Huarong International Financial (993) Hiring Process & Artifical Intelligence / MBA Resources

Introduction to Human Process and Artificial Intelligence

Why Huarong International Financial Needs to Use AI in Hiring Process

With the advancements of Machine Learning and Artificial Intelligence, the next big question in the field of Human Resource Management is – Should Huarong International Financial use algorithm to hire employees?

Huarong International Financial like most of the other companies in the Investment Services industry is struggling for talent identification.

Huarong International Financial is finding it hard to find right people that match both - the vision of the organization and the right attributes needed to be successful at the jobs especially at the highly skills oriented top level positions.

This mismatch in modern day organizations is resulting into many people ending up in positions and jobs that are at best un-inspiring. Management consultants in the Investment Services industry calls them – bullshit jobs, where people find no value other and responsibilities that leave a vacuum inside.

We at Oak Spring University believe that predictive analytics and assessments tools are underutilized at both organization level and industry level. These data driven tools can help Huarong International Financial to reduce prevalent prejudice in the hiring process , remove interviewer’s biases, and help in reducing discrimination at all level.

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What Huarong International Financial can Achieve using AI in Human Resource Management

AI can help Huarong International Financial to predict, understand, evaluate, and match people at scale. Thus lowering the overall cost of running Human Resource Management department.

AI can help Huarong International Financial to structure the whole recruitment, selection, and hiring process where each applicant has to go through the same process and judged without human biases of the moment.

Machine Learning and Artificial Intelligence can help Huarong International Financial to significantly reduce the cost of identifying talent by providing better and more comprehensive predictions than human judgment which is influenced by individual heuristics and biases.

AI and Machine Learning can help Huarong International Financial to make its internal systems more meritocratic and help individual to better understand their job profiles and what is expected of them.

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Machine Learning functions in Human Resources Management process

Most of the companies at a certain level have already started doing it and Huarong International Financial is no different. Some of the early opportunities fields where companies are utilizing algorithms for are -

Scanning through the profiles using Keywords related to the job profile

Trained Algorithms can be used by HR department in Huarong International Financial to assess various characteristics of candidate’s voice – vocal pitch, loudness, and intensity. It can also help in assessing body movements – posture, hand movements, and gestures.

AI and Machine Learning can help HR managers at Huarong International Financial to build a reliable connection between what candidates say during the interviews and their personality traits, ability and performance at job.

Using technology to create a highly structured and standardized interview experience, every candidate can be presented with the same set questions and given the same opportunity to express their talent, which ultimately improves the video’s predictive utility.

AI algorithms simply leverage the same cues that humans do. The difference between humans and AI is that the latter can scale, and can be automated. What’s more, AI does not have an ego that needs to be managed.

AI and Machine Learning can help Huarong International Financial to have access to wider and more diverse talent.

HR managers at Huarong International Financial can utilize AI and Machine learning systems strength in - mining job applicant’s facial expression, body language, along with the responses he/she provides in the live interview. The analytics based approach can help in predicting the job performance and areas where HR can focus to improve the employee’s capability and tools that are needed for that.

Sadly, the AI bias ignorance is harming both the candidate and the organization. The HR departments that realize that science and data, and not intuition or instinct, should be the basis for decisions will attract and retain the best talent.

Scanning social data and public data to assess the applicant profile

Initial research has shown that algorithms are reasonably successful at predicting the over all happiness of an employee at work based on the facial expression, communication initiatives, inter-personal relationship using interaction among employees, and stress tolerance. These are some of the leading indicators of predicting success at work place and Huarong International Financial integrating these factors in evaluation process can help it to outperform the competition.

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Challenges & Dangers of Using AI for Hiring

With the rise of gig economy in the past 5 years the challenge and stakes for HR department in fortune 500 organizations have increased significantly. At present the job market in United States is highly inefficient – there are 6 million job seekers for 7 million jobs.

This inefficiency points toward an inability of the HR processes to match job seekers’ capabilities and job specific requirements. AI and Machine Learning will certainly bring its own challenges and dangers that HR managers at Huarong International Financial needs to be aware of -

Many organizations are facing HR backlash as even though AI and Machine Learning algorithms are used during the hiring process but senior management refuse to accept the recommendations because the belief in higher human expertise in judging people.

AI and Machine Learning algorithms can end up learning all sorts of harmful biases of their own, depending on the data they’re trained on, among other factors. Huarong International Financial needs to pay attention to how these algorithms are trained, the data used, the biases in that data, and should regularly audit them for potential bias.

Body language, voice tone and other assessments by machine may provide a reliable solution but it can lead to letting go a really talented person with a vision that doesn’t fit the company’s present algorithm.

Many organizations are facing HR backlash as even though AI and Machine Learning algorithms are used during the hiring process but senior management refuse to accept the recommendations because the belief in higher human expertise in judging people.

Human biases will still be there and be reflected in the algorithm that each organization will develop. Huarong International Financial needs to find out where its processes are found wanting at present and how it can streamline those processes using AI and Machine Learning.

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Conclusion

Although the scientific research in hiring process and overall HR processes is still in its infancy, Huarong International Financial can explore areas where it is showing promises such as – selecting applications based on CV for interview. Some of the other areas where Huarong International Financial needs to be a bit cautious are –

There should be a human oversight over all the process and a timely and continuous audit should be put in place to correct the algorithm biases.

Legal regulations and other ethical issues should be addressed in designing these algorithms.

Huarong International Financial should start with using AI and Machine learning in areas where hiring process is made better by them.

Candidates must be fully briefed about the processes so that there is complete transparency in the hiring process. They should have an option of opting in the system.

Huarong International Financial should fully protect and keep safe all sensitive data, and personal data. There should be provisions where non-sharing of data is mandated.

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SWOT Analysis / SWOT Matrix of Huarong International Financial

PESTEL / PEST / STEP Analysis of Huarong International Financial

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