ICACT20230154 Slide.16
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Thank you for your attention.
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ICACT20230154 Slide.15
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AI algorithms that correctly assess the feasibility and application of accuracy achieved for set objectives. More research is required to evaluate and implement the validity of any AI model, as it may have some bias in complex scenarios that require human inspection before making a final decision.
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ICACT20230154 Slide.14
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Using an algorithm, the HR department must construct a model with a minimum of 99 percent accuracy to eliminate certain biases and promote justice in particular departments of the organization. It is possible to employ an AI-based algorithm to ensure sure that unprocessed data helps in drawing important conclusions and getting genuine insights that support organisational development and decision-making.
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ICACT20230154 Slide.13
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The goal is to assist in making judgments that are founded on objective data analysis rather than on subjective considerations. Since HR technology is still in its infancy, it has become imperative to implement analytical machine learning to ensure increased productivity, smooth transitions between positions, behavioral assessments, learning, and development, recruit the most qualified candidates, and reap countless other benefits.
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ICACT20230154 Slide.12
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The implementation of informed machine learning necessitates the use of a hybrid information source that is comprised of two components: data and previous knowledge in Figure 8 (Rueden, et al., 12 May 2021) . In conventional machine learning, knowledge is utilized for data preparation and feature engineering; however, this process is intricately linked with the learning process. A collection of HR software called a human resources management system (HRMS), sometimes known as a human resources information system (HRIS) or people management (HCM), integrates several systems and processes to make managing an organization, workflows, and data easier. Businesses utilize HR technology to automate a variety of critical HR processes, such as increasing work satisfaction, monitoring skill and training requirements, processing payments, recruiting, managing compensation (total rewards), and maintaining business information.
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ICACT20230154 Slide.11
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The implementation of informed machine learning necessitates the use of a hybrid information source that is comprised of two components: data and previous knowledge in Figure 8 (Rueden, et al., 12 May 2021). In conventional machine learning, knowledge is utilized for data preparation and feature engineering; however, this process is intricately linked with the learning process.
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ICACT20230154 Slide.10
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The HRSM Competency Model explains what it takes to be a successful human resources professional throughout the performance continuum, around the globe, and across all career levels, job roles, and job functions. HRSM worked with HR professionals from all over the world to come up with the model.
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ICACT20230154 Slide.09
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The HRSM Competency Model explains what it takes to be a successful human resources professional throughout the performance continuum, around the globe, and across all career levels, job roles, and job functions as shown in the next slide.
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ICACT20230154 Slide.08
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We rely on complex algorithms to handle our day-to-day decisions, and algorithmic decision making is becoming increasingly common in a variety of disciplines, including medicine, financial markets, and others. On the one hand, this proves that the functionality and performance of complex machine learning models are continually upgraded. Algorithms are pervasive in the society in which we live. Algorithms are the tools that can simplify decisions, boost efficiency, and provide convenience. These algorithms first organize the thought and activity, and then they are able to accurately output a decision for the human resources department of the firm.
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ICACT20230154 Slide.07
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The data inflow to the organization begins with applicants or aspiring employees creating an account and entering detailed information, selecting the vacant job and associated skills to the job then submitting their Cv. Then another decision is made, and if passed the screening process, proceed to the next phase; interview, if not terminated as shown in the next slide.
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ICACT20230154 Slide.06
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Data processing is the process of transferring data from one medium to another that is more accessible and desirable, hence increasing its value and insight. This entire method can be completed using machine learning techniques. Computational analysis and analytical comprehension Data Because of the importance of preparation in machine learning, data correctness and the knowledge that can be learned from it have a significant impact on our model's performance and learning ability. As a consequence, prepare our data before feeding it input.
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ICACT20230154 Slide.05
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In addition, HR today uses advanced primarily for administrative duties. People need to consider how cutting-edge technology can be used in place of work. This study demonstrates how certain assumptions underlying interactions with interactional fairness can be called into question by the use of AI in decision-making.
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ICACT20230154 Slide.04
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AI in HR Decision-Making:
For critical HR procedures, organizations are investing in AI and cognitive computing. No industry has more demands for data administration and analysis than HR. HR staff should be pushed to let go of their concerns to overcome the challenges.
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ICACT20230154 Slide.03
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The Objective is:
• The Objective of this study was to Develop an AI model to enhance HR decision-making for accurate forecasts using a Machine Learning Algorithm, since many organizations find it difficult to make accurate predictions using the information provided by the company, and so using AI to find new solutions to old problems is the way forward
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ICACT20230154 Slide.02
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Outline:
Objectives,
Methodology,
AI in HR Decision-Making,
Model Analysis,
HR Decision Algorithm,
Analysis of HRSM,
Conclusion.
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ICACT20230154 Slide.01
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AI Model to Improve HR Decision-Making with Machine Learning Predictions Algorithm:
Paper ID : 20230154,
Registration Code : 202212210005,
Presenters:
Akwi Helene Fomude,
George K. Agordzo
Anhui University of Science and Technology, China.
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