AI Governance Research Articles

The Public Interest in the Digital Age: Exploring the Emerging Roles and Governance Models of the AI as a Common Good

With the continuous development of artificial intelligence, we are living through the era of digital transformation, and the fourth Industrial Revolution is a paradigm change with unprecedented speed, scale, scope and complexity, and is fundamentally transforming production, consumption and society as a whole. As the basic unit of public management research, data has established a strong connection between the promotion of digital revolution and the change of public management research paradigm. The rise of AI technologies has enhanced AI governance and brought unprecedented help to policymakers. This study has used a cross-sectional quantitative design to capture perception and attitude towards AI as common good from a sample of 400 Chinese responders coming from diverse age, group and occupation categories. The key factors which measured were public awareness of AIGC, the extent of its utilization in government policy, public attitudes toward AIGC, and public participation in policy development. The findings indicated that public understanding and trust are paramount to successfully integrate Ai as common good in governance framework. Public enthusiasm and participations are highly affected by trust and public understanding. To deliver policies without much resistance, heightened awareness, participations and trust becomes essential.

AI-Powered Information Governance: Balancing Automation and Human Oversight for Optimal Organization Productivity

This study employs a mixed-methods approach to examine the optimal balance between AI-powered automation and human oversight in information governance frameworks, aiming to enhance organizational productivity, efficiency, and compliance. Quantitative data collected from 384 respondents were analyzed using Pearson correlation, regression models, and Structural Equation Modeling (SEM). The results reveal strong positive correlations between AI automation levels and both organization size (r = 0.55, p < .01) and AI adoption duration (r = 0.62, p < .01). Regression analysis indicates that higher levels of AI automation significantly improve error reduction (β = 1.12, p < .001) and compliance (β = 1.05, p < .001), especially in larger organizations with longer AI adoption periods. SEM findings highlight that human oversight positively impacts error reduction (β = 0.65, p < .001) and compliance improvement (β = 0.72, p < .001), and the interaction between human oversight and AI automation further enhances these outcomes (error reduction: β = 0.32, p < .001; compliance improvement: β = 0.35, p < .001). The qualitative analysis, involving thematic extraction from industry reports, reveals ethical challenges such as data quality issues, algorithmic bias, and privacy concerns. Hence, it is necessary to integrate human oversight to ensure ethical standards and build stakeholder trust in AI-driven systems. The study concludes with practical recommendations for organizations: establishing transparent AI governance frameworks, investing in continuous training for employees, and regularly auditing AI processes to mitigate risks. By addressing both the technological and ethical dimensions, organizations can implement AI-powered information governance that not only boosts productivity and efficiency but also ensures compliance and ethical integrity.

Frontrunner model for responsible AI governance in the public sector: the Dutch perspective

AbstractAcross the European Union, considerable discrepancies can be observed regarding the current state of AI adoption in the public sector and the complexity of functioning AI governance structures. This can be attributed to diverse levels of digitalisation, AI maturity and governance styles across EU member states. In the field of AI implementation and AI governance models in the public sector the frontrunner is the Netherlands, scoring first in the Global Index on Responsible AI. Analysing this example of good practices in terms of AI governance, with a focus on the delegation acceptance perspective, is of relevance for the state of art on AI governance within the EU. The article looks into the structure of the public Dutch Algorithm Register which currently contains over 400 entries, the AI framework for the public sector, supervisory structures in place and risks management approaches, addressing the importance of values in the development and deployment of AI systems and algorithms. The article demonstrates how in the case of AI also, early adaptors shape future behaviours, thus carrying a burden of responsibility when developing and deploying key enabling technologies in line with the core values.

The Salesforce Einstein Trust Layer for Retrieval-Augmented Generation (RAG) for Enterprise Applications

Generative AI has the potential to revolutionize the enterprise workflows to great extent but it poses privacy, security, and data governance challenges. Companies who want to utilize advanced AI models like Large Language Models, are only allowed to do so under the guidelines of security and regulatory frameworks. Salesforce Einstein Trust Layer proposes a solution to these challenges by not only setting up a trusted layer for deploying Retrieval-Augmented Generation (RAG) models but also ensures that the data privacy standards are met while delivering the AI generated responses. This paper discusses how the Einstein Trust Layer facilitates the safe practical application of RAG in enterprise systems, including its general architecture, functionality, and the precise processes that demonstrate why the Einstein Trust Layer is a reliable means of incorporating LLMs into commercial processes. Keywords: Salesforce Einstein Trust Layer, Retrieval-Augmented Generation (RAG), Generative AI, Large Language Models (LLMs), Data Privacy, AI Governance, Enterprise AI, Data Masking, Toxicity Scoring, Dynamic Grounding, Zero-Data Retention, AI Compliance

