Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting
- URL: http://arxiv.org/abs/2501.14778v1
- Date: Wed, 01 Jan 2025 17:34:57 GMT
- Title: Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting
- Authors: Avinash Agarwal, Manisha J Nene,
- Abstract summary: The increasing use of AI technologies has led to increasing AI incidents, posing risks and causing harm to individuals, organizations, and society.<n>This study recognizes and addresses the lack of standardized protocols for reliably and comprehensively gathering such incident data.<n>It proposes nine actionable recommendations to enhance standardization efforts to address these gaps.
- Score: 2.209921757303168
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The increasing use of AI technologies has led to increasing AI incidents, posing risks and causing harm to individuals, organizations, and society. This study recognizes and addresses the lack of standardized protocols for reliably and comprehensively gathering such incident data crucial for preventing future incidents and developing mitigating strategies. Specifically, this study analyses existing open-access AI-incident databases through a systematic methodology and identifies nine gaps in current AI incident reporting practices. Further, it proposes nine actionable recommendations to enhance standardization efforts to address these gaps. Ensuring the trustworthiness of enabling technologies such as AI is necessary for sustainable digital transformation. Our research promotes the development of standards to prevent future AI incidents and promote trustworthy AI, thus facilitating achieving the UN sustainable development goals. Through international cooperation, stakeholders can unlock the transformative potential of AI, enabling a sustainable and inclusive future for all.
Related papers
- Responsible Development of Offensive AI [0.0]
This study aims to establish priorities that balance societal benefits against risks.
The two forms of offensive AI evaluated in this study are vulnerability detection agents, which solve Capture- The-Flag challenges, and AI-powered malware.
arXiv Detail & Related papers (2025-04-03T15:37:38Z) - AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.
AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.
We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
arXiv Detail & Related papers (2025-02-24T02:11:52Z) - Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity [0.0]
This paper critically examines the evolving ethical and regulatory challenges posed by the integration of artificial intelligence in cybersecurity.<n>We trace the historical development of AI regulation, highlighting major milestones from theoretical discussions in the 1940s to the implementation of recent global frameworks such as the European Union AI Act.<n>Ethical concerns such as bias, transparency, accountability, privacy, and human oversight are explored in depth, along with their implications for AI-driven cybersecurity systems.
arXiv Detail & Related papers (2025-01-15T18:17:37Z) - Standardization Trends on Safety and Trustworthiness Technology for Advanced AI [0.0]
Recent AI technologies based on large language models and foundation models are approaching or surpassing artificial general intelligence.
These advancements have raised concerns regarding the safety and trustworthiness of advanced AI.
Efforts are being expended to develop internationally agreed-upon standards to ensure the safety and reliability of AI.
arXiv Detail & Related papers (2024-10-29T15:50:24Z) - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context.
We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
arXiv Detail & Related papers (2024-07-31T17:59:24Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - Taking control: Policies to address extinction risks from AI [0.0]
We argue that voluntary commitments from AI companies would be an inappropriate and insufficient response.
We describe three policy proposals that would meaningfully address the threats from advanced AI.
arXiv Detail & Related papers (2023-10-31T15:53:14Z) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - International Institutions for Advanced AI [47.449762587672986]
International institutions may have an important role to play in ensuring advanced AI systems benefit humanity.
This paper identifies a set of governance functions that could be performed at an international level to address these challenges.
It groups these functions into four institutional models that exhibit internal synergies and have precedents in existing organizations.
arXiv Detail & Related papers (2023-07-10T16:55:55Z) - Trustworthy AI: From Principles to Practices [44.67324097900778]
Many current AI systems were found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection, etc.
In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems.
To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems.
arXiv Detail & Related papers (2021-10-04T03:20:39Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.