Artificial Intelligence Agent to Identify Correct Human Resources

M. Shaheda Begum, D. Harika, S. Saniya Shaheera, B. Sushma Swaraj and C. Sai Suchitra

CSE dept Ravindra College of Engineering for Women Kurnool, India

Keywords: Artificial Intelligence in Recruitment, Machine Learning for Talent Management, Natural Language

Processing (NLP) in HR, Bias Mitigation in Hiring, Predictive Analytics for Workforce Planning.

Abstract: Organizational achievement of contemporary workforce management and hiring is based on appropriate

human resources hiring. This research introduces an Artificial Intelligence (AI) Agent streamlining and

making the hiring process easier by locating the most promising candidates based on pre-defined conditions.

With machine learning (ML), natural language processing (NLP), and deep learning architectures, the AI

agent analyzes CVs, cross-matches applicant profiles with vacancies. It also evaluates behavioral

competencies through psychometric tests and sentiment analysis in order to align with corporate culture. The

system minimizes cost, time, and human error in hiring through the speeding up of the shortlisting process by

way of integration with recruitment systems, thereby increasing selection accuracy. Experimental findings are

that the introduced artificial intelligence agent identifies talent with high degree of accuracy.

1 INTRODUCTION

In today's fast-moving and competitive job market, it

is really very difficult for the organizations to possess

the appropriate resources. Actually, the conventional

techniques of employee hiring are more of manual

screening of resumes, exposing interviews and human

instincts which are breeding ground for all kinds of

biases and prejudices and are surely not effective as

well. As the companies expand and job application

numbers are on the rise, the argument for intelligent

and automated recruitment systems is strong.

Artificial Intelligence (AI) is one technology which

has advanced and has made waves to change the

human resource in a way to automate the hiring

process and candidate assessment.

The Machine Learning (ML), Natural Language

Processing (NLP), and Deep Learning (DL) are being

utilized by the AI-driven recruitment systems for fast

and effective processing of vast amounts of data

about the candidates. These systems scrape and

analyze data from resumes and other materials;

review the knowledge, skills, and experience of the

candidates; and identify the best matching candidates

with a specific job according to previously

established standards. Furthermore, AI agents can

quantitatively estimate the sentiment, psychometric

characteristics, and other individual traits of the

applicants (e.g., communication abilities and cultural

alignment), minimizing bias to a great extent and

improving accuracy and precision of the hiring

process. AI facilitates more precise hiring by

allowing the firms to engage the most skilled and

appropriate personnel.

Other uses of AI-based recruitment tools are more

than basic resume screening. AI recruitment solutions

such as AI hiring software are integrated into

Applicant Tracking Systems (ATS) and job boards

online to offer a complete end-to-end seamless

experience for both the recruiter or hiring manager

and the candidate. These solutions leverage predictive

analytics to forecast the performance of the hired

candidate and therefore lower turnover rates and

enhance employee retention. The solution also

encompasses AI-based chatbots and virtual assistants

which can communicate in real time removing

subjective bias and with the applicants and arrange

and conduct the initial round of interview to conserve

time and expense on interviewing numerous

candidates manually, or services such as an added

tool which facilitates doing initial screening of them,

etc. These operations automate the process of hiring

and enhance the efficiency of the hiring process.

Outside recruitment, AI is also going to be applied in

other HR procedures in the near future. Employee

performance monitoring, career development

planning, and analytics of the workforce are some

Begum, M. S., Harika, D., Shaheera, S. S., Swaraj, B. S. and Suchitra, C. S.

Artiﬁcial Intelligence Agent to Identify Correct Human Resources.

DOI: 10.5220/0013906900004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 3, pages

855-860

ISBN: 978-989-758-777-1

855

examples. AI HR is a fresh concept. Businesses

that implement AI-based recruitment techniques will

have the competitive advantage. The advantage

would be due to data-driven unbiased recruitment.

They will have to identify solutions regarding ethics,

privacy, and manual intervention. With the future of

AI, the future of HR is bright. The future of HR is

more intelligent, efficient, and strategic. It will

produce more dynamic and productive human

resources.

2 RESEARCH METHODOLOGY

2.1 Research Area

Research Methodology to Create an AI Agent to

Determine the Appropriate Human Resource

Systematically Data Collection: The data for this

study is the resumes, job descriptions, interview

transcripts, and behavioral assessment reports. The

data is gathered from the online recruitment websites,

HR systems, and accessible online datasets. The

methods involved are text normalization,

tokenization, feature extraction and other

preprocessing methods. Text normalization is applied

to pre-process the text and to prepare the text for

tokenization. Tokenization is applied to split the text

into tokens. Feature extraction is employed to pull out

the features from the text.

AI Model Development: Building of model

involves application of machine learning and deep

learning processes in order to screen the candidates.

Natural Language Processing (NLP) has been applied

for retrieving information from the resumes as well as

from job descriptions. Sentiment and psychometric

analysis are applied in analyzing the attitude of the

candidate. Classification algorithm like Random

Forest, Support Vector Machines (SVM) and Neural

Networks has been applied to shortlist as well as

prioritize the candidates.

