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）第一阶段项目的广泛影响/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。该团队将开发一个生态系统，使专家保持负责任和可信（使用同行评议系统），并使律师及其客户能够更好地了解情况，获得更多的高质量服务。该团队已经证实，以自动化、快速、廉价、标准和客观的方式评估民事法律纠纷中经济损失的标准化和自动化方法存在一个有利可图的市场。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，而不是评估过程的自动化部分。信息更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战略，而司法人员和陪审团将受益于改进的法律和专家证人服务，获得更标准化的信息，以做出更好、更明智的决定，并减少受偏见或不准确意见影响的可能性。该SBIR第一阶段项目包括一组确定性算法，这些算法接收用户输入（关于诉讼各方的事实和数据），并从预先协调的外部数据库检索相关数据系列，然后通过一组经济和统计计算对这些数据进行处理，产生一组输出，包括对以用户输入为特征的诉讼的财务收益（损失）的估计。提出的创新在几个方面改善了现有专家证人行业的低效率，从而带来了可负担性、多功能性、简单性、透明度和标准化。在第一阶段，将开发用于估计就业和人身伤害相关的财务收益（损失）的算法和可用原型。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 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.