of AI in defence. Centring on the autonomy and learning capabilities of AI technologies, the work is rooted in AI ethics and Just War Theory. It provides a systemic conceptual analysis of the different uses of AI in defence and their ethical implications, proposes ethical principles and a methodology for their implementation in practice. It then translates this analysis into actionable recommendations for decision-maker and policymakers to foster ethical governance of AI in the defence sector.
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Pagallo, Ugo. New Laws of Outer Space. Hart Publishing, 2024. http://dx.doi.org/10.5040/9781509976218.

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This book maps out the moral, legal and societal issues brought forth by the use of autonomous systems such as AI and smart robots in outer space. Humanity is on the brink of a new space era in which projects for permanent human colonies on the Moon and space missions with autonomous AI systems will soon become a reality. Principles and provisions of international space law fall increasingly short in tackling this scenario. Experts and institutions have recommended improvements to the legal framework, such as new international agreements, or policies that would not require any amendment to conventional law. Most of the time, such proposals and recommendations overlook the challenges posed by technology and how autonomous and intelligent systems in outer space require moral and legal standards of their own. This book argues that the traditional focus on satellite communications, space-related services, and the appropriability of celestial resources needs to be integrated by new laws of outer space regulating cybersecurity law and environmental law, data governance and consumer protection. The new laws of outer space will increasingly concern the development of new standards for the behaviour and decision-making of AI systems and smart robots, with and without humans aboard deep space missions and in next-generation colonies. What laws shall govern us out there, in a new terra incognita? This is the question that the book sets out to answer.
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BAHADUR TIWARI, BHUPENDRA, E. ESWARA REDDY, and SAM X. KINGSLEY JOSHUA. INNOVATIVE HUMAN RESOURCE PRACTICES AND EMPLOYEE ENGAGEMENT WITH SPECIAL REFERENCE TO IT SECTOR. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-79-2.

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The industry of information technology in India includes the following services namely IT and software services, IT enabled services, hardware (engineering) services, and e-businesses/e-governance associated with government services. IT services are outsourcing of software support/installation, processing services, systems integration, exports of products and services, and training/education of the information technology science. The significant improvements in the industry have brought about a vital need for systematic process of managing the majority of employees in the IT industry. There was also a need created for technology in the subject matter of managing the employees and other aspects that came into picture. Hence, Innovative Human Resource practices came into existence for upgrading the skills and building the employees to work towards the goal of the organization. This gave birth to HR technology, Employee Engagement, ERP and so on. The study focuses on identifying various applications of Innovative Human resource practices in IT industry, the role of demographics and the factors influencing employee engagement and productivity. The study also analyzes the impact of innovative human resource practices on employee engagement and productivity and finally examines the mediating role of employee engagement upon the relationship between innovative human resource practices and employee productivity. To support the study, review of the relevant literature (Books, Research thesis and research papers) available in the innovative human resource practices space (both Global and Indian) was done. The research gap was identified in 4 categories i.e. empirical gap, evidence gap, methodological gap and population gap. The conceptual framework for the study was also designed. The literature review was categorized into national and international, theoretical and empirical to keep the study relevant according to the current global standards. Based on the research gap and the conceptual framework, the questionnaire was framed and according to the hypothesis the plan of analysis was structured to further the study. The data collection was completed through offline and online method, based on sample design. The analysis included Structural Equation Model, ANOVA, Independent t test and Mediation analysis – Andrew Hayes, Model 4 using SPSS and AMOS software. The study found out that HR Technology, HR Analytics, Collaboration Tools, AI in HR and Employee Pulse survey, are contributors to Innovative Human resource practices but there is no significant impact of demographic variables on perception of IHRM. Also, Employee retention, Reward and recognition, Personality development and Performance appraisal are factors influencing Employee engagement and Innovative work system, Employee contribution, Vigour, Dedication, Psychological factors, Motivational factors, Experience Factors and Individual capacity are factors influencing Employee Productivity. IHRM has significant impact on employee engagement and the employee productivity. Employee engagement mediates the relationship between IHRM and employee productivity. To conclude, this study provides insights into how employees are affected by innovative HR practices and provides practical solutions for organizations looking to encourage staff. By using motivational strategies that are directly tied to employees’ immediate interests and that are intended to affect their views and attitudes, innovative HR practices can assist firms in projecting a sense of employee engagement. Employees are further encouraged to be selfless and altruistic by the degrees of perceived satisfaction with the creative HR methods. As a result, they become more open to doing tasks that aren’t directly relevant to their professions but nevertheless helpful to their businesses. This would increase the efficiency of enterprises in managing their human resources, particularly those businesses that are team-based. Keywords: Innovative Human Resource Practices, Employee Engagement, Employee Productivity, IT Sector, Bengaluru, Human Resource Technology, Trends of IHRM, Innovative Human Resource Technology tools, IHRM Strategies, Information Technology.
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Book chapters on the topic "AI Governance Framework"

