Developing, Evaluating and Commercialising Scalable Automated Multiple Mini Interviews

大型跨国公司平均花费4000英镑雇佣每名新员工;一年要填补4000多个职位空缺，成本可能超过1600万英镑。各部门普遍存在面试官偏见;根据英国政府的数据，在最高级别的企业领导层中，96.7%的人是白色，71%的人是男性。这个由英国创新基金资助的为期18个月的项目将使一个最先进的自动候选人选择系统得到完善、测试和推向市场，Sammi（可扩展的自动化多迷你面试）旨在让合适的人进入合适的工作，同时提高成本效益和多样性招聘。SAMMI目前是一个原型，旨在提供一个可扩展的，自动化版本的面对面的“多个迷你面试”（MMI）技术认可的健康教育英格兰和广泛使用的医疗保健学生的选择。MMI涉及到候选人坐在一系列的“站”，目前的偏见，“真实的生活情景”，旨在评估软技能。SAMMI建立在超过十年的MMI研究和文本分析的基础上，拥有定制的在线系统。会话分析技术评估申请人的面试回答（文本/语音），人工智能可以根据预定义的软技能对候选人的回答/互动进行评分。从候选人的软技能中提取的分析适用于：预筛选，选择和与简历/个人陈述/求职信的比较。SAMMI原型于2018年在萨里大学开发。它已经在曼彻斯特医学院和萨里大学进行了测试，证明了良好的功能和测试重复测试的可靠性。Innovate UK资助的市场验证之旅显示了国际上对SAMMI的广泛兴趣，超过100家公司/组织（私人，公共和慈善机构）正在进行大规模的候选人选择。该项目将利用SAMMI实现更快的市场进入，从而利用先前的投资和建立的客户联系。将开发、完善和测试SAMMI核心组成部分（以确保无偏倚），并计划对SAMMI的有效性（与面对面比较者相比的可靠性、有效性、功能性、可接受性和成本效益）进行案例研究评价。正在与潜在的试点地点进行讨论，例如：英国和美国的医学院;国际非政府组织和跨国公司。时间和资源将用于通过补充“附加组件”来增强SAMMI，包括将简历/个人陈述内容与面试表现进行比较的新型申请人跟踪系统。SAMMI提供了一个独特的系统，旨在提高成本效益并避免无意的面试偏见。

Large multinational companies spend, on average, £4k hiring each new employee; with over 4000 vacancies to fill a year, the costs can exceed £16m. Interviewer bias is widespread across sectors; according to UK government figures, at the most senior level of corporate leadership, 96.7% of individuals are white and 71% male.This 18-month, project, funded by Innovate UK, will enable the refinement, testing and launch to market of a state-of-the-art automated candidate selection system, SAMMI (scalable automated multiple mini interview) designed to get the right people into the right job while increasing cost-efficiency and diversity hiring.SAMMI is currently a prototype designed to offer a scalable, automated version of the face-to-face 'Multiple Mini Interview' (MMI) technique endorsed by Health Education England and widely used in healthcare student selection. MMIs involve candidates sitting a series of 'stations' which present bias-free, 'real life scenarios' designed to assess soft skills. SAMMI builds on over ten years of MMI research and text analytics with a custom built online system. Conversational analysis techniques evaluate applicants' interview responses (text/speech) and artificial intelligence enables scoring of candidate answers/interactions against pre-defined soft-skills. The analytics extracted of candidates' soft skills are applicable to: pre-screening, selection and comparison against CVs/personal statements/cover letters.The SAMMI prototype was developed at the University of Surrey in 2018. It has been tested at Manchester Medical School and The University of Surrey demonstrating good functionality and test re-test reliability. The Innovate UK funded market validation journey revealed extensive international interest in SAMMI across 100+ companies/organisations (private, public and charity) undertaking large scale candidate selection.This project will leverage SAMMI to enable quicker market entry thereby capitalising on prior investment and established customer contact. Core SAMMI components will be developed, refined, tested (to ensure bias-free) and case study evaluation of the effectiveness (reliability, validity, functionality, acceptability and cost-benefit compared to a face-to-face comparator) of SAMMI is planned. Discussion are ongoing with potential pilot sites for example: UK and US Medical Schools; international non-governmental organisations and multinational companies. Time and resources will be used to enhance SAMMI with complementary 'add-ons' including a novel applicant tracking system comparing CV/personal statement content with interview performance.SAMMI offers a unique system designed to be cost-efficient as well as avoid unintentional interview bias.