Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward

• URL: http://arxiv.org/abs/2601.13122v1
• Date: Mon, 19 Jan 2026 15:10:59 GMT
• Title: Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward
• Authors: Gourab K Patro, Himanshi Agrawal, Himanshu Gharat, Supriya Panigrahi, Nim Sherpa, Vishal Vaddina, Dagnachew Birru,
• Abstract summary: We review various risks and vulnerabilities of modern general-purpose AI along eight widely accepted responsible AI (RAI) principles.<n>We argue that this is due to the non-deterministically high Degree of Freedom in output of general-purpose AI.<n>We derive C2V2 desiderata to meet the RAI requirements for future general-purpose AI systems.
• Score: 1.6714765221657821
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Modern general-purpose AI systems made using large language and vision models, are capable of performing a range of tasks like writing text articles, generating and debugging codes, querying databases, and translating from one language to another, which has made them quite popular across industries. However, there are risks like hallucinations, toxicity, and stereotypes in their output that make them untrustworthy. We review various risks and vulnerabilities of modern general-purpose AI along eight widely accepted responsible AI (RAI) principles (fairness, privacy, explainability, robustness, safety, truthfulness, governance, and sustainability) and compare how they are non-existent or less severe and easily mitigable in traditional task-specific counterparts. We argue that this is due to the non-deterministically high Degree of Freedom in output (DoFo) of general-purpose AI (unlike the deterministically constant or low DoFo of traditional task-specific AI systems), and there is a need to rethink our approach to RAI for general-purpose AI. Following this, we derive C2V2 (Control, Consistency, Value, Veracity) desiderata to meet the RAI requirements for future general-purpose AI systems, and discuss how recent efforts in AI alignment, retrieval-augmented generation, reasoning enhancements, etc. fare along one or more of the desiderata. We believe that the goal of developing responsible general-purpose AI can be achieved by formally modeling application- or domain-dependent RAI requirements along C2V2 dimensions, and taking a system design approach to suitably combine various techniques to meet the desiderata.

Related papers

• Embodied AI: From LLMs to World Models [65.68972714346909]
  Embodied Artificial Intelligence (AI) is an intelligent system paradigm for achieving Artificial General Intelligence (AGI)<n>Recent breakthroughs in Large Language Models (LLMs) and World Models (WMs) have drawn significant attention for embodied AI.
  arXiv  Detail & Related papers  (2025-09-24T11:37:48Z)
• Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
  This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
  arXiv  Detail & Related papers  (2025-05-13T17:59:20Z)
• Common Sense Is All You Need [5.280511830552275]
  Artificial intelligence (AI) has made significant strides in recent years, yet it continues to struggle with a fundamental aspect of cognition present in all animals: common sense.<n>Current AI systems often lack the ability to adapt to new situations without extensive prior knowledge.<n>This manuscript argues that integrating common sense into AI systems is essential for achieving true autonomy and unlocking the full societal and commercial value of AI.
  arXiv  Detail & Related papers  (2025-01-11T21:23:41Z)
• Fundamental Risks in the Current Deployment of General-Purpose AI Models: What Have We (Not) Learnt From Cybersecurity? [60.629883024152576]
  Large Language Models (LLMs) have seen rapid deployment in a wide range of use cases.<n>OpenAIs Altera are just a few examples of increased autonomy, data access, and execution capabilities.<n>These methods come with a range of cybersecurity challenges.
  arXiv  Detail & Related papers  (2024-12-19T14:44:41Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Assurance of AI Systems From a Dependability Perspective [0.0]
  We outline the principles of classical assurance for computer-based systems that pose significant risks.<n>We then consider application of these principles to systems that employ Artificial Intelligence (AI) and Machine Learning (ML)<n>For guards that require perception using AI and ML, we examine ways to minimize the trust placed in these elements.
  arXiv  Detail & Related papers  (2024-07-18T23:55:43Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• POLARIS: A framework to guide the development of Trustworthy AI systems [3.02243271391691]
  There is a significant gap between high-level AI ethics principles and low-level concrete practices for AI professionals. We develop a novel holistic framework for Trustworthy AI - designed to bridge the gap between theory and practice. Our goal is to empower AI professionals to confidently navigate the ethical dimensions of Trustworthy AI.
  arXiv  Detail & Related papers  (2024-02-08T01:05:16Z)
• General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance [16.030931070783637]
  General-Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations.
  arXiv  Detail & Related papers  (2023-07-26T16:35:48Z)
• Never trust, always verify : a roadmap for Trustworthy AI? [12.031113181911627]
  We examine trust in the context of AI-based systems to understand what it means for an AI system to be trustworthy. We suggest a trust (resp. zero-trust) model for AI and suggest a set of properties that should be satisfied to ensure the trustworthiness of AI systems.
  arXiv  Detail & Related papers  (2022-06-23T21:13:10Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)

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.