SBIR Phase I: Development of Collaborative and Interpretable Machine Learning Platform

这个SBIR第一阶段项目旨在设计和开发一个协作和可解释的机器学习平台，让关键的机器学习利益相关者共同努力，提供可信的机器学习和人工智能能力。这个项目将解决严重的商业和社会问题，即由于无法对机器生成的结果提供有意义的解释、解释和协作监督而缺乏信任。这个问题已经成为更广泛地采用机器学习(ML)和人工智能(AI)的最大挑战之一，特别是在监管严格的行业，在这些行业，机器算法如何得出结果和预测的合理程度的可跟踪性、可审核性和合理性是必要的，并且是法律强制要求的。该项目旨在初步解决医疗保健和保险领域的特定和关键用例，并计划扩展到金融服务、制药和自动驾驶汽车行业等其他行业。该项目旨在通过成为关键监管市场垂直市场的ML平台选择，利用人工智能推动的经济增长，同时提供保障，防止由于不正确的ML和AI预测而造成的负面影响永久化。该项目的关键创新侧重于结合机器学习快速大规模处理的能力的优势，找到人类合作、认知和监督难以找到的模式，以实现对ML结果的透明度和信任。该项目旨在推进过去和正在进行的可解释ML(IML)和可解释人工智能(XAI)的科学研究，并将其商业化。它还通过将此研究应用于特定行业领域垂直领域的关键业务用例，显著加快了该研究的采用和商业化。该项目提供了一个新颖的协作界面，可以评估、增强并将解释应用于高度监管行业中的特定业务用例。该项目还使用了创新的机器和人在环中的混合设计，使ML交互更有意义，并在商业决策使用之前，使人类成为ML解释评估和监督的中心权威。在未来阶段，该项目还旨在利用其黄金标准解释知识库提供持续的闭环反馈，并改善ML模型的可解释性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This SBIR Phase I project aims to design and develop a collaborative and interpretable machine learning platform for key machine learning stakeholders to work together to deliver trusted machine learning and artificial intelligence capabilities. This project will address the critical commercial and societal problem of lack of trust due to inability to provide meaningful interpretation, explanation & collaborative oversight for machine generated results. This problem has become one of the biggest challenges for broader adoption of Machine Learning (ML) and Artificial Intelligence (AI), especially, in highly regulated industries, where reasonable degree of traceability, auditability and rationale as to how the machine algorithms arrived at the outcomes and predictions is necessary and mandated by law. This project aims to initially address specific and critical use cases in the healthcare and insurance areas with a plan to expand to other sectors like financial services, pharma and self-driving auto industry. The project intends to capitalize on AI driven growth in the economy by becoming the ML platform of choice in key regulatory market verticals while providing safeguards against perpetuating negative impacts due to incorrect ML and AI predictions. The project key innovation focuses on combining the benefits of Machine Learning's ability to mass process at fast rates, find patterns which are hard to find with human collaboration, cognition and oversight to achieve transparency and trust in the ML outcomes. The project aims to advance past and ongoing scientific research in Interpretable ML (IML) and Explainable AI (XAI) and commercialize it. It also significantly speeds up adoption and commercialization of this research by applying it to critical business use cases in specific industry domain verticals. This project provides a novel collaborative interface that evaluates, augments and applies the explanations to specific business use cases in highly regulated industries. This project also uses an innovative hybrid machine and human in the loop design to make the ML interactions more meaningful and a human at the center authority for evaluation and oversight of the ML explanations prior to use in business decision-making. In future phases, this project also aims to provide continuous closed loop feedback and improvement of interpretability of ML models using its gold standard explanations knowledgebase.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.