Model Employee Appraisal System with Artificial Intelligence Capabilities

Abstract

An employee appraisal system is one of the most critical components of every organization and its effective management leads to higher productivity among the employees. In practice, there are many possible errors or biases in the performance appraisal process. These errors affect the objectivity of the appraisal to a large extent. The most common errors in appraisal include the Halo Effect and Horn Effect, leniency Error (Loose rater), the Error of Strictness (Tight rater), the Central Tendency Error, the Recency Effect, the Contrast Error and the Similarity (similar-to-me) Effect. The aim of this research project is to develop an intelligent IT system to address the issues of existing employee appraisal system. The system would be seamless and reduce human intervention to minimal. The system can be realized as a dashboard. Outcome of this research can be applied in today's businesses. The design of the system mentioned here is a process to ensure that the rater bias is limited. This type of system would be more beneficial for larger organizations with thousands of employees. A limitation could be that this type of application can be overhead for small companies.

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