I-Corps: Probabilistic artificial intelligence (AI)-based software for proactive data quality assessment

I-Corps项目更广泛的影响/商业潜力是开发一种识别和纠正数据输入错误的解决方案。电子商务和数字系统的加速采用增加了数字数据输入系统的使用。这可能对向低收入和教育水平低的人提供公平的服务造成挑战。拟议的数据质量软件在数据输入点发现并消除用户错误，在填写表单时为用户提供主动、安全和方便的支持。该解决方案还可以为公司提供准确的数据。与现有的数据质量解决方案相比，该产品消除了耗时且成本高昂的数据收集后清理的需要。该解决方案可能有利于以数据为中心的领域，如医疗保健、金融、电子商务、税收和政府福利应用程序系统，在这些领域，数据错误可能代价高昂。目标是为用户提供主动帮助，以减少错误处理和及时访问的潜在问题。这种建议的实时、错误警报和纠正技术可以通过纠正能力使数据输入专家和消费者受益，而处理数字文档和在线表单/web提交的公司可以从提交后纠正成本和潜在损害控制成本方面节省成本。I-Corps项目的基础是开发人工智能驱动的分析过程和智能软件，帮助用户在数据输入时提交无错误的信息。算法通过使用基本格式检查、数据匹配和概率机器学习算法来分析数据以揭示异常条目。基于上下文和个性化的概率分析方法使变量和数据准确性评估成为可能。这些算法的贝叶斯性质允许结合专家意见和用户反馈。所建议的软件提供了一个使用分析输出的界面，并在提交之前与用户一起验证他们的数据输入。这个界面可以帮助用户在输入数据时修复错误。使用概率算法允许使用相对发生权重，而不是在整个数据库上进行计算。这使得过程更快，减少了对原始数据传输的隐私和安全问题。提出的技术涉及多个研究领域，包括贝叶斯统计、数据分析和软件开发。该项目旨在验证一种基于主动数据质量范式和包括概率方法在内的一套算法的安全、可信和经过验证的方法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a solution that identifies and corrects data data entry errors. The accelerating adoption of e-commerce and digital systems has increased the use of digital data entry systems. This may create challenges for providing equitable service to people with low income and education levels. The proposed data quality software finds and eliminates user errors at the point of data entry, providing proactive, secure and convenient support for users while filling in forms. The solution may also provide accurate data for companies. Compared to existing data quality solutions, this product eliminates the need for time-consuming and costly post data collection cleaning. The solution may benefit data-centric domains such as health care, finance, e-commerce, and tax and government benefit application systems where data errors can be costly. The goal is to provide users with proactive help that may reduce incorrect processing and potential issues with timely access. This proposed real-time, error alert and correction technology may benefit data entry specialists and consumers through corrective capability, while the companies that process the digital documents and online forms/web submissions may benefit from the cost-savings in terms of both post-submission corrective costs and potential damage control costs.This I-Corps project is based on the development of an artificial intelligence-driven analytical process and smart software that helps users submit mistake-free information at the time of data entry. The algorithms analyze the data to reveal unusual entries through the use of basic format checks, data matching, and probabilistic machine learning algorithms. Context based and personalized probabilistic analytical methods enable both variable and data accuracy assessment. The Bayesian nature of these algorithms allows incorporation of expert opinion and user feedback. The proposed software provides an interface that uses the analytical output and works with the user to verify their data entry before submission. This interface helps users fix mistakes at the time of data entry. Use of probabilistic algorithms allow the use of relative occurrence weights instead of computing over the whole data base. This makes the process faster with diminished privacy and security concerns over raw data transfer. The proposed technology involves multiple research areas including Bayesian statistics, data analytics, and software development. This project aims to verify a secure, trustworthy, and validated approach based on a proactive data quality paradigm and a suite of algorithms including probabilistic methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.