Zero-to-One IDV: A Conceptual Model for AI-Powered Identity Verification
- URL: http://arxiv.org/abs/2503.08734v1
- Date: Tue, 11 Mar 2025 04:20:02 GMT
- Title: Zero-to-One IDV: A Conceptual Model for AI-Powered Identity Verification
- Authors: Aniket Vaidya, Anurag Awasthi,
- Abstract summary: This paper introduces Zero to One'', a holistic conceptual framework for developing AI-powered IDV products.<n>It details the evolution of identity verification and the current regulatory landscape to contextualize the need for a robust conceptual model.<n>The framework addresses security, privacy, UX, and regulatory compliance, offering a structured approach to building effective IDV solutions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In today's increasingly digital interactions, robust Identity Verification (IDV) is crucial for security and trust. Artificial Intelligence (AI) is transforming IDV, enhancing accuracy and fraud detection. This paper introduces ``Zero to One,'' a holistic conceptual framework for developing AI-powered IDV products. This paper outlines the foundational problem and research objectives that necessitate a new framework for IDV in the age of AI. It details the evolution of identity verification and the current regulatory landscape to contextualize the need for a robust conceptual model. The core of the paper is the presentation of the ``Zero to One'' framework itself, dissecting its four essential components: Document Verification, Biometric Verification, Risk Assessment, and Orchestration. The paper concludes by discussing the implications of this conceptual model and suggesting future research directions focused on the framework's further development and application. The framework addresses security, privacy, UX, and regulatory compliance, offering a structured approach to building effective IDV solutions. Successful IDV platforms require a balanced conceptual understanding of verification methods, risk management, and operational scalability, with AI as a key enabler. This paper presents the ``Zero to One'' framework as a refined conceptual model, detailing verification layers, and AI's transformative role in shaping next-generation IDV products.
Related papers
- A Framework for Cryptographic Verifiability of End-to-End AI Pipelines [0.8075866265341175]
We propose a framework for complete verifiable AI pipelines, identifying key components and analyzing existing cryptographic approaches.
This framework could be used to combat misinformation by providing cryptographic proofs alongside AI-generated assets.
arXiv Detail & Related papers (2025-03-28T16:20:57Z) - On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective [314.7991906491166]
Generative Foundation Models (GenFMs) have emerged as transformative tools.<n>Their widespread adoption raises critical concerns regarding trustworthiness across dimensions.<n>This paper presents a comprehensive framework to address these challenges through three key contributions.
arXiv Detail & Related papers (2025-02-20T06:20:36Z) - AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons [62.374792825813394]
This paper introduces AILuminate v1.0, the first comprehensive industry-standard benchmark for assessing AI-product risk and reliability.
The benchmark evaluates an AI system's resistance to prompts designed to elicit dangerous, illegal, or undesirable behavior in 12 hazard categories.
arXiv Detail & Related papers (2025-02-19T05:58:52Z) - Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC)<n>RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks.<n>Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
arXiv Detail & Related papers (2025-02-08T06:50:47Z) - 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) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - A Nested Model for AI Design and Validation [0.5120567378386615]
Despite the need for new regulations, there is a mismatch between regulatory science and AI.
A five-layer nested model for AI design and validation aims to address these issues.
arXiv Detail & Related papers (2024-06-08T12:46:12Z) - Developing trustworthy AI applications with foundation models [0.8005355048487703]
The trustworthiness of AI applications has been the subject of recent research and is also addressed in the EU's recently adopted AI Regulation.
Foundation models in the field of text, speech and image processing offer completely new possibilities for developing AI applications.
This white paper shows how the trustworthiness of an AI application developed with foundation models can be evaluated and ensured.
arXiv Detail & Related papers (2024-05-08T10:08:45Z) - Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities [1.3225694028747144]
We propose a Coordinated Flaw Disclosure framework tailored to the complexities of machine learning (ML) issues.
Our framework introduces innovations such as extended model cards, dynamic scope expansion, an independent adjudication panel, and an automated verification process.
We argue that CFD could significantly enhance public trust in AI systems.
arXiv Detail & Related papers (2024-02-10T20:39:04Z) - Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review [12.38351931894004]
We present the first systematic literature review of explainable methods for safe and trustworthy autonomous driving.
We identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation.
We propose a modular framework called SafeX to integrate these contributions, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.
arXiv Detail & Related papers (2024-02-08T09:08:44Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z)
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.