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Choung, Hyesun, Prabu David, and John S. Seberger. "A Multilevel Framework for AI Governance." In The Routledge Handbook of Global and Digital Governance Crossroads. Routledge India, 2024. http://dx.doi.org/10.4324/9781003316077-25.

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Hawkins, William. "Guardians of Intelligence: Building a Robust AI Governance Framework." In AI Essentials Guide. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0911-8_7.

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Pery, Andrew, Majid Rafiei, Michael Simon, and Wil M. P. van der Aalst. "Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_29.

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AbstractThe premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accountability, and confidentiality requirements. These processes involve multiple steps, hand-offs, re-works, and human-in-the-loop oversight. In this paper, we demonstrate that process mining can provide a useful framework for gaining fact-based visibility to AI compliance process execution, surfacing compliance bottlenecks, and providing for an automated approach to analyze, remediate and monitor uncertainty in AI regulatory compliance processes.
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Husain, Coovadia, Marx Benjamin, and Ilse Botha. "Identifying AI Corporate Governance Principles That Should Be Prevalent in a Governance Framework for Business." In Towards Digitally Transforming Accounting and Business Processes. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46177-4_15.

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Lei, Yuxiao, Yucong Duan, and Mengmeng Song. "Technical Implementation Framework of AI Governance Policies for Cross-Modal Privacy Protection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67540-0_27.

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Nikolinakos, Nikos Th. "Reforming the EU Civil Liability Framework Applicable to Artificial Intelligence and Other Emerging Digital Technologies: The Proposed AI Liability Directive." In Law, Governance and Technology Series. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-67969-8_8.

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Misuraca, Gianluca, and Gianluigi Viscusi. "AI-Enabled Innovation in the Public Sector: A Framework for Digital Governance and Resilience." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57599-1_9.

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Lee, Michelle Seng Ah, Luciano Floridi, and Alexander Denev. "Innovating with Confidence: Embedding AI Governance and Fairness in a Financial Services Risk Management Framework." In Philosophical Studies Series. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81907-1_20.

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Kritikos, Mihalis. "Artificial Intelligence (AI) in a Time of Pandemics: Developing Options for the Ethical Governance of COVID-19 AI Applications." In Research Ethics Forum. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15746-2_13.

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AbstractThis chapter analyses the various applications of artificial intelligence (AI) developed in the context of the COVID-19 pandemic and examines the range of ethical questions that their multi-level deployment may raise. Within this frame, the author sheds light on the challenges posed by the fast-tracking authorization of some of the AI systems and pays particular attention to the form and shape that ‘emergency response’ in the field of ethics has taken in order to cope with these extraordinary challenges and the ethical practices that have been developed thus far. The chapter will also provide a detailed set of policy suggestions to overcome these challenges with a special focus on the need to develop an emergency ethics framework that will allow policy-makers to authorize the deployment of AI-powered tools in a responsible and trustworthy manner.
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Nesi, Paolo. "Valutazione della propensione alla mediazione tramite eXplainable AI." In Giustizia sostenibile. Firenze University Press, 2024. http://dx.doi.org/10.36253/979-12-215-0316-6.13.

