Tokyo City University pioneers have made significant strides in workforce management with the development of an artificial intelligence (AI) program designed to anticipate employees’ longevity at a company. This technological innovation aims to assist employers in identifying and supporting team members who might be considering leaving their position.
The AI model, a brainchild of professor Naruhiko Shiratori and a Tokyo-based start-up, carefully examines a multitude of variables such as attendance, age, gender, and even historical data from past employees. This holistic approach facilitates the development of a custom turnover model tailored to each organization’s unique environment.
The model offers predictive insights on new employees, quantifying the likelihood of their departure in percentage terms. Shiratori, who specializes in media education, emphasizes the potential application of this model in providing preemptive support to those who may be struggling, albeit cautiously and without presenting potentially alarming raw data directly to the individual.
The conception of this cutting-edge tool is not without precedent; it evolves from previous research efforts utilizing AI to detect which university students might discontinue their studies. Focusing on continuous improvement, the team is now channeling efforts toward augmenting the AI to recommend optimal job assignments based on the amalgamation of interview insights, personality traits, and personal backgrounds.
This innovation comes in response to a notable trend in Japanese corporate culture, where businesses collectively onboard new graduates but face a trend where nearly 10% of these fresh employees resign within their first year, and about a third leave before completing three years of service, as documented by Japan’s labor ministry.
Important Questions and Answers:
What factors does the AI model consider in forecasting job tenure?
The AI model considers a variety of factors such as attendance, age, gender, historical data, and possibly others like interview insights, personality traits, and personal backgrounds, to forecast employees’ job tenure.
How does this technology help employers?
Employers can use the AI model to identify employees who might be at risk of leaving the company and provide them with preemptive support to improve retention.
What are the ethical considerations regarding this AI tool?
Using an AI model to predict employee behavior may raise ethical concerns regarding privacy and the potential misuse of the data for discriminatory practices. It’s important to use this tool responsibly and safeguard employee information.
Key Challenges and Controversies:
– Data Privacy: Protecting employees’ personal data is a significant challenge. Ensuring that predictive models do not compromise privacy is essential.
– Accuracy: The accuracy of predictions is always a question. Incorrect predictions could lead to unwarranted consequences for employees.
– Bias: There’s a risk of inherent bias in AI models, depending on the data used to train them. Biased data can lead to discriminatory practices.
Advantages:
– Proactive Retention Strategies: It allows companies to implement retention measures proactively rather than reacting to resignations.
– Data-driven Decision Making: Employers can make informed decisions based on quantifiable data, reducing the guesswork in human resources management.
Disadvantages:
– Depersonalization: Over-reliance on AI may lead to the neglect of human aspects in managing workforce relationships.
– Resistance to Surveillance: Employees may feel uncomfortable knowing their behavior is constantly being monitored and analyzed.
To explore more about AI applications in the field of human resource management, interested readers may visit reputable sources like IBM Watson or Accenture, companies known for developing and implementing AI solutions across various business domains. Please ensure that the URLs are valid and directly linked to the main domain to avoid ‘404 not found’ errors or sending users to incorrect web pages.
The source of the article is from the blog oinegro.com.br