King's Algorithm for Acceptance Likelihood Identification(KAALI)

精神疾病患者在劳动力市场受到不公正待遇。要解决这一趋势，需要详细了解影响招聘决策和职业轨迹的机制，将这些发现转化为富有成效的干预措施，以确保人才的最佳、公平分配，并开发防止浪费申请的方法。人工智能（AI）和机器学习（ML）的使用在数字招聘时代发挥着核心作用。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有精神健康障碍的求职者的申请和职业结果。作为该博士课程的核心成果，工作推荐系统将积极地为潜在候选人提供更容易的工作申请决策，并为求职者提供有关他们在组织或特定职位上茁壮成长的机会和潜力的建议。当这项技术得到充分发展时，我们将能够限制心肌梗死患者找工作的时间，从而在早期干预和病情管理中发挥积极作用。这不仅可以减轻数百万患者的痛苦，还可以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.