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Rapidly developing artificial intelligence (AI) technologies provide great convenience for all departments. Human resources (HR) is just one of these departments and is also the subject of this article. The HR team's job is not easy because it is difficult to evaluate people. It is problematic issue that people evaluate other people. Also, as you know it is often not possible to be completely fair. Being fair is one of the biggest problems of human resources. No unfair organization will be healthy and long-lasting. Therefore, HR responsibilities are very crucial and essential.
As humans, we are very prone to making emotional decisions. In daily life, we make many of our decisions under manipulation. But if the point is the future of the potential employees and the welfare of the company, we can never take any chances. This is one of the places where artificial intelligence can relieve human resources critical responsibilities.
A machine learning (ML) algorithm trained with quality, unbiased and balanced data sets can probably make much more consistent and rational decisions than humans. Moreover, as you can imagine, in a much shorter time than humans. However, HR data can often be messy and unstructured. For example, unstructured data such as resumes, documents, images, videos, texts, messages. As you know, pre-processing and modeling of unstructured data is not easy.
In this article, the relationship between AI and 3 possible topics (tasks) where human resources have problems are mentioned.
1 - Video Interview Analysis
Video interviews are done with the employee candidates before they are employed. It is generally not possible to watch all videos in a limited time before evaluating them. Too much video takes up a lot of space on your hard drive, and you may want to print the transcript of the videos. If you do not get support from AI to convert videos to text, this job can take long time and is extremely tiring.
Even if you have transcripts, isn't it exhausting to review and evaluate too many texts? With artificial intelligence, you can run a natural language processing project on texts and score the speech texts. On the other hand, you can run image processing project to analyze emotions such as nervous, excited, sad, happy etc. from videos.
2 - Churn Analysis
No relationship on earth lasts forever. Human relations in the business world are very prone to be problematic. For example, most employees feel that they are not getting the salary they deserve. In addition, employees think that the employer is not treating them fairly. Moreover, employees may be subject to mobbing.
HR should be aware of possible problems in the workplace and should take preventive actions. Identifying these problems will both increase efficiency and prevent possible resignation. As you know, an unexpected resignation creates a difficult situation to organize during a busy period.
At this point, churn analysis is the evaluation of a company to reduce the employee loss rate. Therefore, one of the best preventive actions is churn analysis.
3 - Resume Analysis & Evaluation
Do you know that thousands of people leave resume for a job where only one person will be employed? The HR department never has enough time to view all resumes. Often, they use a rule-based filtering to reduce these resumes. However, this filtering process may not always work as healthy and as desired. Even after filtering resumes, it is impossible to allocate same time and care for each resume.
In this case, a natural language processing (NLP) project can be developed to score resumes. Thus, only the resumes with the highest scores are ranked and can be focused on. So, time and energy are saved.
These examples can be extended and increased. However, the solution of the problems we encounter in real life is not as simple as described. If you need a model that makes successful predictions, remember that you should take care to ensure that your data set is balanced, of high quality and unbiased. On the other hand, pay attention to the need to adjust the model that suits your needs with the most optimal parameters.
Some deep learning algorithms may be like a black box and have no explanation. Good example is the Amazon came to the fore with secret artificial intelligence that biased women. By 2015, the company realized its new system was not rating candidates for software developer jobs and other technical posts in a gender-neutral way. Machine learning algorithms that we expect to behave fairly can cause disaster when not developed with quality neutral data and correct techniques. So artificial intelligence is not an easy-to-use magic wand.
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