Using AI Governance on Fake News Detection: A Novel Approach

Using AI Governance on Fake News Detection: A Novel Approach

A Bourdieusian theory on communicating an opinion about AI governance

A Bourdieusian theory on communicating an opinion about AI governance

When Should Algorithms Resign? A Proposal for AI Governance

When Should Algorithms Resign? A Proposal for AI Governance

Contesting the public interest in AI governance

This article argues that public contestability is a critical attribute of governance arrangements designed to align AI deployment with the public interest. Mechanisms to collectively contest decisions which do not track public interests are an important guardrail against erroneous, exclusionary, and arbitrary decision-making. On that basis, we suggest that efforts to align AI to the public interest through democratic participation will benefit substantially from strengthening capabilities for public contestation outside aggregative and deliberative processes. We draw on insights from democratic and regulatory theory to explore three underlying requirements for public contestability in AI governance: (1) capabilities to organise; (2) separation of powers; and (3) access to alternative and independent information. While recognising that suitable mechanisms for contestability will vary by system and context, we sketch out some possibilities for embedding public contestability in AI governance frameworks with a view to provoking further discussion on institutional design.

AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities

Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher education institutions (HEIs) becomes increasingly important. Leading universities have already published guidelines on Generative AI, with most attempting to embrace this technology responsibly. This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines. Through a case study of 14 prestigious universities in the United States, we identified the multi-unit governance of AI, the role-specific governance of AI, and the academic characteristics of AI governance from their AI guidelines. The strengths and potential limitations of these strategies and characteristics are discussed. The findings offer practical implications for guiding responsible AI usage in HEIs and beyond.

AI governance systems: a multi-scale analysis framework, empirical findings, and future directions

AI governance systems: a multi-scale analysis framework, empirical findings, and future directions

International governance of advancing artificial intelligence

AbstractNew technologies with military applications may demand new modes of governance. In this article, we develop a taxonomy of technology governance forms, outline their strengths, and red-team their weaknesses. In particular, we consider the challenges and opportunities posed by advancing artificial intelligence, which is likely to have substantial dual-use properties. We conclude that subnational governance, though prevalent and mitigating some risks, is insufficient when the individual rewards from societally harmful actions outweigh normative sanctions, as is likely to be the case with AI. Nationally enforced standards are promising ways to govern AI deployment, but they are less viable in the “race-to-the-bottom” environments that are becoming common. When it comes to powerful technologies with military implications, there is only one multilateral option with a strong historical precedent: a non-proliferation plus norms-of-use regime, which we call NPT+. We believe that a non-proliferation regime may, therefore, be the necessary foundation for AI governance. However, AI may exhibit characteristics that would make a non-proliferation regime less effective than it has proven for nuclear weapons. As an alternative, verification-backed restrictions on AI development and use would address more risks, but they face challenges in the case of advanced AI, and we show how these challenges may not have technical solutions. Perhaps more importantly, we show that there is no clear example of major powers restricting the development of a powerful military technology when that technology lacks a ready substitute. We, therefore, turn to a final alternative, International Monopoly, which was the preferred solution of many scholars and policymakers in the early nuclear era. It should be considered again for governing AI: a monopoly would require less-invasive monitoring, though at the possible cost of eroding national sovereignty. Ultimately, we conclude that it is too soon to tell whether a non-proliferation regime, a verification-based regime, or an International Monopoly is most feasible for governing AI. Nonetheless, a variety of policies would yield a high return across all three scenarios, and we conclude by identifying some of these steps that could be taken today.

What drives the corporate payoffs of using generative artificial intelligence?

Artificial Intelligence, a set of technologies that aim to replicate human cognitive functions, has seen remarkable improvements over the last decade. In particular, generative AI (GenAI), a subset of AI able to generate content tasks based on Large Language Models (LLM), has recently gained momentum. Based on an extensive analysis of generative AI use cases in large enterprises, we find that Gen AI shows strong labor productivity improvements across metrics such as throughput time, unit cost, and task effectiveness. However, the distribution of gains is asymmetric in favor of a few companies. While the current distribution of gains does not provide evidence of a power law effect, the current asymmetry reflects differences in AI resources/capabilities across companies - mainly data access, AI talent, or AI governance.

On Using Self-Report Studies to Analyze Language Models

We are at a curious point in time where our ability to build language models (LMs) has outpaced our ability to analyze them. We do not really know how to reliably determine their capabilities, biases, dangers, knowledge, and so on. The benchmarks we have are often overly specific, do not generalize well, and are susceptible to data leakage. Recently, I have noticed a trend of using self-report studies, such as various polls and questionnaires originally designed for humans, to analyze the properties of LMs. I think that this approach can easily lead to false results, which can be quite dangerous considering the current discussions on AI safety, governance, and regulation. To illustrate my point, I will delve deeper into several papers that employ self-report methodologies and I will try to highlight some of their weaknesses.

