King's Algorithm for Acceptance Likelihood Identification(KAALI)

患有精神疾病(MI)的人在劳动力市场受到不公正的待遇。应对这一趋势需要详细了解影响招聘决定和职业发展轨迹的机制，将这些发现转化为有效的干预措施，以确保最佳、公平的人才配置，并制定防止浪费申请的方法。在数字招聘时代发挥核心作用的是人工智能(AI)和机器学习(ML)的使用。对这个项目感兴趣的是人工智能在工作推荐中的应用。我们希望建立一个工具，根据个人诊断、教育、职业道路和人口统计信息为MI患者提供预测。我们计划演示如何使用算法来预测患有精神健康障碍的求职者的申请和职业结果。就业推荐系统将作为这一博士学位的核心成果而创建，它将积极地让潜在候选人更容易做出工作申请决定，并向求职者提供关于他们在一个组织或特定职位上取得成功的机会和潜力的建议。当我们完全发展起来后，我们将能够限制MI患者的找工作时间，从而积极参与早期干预以及对他们的病情进行管理。这不仅将使数百万患者减轻痛苦，还将为英国政府节省大量资金。

Individuals with mental illness (MI) experience unjust treatment in the labour market. Tackling this trend calls fora detailed understanding of mechanisms influencing hiring decisions and career trajectories, translating these findings into fruitful interventions for ensuring optimal, fair allocation of talent, and developments of methods for preventing wasted applications. Playing a centre role in the age of digital recruitment is the use of Artificial Intelligence(AI) and Machine Learning (ML). Of interest for this project is the application of AI for job recommendations. We want to build a tool providing predictions for people with MI based on information regarding individual diagnosis, education, career path and demographics. We plan to demonstrate how algorithms can be used to predict application and career outcomes for job seekers with mental health disorders. The job recommendation system which will be created as a core outcome of this PhD will actively make job application decisions easier for potential candidates and give advice to job seekers on their chances and potential to thrive at an organisation or in a specific position. When fully developed, we will be able to limit time of job seeking for people with MI, and therefore actively play a part in early intervention, as well as management of their condition. This would not only allow millions of patients to alleviate suffering but save the UK government substantial amounts of money.