Several ways to solve the illusion of AI in HR applications

Many HRs want to try to apply AI to the daily work of human resource management, such as intelligent Q&A for employees, intelligent recruitment and resume screening, employee training needs analysis, and performance evaluation prediction.

However, the most troublesome problem for HR in applying AI is that AI may produce "hallucinations", which becomes an obstacle for HR to fully trust and use AI.

Why HR finds it difficult to accept the AI ​​illusion

Decision-making accuracy requires extremely high

HR work involves key decisions such as employee recruitment, promotion, and salary adjustment, which directly affect employee career development and corporate talent layout. For example, in the recruitment process, if AI recommends candidates that are seriously inconsistent with job requirements due to hallucinations, the company will not only waste recruitment costs, but may also miss out on excellent talents and disrupt the pace of business advancement. In promotion decisions, incorrect performance evaluations and ability judgments based on AI hallucinations will cause truly capable and outstanding employees to miss out on promotion opportunities, undermine employee enthusiasm, and destroy the fair competition environment within the company.

Employee interests are of vital importance

Employees’ questions are generally related to their personal interests, such as how to settle down, how to apply for work-related injuries, how to retire, etc. If AI hallucinations lead to wrong answers, it may affect the actual interests of employees, which will affect the professionalism of HR in the minds of employees, reduce trust in the company, affect employee satisfaction and loyalty, and increase the risk of employee turnover. It will also cause some employee relationship problems.

Legal compliance risks should not be underestimated

HR work must strictly comply with labor laws and regulations, such as anti-discrimination laws and privacy protection laws. AI illusions may lead to unreasonable bias against candidates of a certain gender, age, or race during recruitment screening, or violate privacy protection regulations when processing employee data. Once a legal dispute is triggered, the company will face consequences such as fines and damage to its reputation.

Solutions to solve AI illusions in HR applications

Optimizing training data

The quality of training data directly determines the performance of AI models. In HR application scenarios, it is necessary to collect massive, diverse and accurate human resources data. For example, in recruitment model training, it is necessary to ensure that information such as various job requirements, ability assessments, and interview results are accurate. You can cooperate with professional human resources data providers to obtain high-quality data, and use data cleaning technology to remove erroneous, duplicated, and biased data. For example, you can conduct regular manual sampling of training data and correct problems in a timely manner to ensure the accuracy and reliability of the data.

Using Retrieval Enhanced Generation (RAG) technology

RAG technology allows AI to retrieve relevant information from authoritative knowledge bases before generating answers, ensuring that the answers are based on facts. In the HR field, a knowledge base containing labor laws and regulations, industry best practices, and internal corporate rules and regulations can be built. When AI handles tasks such as employee consultations and formulating human resources policies, it first retrieves information from the knowledge base and then generates content based on its own model. For example, when an employee inquires about the calculation of overtime wages, AI uses RAG technology to obtain accurate calculation methods from the regulatory knowledge base to avoid giving wrong answers due to hallucinations.

Introducing a manual review mechanism

After AI outputs the results, a manual review process is set up, and experienced HR professionals check key decisions. For example, in the recruitment process, after AI completes resume screening, HR conducts a second review of the recommended candidate resumes to check whether the AI ​​recommendation reasons are reasonable and whether the candidate really meets the core requirements of the position. In performance evaluation, HR and managers review the evaluation report generated by AI to check whether the data source is reliable and whether the evaluation logic is reasonable, and make corrections to any doubtful areas to ensure that the final result is accurate and fair.

Model interpretation and visualization

Develop model explanation tools to help HR understand the AI ​​decision-making process. For example, use a visual interface to show which keywords, skills, experience, and other factors AI uses to score and rank candidates when screening resumes; in the performance evaluation model, show how the weights of various indicators affect the final evaluation results. In this way, HR can judge whether AI decisions are reasonable and detect signs of hallucination in a timely manner. If it is found that AI pays too much attention to a non-critical factor and gives unreasonable recommendations, the model parameters or training data can be adjusted accordingly.

Continuous monitoring and feedback optimization

Establish an AI application effect monitoring system and collect feedback data from HR during use. Regularly analyze AI's performance in different HR tasks, such as statistical recruitment recommendation accuracy, performance evaluation and actual performance compliance, and other indicators. Once errors caused by hallucinations are found, timely feedback is given to the technical team to optimize and iterate the model. At the same time, HR is encouraged to report AI hallucination problems encountered in a timely manner to form a closed-loop optimization mechanism and continuously improve the reliability of AI in HR applications.

By using the above-mentioned methods in coordination, it is possible to gradually solve the illusion problem of AI in HR applications, making AI a real assistant to HR and promoting human resource management to a higher level.