The Future of AI Governance: Navigating the Challenges of Generative AI

The Future of AI Governance: Navigating the Challenges of Generative AI

Ensuring fundamental rights compliance and trustworthiness of law enforcement AI systems: the ALIGNER Fundamental Rights Impact Assessment

AbstractArtificial intelligence systems can expand the capabilities and enhance the efficiency of law enforcement agencies preventing, investigating, detecting, and prosecuting criminal offences in the European Union. At the same time, the deployment of artificial intelligence in the security domain often raises numerous legal and ethical concerns. The ALIGNER Fundamental Rights Impact Assessment is an operational tool, rooted in fundamental rights and in the principles of AI ethics, ready to be integrated in the AI governance measures of European law enforcement agencies to inform their decision-making processes and ensure compliance with the recently adopted Artificial Intelligence Act. This paper first introduces the main tensions between law enforcement AI and fundamental rights, as enshrined in the Charter of Fundamental Rights of the European Union; then, it gives an overview of the main developments and best practices in AI governance and their relationship with fundamental rights as well as AI ethics; and finally, it describes the structure of the ALIGNER Fundamental Rights Impact Assessment.

The pursuit of ‘good’ Internet policy

AbstractAs our digital society continues to advance, an increasingly diverse array of scholars, policymakers, civic interest groups, companies and regulatory bodies from around the world are seeking to have an increased say in its governance. This diversity makes the challenge of achieving ‘good’ Internet policy more complex and contested. Of course, the notion of 'good Internet policy' raises several important questions: What defines a good Internet policy? Who is it intended for? How can it be achieved and evaluated? Why is it worth pursuing? The papers in this collection explore a number of issues relevant to this pursuit. They include research into deploying digital systems to increase participation in democratic elections and other e‐governance initiatives, navigating the intersection of local norms and global rules, addressing algorithmic discrimination, confronting the role of conspiracy theories in democratic processes, defining responsibility in content moderation, balancing legal certainty with operational flexibility in platform governance and the potential for data justice in AI governance.

Two paths for health AI governance: paternalism or democracy

This article assesses the cyclical failures of NHS data modernisation programmes, and considers that they fail because they proceed from a faulty – excessively paternalistic – governance model. Bias in algorithmic delivery of healthcare, a demonstrated problem with many existing health applications, is another serious risk. To regain trust and move towards better use of data in the NHS, we should democratise the development of these systems, and de-risk operational systems from issues such as automation bias. As a comparison, the essay explores two approaches to trust and bias problems in other contexts: Taiwan’s digital democracy, and American Airlines’ struggles to overcome automation bias in their pilots.

The Evolution of AI Governance

The Evolution of AI Governance

AI Governance in Healthcare: Best Practices, Solutions, and Unresolved Issues

What challenges are presented by AI adoption in healthcare? What role should AI governance play in regard to the opportunities and risks for generative AI (large language models like ChatGPT-4, etc.) in health? How do we responsibly evaluate new technology and ensure guardrails for its deployment, but without stifling innovation? How do we address societal fears and biases? What level of reliability and robustness should algorithms achieve before being adopted with confidence across healthcare? How can AI support health equity and expand access? We will explore these questions and more with some of the nation’s leading experts on these topics. Learning Objectives Understand key opportunities and challenges for AI governance in healthcare, particularly in regard to rapidly emerging generative AI applications Discuss various responsible AI and AI governance frameworks that seek to ensure safety and efficacy in AI adoption Explore the role of government, legislation, and regulators in supporting safe and equitable adoption of AI in healthcare Explore opportunities for government, industry, and academic partnerships to accelerate AI validation, development, and adoption of safety standards Consider the potential impact of AI on the healthcare workforce and provider and patient experiences Identify the risks we should be most concerned with and what approaches can we take to mitigate those risks? By the end of the presentation, participants should have a sense of the opportunities and challenges of adopting AI in healthcare, key risks and frameworks for mitigating them, and the potential role of government, academia, and industry in addressing these issues Generative AI application examples Patient Communication and Engagement Intelligent chatbots interacting with patients, answering their queries, providing health advice, and helping them manage their conditions Mental Health Support Provide psychological support to patients, helping them manage stress, anxiety, depression, and other mental health issues. Provide interventions based on CBT and other therapeutic techniques Automated Medical Documentation Note-taking can allowing doctors to focus more on patient care Education and Training Create educational content and personalized training for medical students and healthcare professionals, potentially simulating things like patient cases and providing guidance on diagnosis, etc. Synthesis and Summarization of Medical Research Make it easier for people to keep up with the latest developments Public Health Communication In a public health emergency, help craft health messages, FAQs, and timely and accurate updates for the public Generative AI challenges and limitations Ensuring the accuracy of AI-generated information Maintaining patient privacy Handling the ethical implications of AI and human-machine interactions in healthcare “Hallucinations” (false or inaccurate information) can occur when model has limited knowledge of a particular domain “Distraction” (losing track of earlier inputs) can occur when inputs go beyond allowable token count for model “context window” and models start to produce responses not relevant to queries and context.

Navigating AI Governance in Health & Human Services: Principals and Implementation Strategy

Navigating AI Governance in Health & Human Services: Principals and Implementation Strategy