SBIR Phase I: The Automated Forensic Economist: Towards Affordability, Transparency, and Efficiency in Forensic Economics

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业影响利用了现有专家证人行业的低效率，并为法律的程序带来了可负担性、多功能性（无限场景生成）、简单性、透明度和标准化。该小组将建立一个生态系统，使专家保持问责和可信（使用同行审查制度），并使律师及其客户能够更好地了解情况，更好地获得优质服务。该团队已经证实，一个有利可图的市场存在一个标准化和自动化的方法，以自动化，快速，廉价，标准和客观的方式评估民事法律的纠纷中的经济损失。专家证人服务的质量将在整个行业得到提高，因为专家将能够更深入地关注更具争议的诉讼问题，而不是评估过程中可自动化的部分。消息灵通的律师和事务所将能够在整个过程中为其客户提供咨询，并制定更好的法律的战略，而司法人员和陪审团将受益于更好的法律的和专家证人服务，获得更标准化的信息，更多-该SBIR第一阶段项目由一组确定性算法组成，该系统的主要工作是收集输入（关于诉讼当事方的事实和数据），并从预先统一的外部数据库检索相关数据系列，然后通过一套经济和统计计算对这些数据系列进行处理，产生一套产出，包括对以用户输入为特点的诉讼的财务收益（损失）的估计。拟议的创新在几个方面改善了现有专家证人行业的低效率，因此带来了可负担性，多功能性，简单性，透明度和标准化。在第一阶段，将开发用于捕获就业和人身伤害相关的财务收益（损失）估计的算法和可用原型。该奖项反映了NSF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 impacts of this Small Business Innovation Research (SBIR) Phase I project capitalizes on the inefficiencies of the existing expert witness industry and brings affordability, versatility (unlimited scenario generation), simplicity, transparency, andstandardization to legal proceedings. The team will develop an ecosystem that will keep experts accountable and credible (using a peer-review system) and allow lawyers and their clients to be better informed with enhanced access to high-quality services. The team has validated that a profitable market exists for a standardized and automated method of evaluating economic losses in civil legal disputes in an automated, fast,inexpensive, standard, and objective manner. The quality of expert witness services will increase across the industry, as experts will be able to focus more deeply on the more disputed issues of litigation rather than the automatable portions of the estimation process. Better-informed attorneys and firms will be able to counsel their clients and develop better legal strategies throughout the process, while judicial personnel and juries will benefit from improved legal and expert witness services, gaining access to more standardized information to make better, more-informed decisions and be less susceptible to biased or inaccurate opinions.This SBIR Phase I project consists of a set of deterministic algorithms intaking user inputs (facts and data regarding parties in a lawsuit) and retrieving relevant data series from pre-harmonized external databases, which are then processed through a set of economic and statistical computations, producing a set of outputs, including an estimate of financial gains (losses) for the lawsuit characterized by user inputs. The proposed innovation improves on the inefficiencies of the existing expert witness industry in several dimensions, and as a result brings affordability, versatility, simplicity, transparency, and standardization. In Phase I, the algorithm and a usable prototype for capturing employment and personal injury-related financial gains (losses) estimation will be developed.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.