FairCVreview: A novel platform to advance trust in the compliance and fairness of AI systems for the recruitment sector by providing fully transparent and bias-free AI systems.

FairCVreview是一个基于人工智能的简历筛选工具，专注于公平和透明。它基于一个易于使用的界面，即使是最小的招聘公司也可以获得最新的技术，而无需编码或技术技能来操作。筛选技术专注于公平性，使用AI来识别和消除基础数据中的偏见。对结果进行了充分的解释，以便招聘人员、求职者和雇主都能对甄选的公平性充满信心。招聘人员和雇主将看到决策过程的细节，并获得关于平等参数的衡量标准。他们将能够与申请人分享详细信息，解释他们为什么没有被选中（例如，指出与选定的候选人相比缺乏哪些技能或资格）。提供多样性指标，以便雇主可以识别任何问题并采取措施解决这些问题。例如，如果候选人库缺乏多样性，那么雇主可以改进他们的广告策略，以鼓励更广泛的申请人。此外，求职者将能够通过我们额外的面向公众的应用程序测试自己的简历。这将提供一个用户友好的界面，允许个人上传他们的简历，它将标记任何可能使他们暴露于偏见的元素。例如，如果他们提供了泄露其年龄的详细信息，系统将提示他们考虑是否有必要这样做。（招聘人员）。首先，我们将确定可能影响项目的偏见类型，并确定可能受到影响的目标群体和关键维度这包括与主题专家合作，以识别基础数据中存在的偏见以及机器学习处理过程中可能出现的任何偏见。在人工智能开发和培训过程中，我们将评估所有潜在维度的偏差，并根据绩效指标对这些偏差进行评分，使我们能够识别和减轻任何偏差。其次，我们将提供透明的结果，向招聘人员/雇主反馈简历获得匹配分数的确切原因。为了实现这一目标，我们将利用我们在为论文评分部门开发类似的可解释性技术方面的经验（该部门使用类似的方法来消除偏见并提供透明的结果）。

FairCVreview is an AI-based CV screening tool that focuses on fairness and transparency. It is based on an easy-to-use interface that gives even the smallest recruitment companies access to the latest technologies without needing coding or technical skills to operate.The screening technology focuses on fairness, using AI to identify and eliminate the bias in underlying data. The outputs are fully explained so that recruiters, job applicants and employers can all have full confidence in the fairness of the selection.Recruiters and employers will see details of the decision-making process and get metrics on equality parameters. They will be able to share details with applicants to explain why they have not been selected (e.g. indicating which skills or qualifications were lacking compared to the selected candidates).Diversity metrics are provided so employers can identify any issues and take steps to address them. For example, if the candidate pool lacks diversity then the employer could improve their advertising strategies to encourage a broader range of applicants.Additionally, Job seekers will be able to test their own CV via our additional public-facing application. This will provide a user-friendly interface allowing individuals to upload their CV and it will flag any elements that could potentially expose them to bias. For example, if they have included details that give away their age, they will be prompted to consider if this is necessary.FairCVreview will be developed using a collaborative approach with input from customer representatives (recruiters).Firstly, we will identify the types of bias that could potentially impact the project and we will identify the target groups and key dimensions that could be impacted (e.g. gender, ethnicity, age, etc).This includes working with subject experts to identify bias that exists in the underlying data as well as any that can arise during machine learning processing. During AI development and training, we will evaluate the outputs for bias across all potential dimensions and these will be scored against performance metrics, allowing us to identify and mitigate against any bias.Secondly, we will deliver transparent results, giving feedback to the recruiter/employer on exactly why the CV achieved that match score. To achieve this, we will build on our experience in developing similar explainability technology for the essay marking sector (which uses a similar approach to eliminate bias and deliver transparent results).