10076196FairCVreview: 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.ClosedGrant for R&DInnovate UKFairCVreview 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).