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.

Biometric Verification (BV) systems often exhibit accuracy disparities across different demographic groups, leading to biases in BV applications. Assessing and quantifying these biases is essential for ensuring the fairness of BV systems. However, existing bias evaluation metrics in BV have limitations, such as focusing exclusively on match or non-match error rates, overlooking bias on demographic groups with performance levels falling between the best and worst performance levels, and neglecting the magnitude of the bias present. This paper presents an in-depth analysis of the limitations of current bias evaluation metrics in BV and, through experimental analysis, demonstrates their contextual suitability, merits, and limitations. Additionally, it introduces a novel general-purpose bias evaluation measure for BV, the ``Sum of Group Error Differences (SEDG)''. Our experimental results on controlled synthetic datasets demonstrate the effectiveness of demographic bias quantification when using existing metrics and our own proposed measure. We discuss the applicability of the bias evaluation metrics in a set of simulated demographic bias scenarios and provide scenario-based metric recommendations. Our code is publicly available under \url{https://github.com/alaaobeid/SEDG}.

All it took was public outrage, a widespread campaign, and political condemnation for the IRS to reverse its plans to require facial recognition for access to certain online services. In abandoning its intention to require tax-payers to upload images of their government-issued IDs and video selfies to controversial third-party company ID.me, the IRS has acknowledged that Americans shouldn't have to sacrifice their privacy for security. But the controversy around ID.me has somewhat eclipsed the broader and more concerning context of biometric identification technologies. Coverage of the IRS's announcement has in many cases not addressed the fact that millions of less advantaged individuals in the United States have already been forced to have their faces scanned by ID.me to access government services. ID.me has contracts with 10 federal agencies and has been verifying identities for the IRS's Child Tax Credit Update Portal since last year.

The'biometric verification of identities' of all travelers crossing US borders is set for a 2021 start date, with Homeland Security scrambling to get the system in place after Trump issued an executive order in March 2017 expediting the process

Delta Air Lines will implement facial recognition technology at Los Angeles International Airport from Friday, with cameras identifying passengers at a boarding gate with more to be installed after. The move has been met with controversy however, as groups such as Greenpeace call for a federal banning of the technology by law enforcement agencies. Critics say the technology could be used to violate privacy and date, as well as pointing to issues with accuracy for non-white male subjects. A spokeswoman for the coalition of groups, which also includes MoveOn and the Electronic Privacy Information Center, said the groups also oppose the use of the technology by airlines. 'There is no real oversight for how a private corporation can use our biometric information once they've collected it,' said Evan Greer, deputy director of Fight for the Future.