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Mediation in civil trials can effectively resolve disputes outside of court proceedings, easing the burden on the courts if successful. Efficiency in identifying disputes is essential, as a failed attempt at mediation can lengthen the duration of the trial. The decision rests with the judge/tribunal on the basis of numerous documents that contain certain statements significant to the decision. This paper describes an artificial intelligence, AI, solution to provide a decision support system that can process documents and (i) produce reliable suggestions, (ii) produce substantiated reasons by highlighting the statements that led to the suggestion, and (iii) respect privacy and data security. Explainable AI techniques (XAI) technologies were used for this purpose, resulting in a solution that meets the defined objectives. The solution was developed as part of the research project "Agile Justice," funded in the Italian National Governance and Institutional Capacity NOP, and validated against real cases. The solution leveraged the Snap4City framework for data management and AI/XAI solution.
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Conference papers on the topic "AI Governance Framework"

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Teguh, Andy, Joko Slamet, Hartono Saputro, Kelly Sungkono, and Riyanarto Sarno. "Optimizing IT Governance and Project Management in Software Development through AI Integration and COBIT 2019 Framework." In 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA). IEEE, 2024. https://doi.org/10.1109/ictiia61827.2024.10761914.

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Sidorova, Anna, and Kashif Saeed. "Incorporating Stakeholder enfranchisement, Risks, Gains, and AI decisions in AI Governance Framework." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2022. http://dx.doi.org/10.24251/hicss.2022.722.

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Rajagopal, Manikandan, Ramkumar Sivasakthivel, Gobinath Ramar, Mansurali A, and Sathesh Kumar Karuppasamy. "A Conceptual Framework for AI Governance in Public Administration – A Smart Governance Perspective." In 2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2023. http://dx.doi.org/10.1109/i-smac58438.2023.10290366.

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Fares, Nadine Y., Denis Nedeljkovic, and Manar Jammal. "AI-enabled IoT Applications: Towards a Transparent Governance Framework." In 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). IEEE, 2023. http://dx.doi.org/10.1109/gcaiot61060.2023.10385106.

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Alzeqri, Aziz. "Artificial Intelligence in Non-Clinical Functions: A Strategic Framework for Healthcare Organizations." In The Integration of AI and Technology in Modern Business Practices. SBS Swiss Business School, 2024. http://dx.doi.org/10.70301/conf.sbs-jabr.2024.1/1.4.

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Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in clinical applications such as diagnostics and personalized treatment. However, the application of AI in non-clinical areas, such as operational efficiency, data governance, and data monetization, remains underexplored. This paper addresses this gap by proposing an AI-driven framework for healthcare organizations, synthesizing existing literature on AI applications and data management. Using a qualitative approach, this study identifies six key areas where AI can enhance non-clinical operations: data governance and quality management, technological infrastructure and scalability, leadership and workforce development, operational efficiency, data monetization, and ethical considerations. The framework provides a strategic framework for healthcare organizations to adopt AI technologies effectively while ensuring compliance with local and international regulations. This paper contributes to the growing body of research by offering practical solutions for leveraging AI to improve healthcare administration and create new revenue streams through data valorization.
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Correia, Anacleto, and Pedro B. Água. "Enhancing corporate governance: A conceptual approach to artificial intelligence usage." In New outlooks for the scholarly research in corporate governance. Virtus Interpress, 2023. http://dx.doi.org/10.22495/nosrcgp23.

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In this exploratory study, we examine the intersection of corporate governance (CG) and artificial intelligence (AI), addressing the essential question: how can AI be utilized to improve ethical and transparent decision-making in the corporate endeavour? Using current research on organizational governance, AI ethics, and data science, our study examines the potential of AI to augment conventional governance mechanisms, as well as the ethical dilemmas and challenges it may present. We propose a conceptual framework based on the principles of separation of ownership and control while considering data ethics, which will be supported and validated in the future by an empirical study.
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Mikhail, Bundin, Martynov Aleksei, and Shireeva Ekaterina. "On the Way to Legal Framework for AI in Public Sector." In ICEGOV '18: 11th International Conference on Theory and Practice of Electronic Governance. ACM, 2018. http://dx.doi.org/10.1145/3209415.3209448.

