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

这一小企业创新研究(SBIR)第一阶段项目的更广泛影响/商业影响利用了现有专家证人行业的低效，并为法律程序带来了可负担性、多功能性(无限情景生成)、简单性、透明度和标准化。该团队将开发一个生态系统，使专家负责和可信(使用同行审查系统)，并允许律师及其客户更好地了解更多获得高质量服务的机会。该团队已经证实，以自动、快速、廉价、标准和客观的方式评估民事法律纠纷中的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。整个行业的专家证人服务的质量将会提高，因为专家们将能够更深入地专注于更有争议的诉讼问题，而不是评估过程中自动进行的部分。消息灵通的律师和公司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、更知情的决定，不太容易受到偏见或不准确意见的影响。SBIR第一阶段项目包括一套确定性算法，吸收用户输入(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。拟议的创新在几个方面改善了现有专家证人行业的低效，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，将开发用于获取就业和与个人伤害相关的财务收益(损失)估计的算法和可用的原型。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。

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.