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Ortega, Eduardo, Michelle Tran, and Grace Bandeen. "AI Digital Tool Product Lifecycle Governance Framework through Ethics and Compliance by Design†." In 2023 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2023. http://dx.doi.org/10.1109/cai54212.2023.00155.

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Hoyas, Sergio. "ASDG — An AI-based framework for automatic classification of impact on the SDGs." In ICEGOV 2022: 15th International Conference on Theory and Practice of Electronic Governance. ACM, 2022. http://dx.doi.org/10.1145/3560107.3560128.

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Ali, Umar, and Cenk Calis. "Centralized Smart Governance Framework Based on IoT Smart City Using TTG-Classified Technique." In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT). IEEE, 2019. http://dx.doi.org/10.1109/honet.2019.8908070.

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Reports on the topic "AI Governance Framework"

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Aiken, Catherine. Classifying AI Systems. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200025.

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This brief explores the development and testing of artificial intelligence system classification frameworks intended to distill AI systems into concise, comparable and policy-relevant dimensions. Comparing more than 1,800 system classifications, it points to several factors that increase the utility of a framework for human classification of AI systems and enable AI system management, risk assessment and governance.
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Hulin, Anne-Sophie, Anita Burgun, Stéphanie Combes, et al. Between Data Governance and Artificial Intelligence : What Place for the Pursuit of the General Interest? : Proceedings of the Closing Conference of the Chair for Social Justice and AI. Observatoire international sur les impacts sociétaux de l'intelligence artificielle et du numérique, 2024. http://dx.doi.org/10.61737/neag1390.

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This collection is set within a contemporary context where defining the directions and objectives for the governance framework of data and artificial intelligence is of paramount importance. The contributions gathered here illustrate some of the discussions that took place at the symposium organized by the Social Justice and Artificial Intelligence Chair, held on February 8, 2024, at the École Normale Supérieure in Paris. Bringing together experts from a variety of fields, the symposium aimed to explore some of the points of intersection between data, AI, and the general interest. This leads to the following questions: to what extent can the pursuit of the general interest legitimize digital innovation? How can it influence its regulation? What social perspectives and/or ways of tempering current practices does it offer from the point of view of data use and AI development? In other words: between data governance and artificial intelligence: what is the place of the pursuit of the general interest?
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Atabey, Ayça, Cory Robinson, Anna Lindroos Cermakova, Andra Siibak, and Natalia Ingebretsen Kucirkova. Ethics in EdTech: Consolidating Standards For Responsible Data Handling And Usercentric Design. University in Stavanger, 2024. http://dx.doi.org/10.31265/usps.283.

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This report proposes aspirational principles for EdTech providers, emphasizing ethical practices, robust data protection, ownership rights, transparent consent processes, and active user engagement, particularly with children. These measures aim to enhance transparency, accountability, and trust in EdTech platforms. Focusing on the K12 sector, the report systematically reviews and integrates key academic, legal, and technical frameworks to propose ethical benchmarks for the EdTech industry. The benchmarks go beyond quality assurance, highlighting good practices and ethical leadership for the field. The report addresses the need for a new culture in EdTech ethics, one that is collaborative and views EdTech providers as partners in dialogue with researchers and policy-makers to identify constructive solutions and uphold social trust. The outlined benchmarks are intended for national policymakers, international agencies, and certification bodies to consider when developing quality standards for EdTech used in schools. They include AI safeguards and stress the importance of meeting international data protection standards, establishing clear ownership rights, and implementing transparent consent processes to address data control issues, as well as active user engagement for improving data governance practices.
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