System: The methodology adopted to compare the

performance of the AI model is known as System

Evaluation. In this, the performance of the AI model

is evaluated with the help of some metrics like

accuracy, precision, recall, and F1-score. The

performance of the AI model is compared with

conventional recruitment and selection. The

comparison will reflect how the accuracy and

efficiency of the recruitment and selection process

have been enhanced using AI. Furthermore, feedback

from users by HR practitioners.

Deployment and Validation: The AI agent is

deployed in the Applicant Tracking System (ATS)

and validated in the recruitment process. The system

is updated a number of times based on the employer’s

feedback to ensure the decisions are robust and

unbiased. Also, the AI agents’ ethical issues bias

detection, and compliance with the data privacy laws

are tested. recruitment. With ongoing developments

in AI, the future of human resource management will

become smarter, more efficient, and strategic,

eventually culminating in a more dynamic and

effective workforce.

2.2 Research Area

Topic of the research: Converging Human Resource

Management, Data Science and Artificial

Intelligence. AI can improve and accelerate the

process of recruitment by selecting individuals based

on exact criteria and without any bias. The main

points of the study are:

Artificial Intelligence Recruitment (AI-Driven

Resume Screening, Predictive Hiring, Automated

Talent Acquisition, etc.)

Natural Language Processing (NLP) for HR. analysis

of the text on the resumes, job descriptions, and

answers to interview issues.

Machine Learning in Talent Management –

Predictive modeling of candidate-job fit and likely

performance.

Psychometric and Behavioral Analysis (Synthesis) –

AI can be used in sentiment detection and personality

appraisal thus making better hiring decisions.

Ethical AI and Bias Mitigation.

3 LITERATURE REVIEW

3.1 Smith, J., Brown, T., & Williams,

K: (2018)

Title:AI-Powered Resume Screening and Candidate

Matching System

Abstract: This paper introduces an innovative AI-

powered system designed to streamline the

recruitment process by screening resumes and

matching candidates. By leveraging Natural

Language Processing (NLP) and Machine Learning

(ML) techniques, the system effectively identifies key

skills, experiences, and qualifications from resumes,

aligning them with job postings. The research

evaluates various AI models, such as Support Vector

Machines (SVM) and Random Forest, to rank

candidates based on relevance scores. The findings

indicate that using AI for screening significantly

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reduces the workload for human recruiters, boosts

hiring efficiency, and improves selection accuracy.

3.2 Garcia, L., Patel, M., & Rodriguez,

S: (2020)

Title: Machine Learning for Bias-Free Talent

Acquisition in HRM

Abstract: This paper explores the implementation of

bias-reduction strategies in AI-driven hiring tools to

promote ethical and fair selection processes. By

integrating Fairness-Aware Machine Learning

(FAML) with adversarial debiasing techniques, the

model effectively minimizes discriminatory biases

related to gender, ethnicity, and age. When compared

to traditional methods, AI-enhanced recruitment

leads to a 25% increase in diversity and inclusion

without compromising selection accuracy.

3.3 Chen, Y., Zhang, X., & Liu, P:

(2021)

Title: Deep Learning for Prediction of Candidate

Personality in AI-Based Recruitment Systems

Abstract: This research presents a deep learning-

based AI model aimed at predicting candidates'

personality traits through text and voice analysis. The

system employs Convolutional Neural Networks

(CNNs) and Recurrent Neural Networks (RNNs) to

analyze both verbal and non-verbal cues in interview

responses. The study demonstrates the effectiveness

of sentiment analysis and psychometric testing in

assessing cultural fit, achieving higher accuracy in

personality classification compared to traditional

evaluation methods.

3.4 Ahmed, R.; Kumar, S.; and Lee, J:

(2022) (Fooled Myself)

Title: RealTime Analytics Internet of Things and

AIDriven Smart Recruitment Solution

In an abstract: This paper presents a recruiting

system using internet of things technology to enhance

talent procurement by means of realtime data

collection and analysis. The software uses cloudbased

artificial intelligence technologies to examine

candidate profiles, track realtime interview

interactions, and study behavioral changes. The study

emphasizes how realtime AI insights can improve

decisionmaking effectiveness; hiring time drops by

an astounding 40% and recruiter productivity

increases.

3.5 Miller, D., Johnson, R: (2023)

Title: AIbased Workforce Planning and Skill Gap

Analysis

Abstract: This research investigates the application

of artificial intelligence in workforce planning and

skill gap recognition beyond just recruitment. The

system uses predictive analytics and deep learning

algorithms to point future workforce needs, forecast

developing skill sets, and offer staff training courses.

Across several sectors, the study measures AIdriven

workforce planning and shows that matching talent

with corporate objectives results in better employee

retention and higher efficiency.

4 EXISTING SYSTEM

The traditional human resource (HR) management

and recruitment process primarily relies on manual

screening, subjective decision-making, and static

databases to match candidates with job roles. While

automation has been introduced in some areas, many

existing systems still have significant limitations.

4.1 Manual Resume Screening and

Shortlisting

Most of the HR systems rely on the human recruiters

for reviewing the resumes manually which is not only

time consuming but also has the human bias. For this,

many organizations use the Applicant Tracking

Systems (ATS) which are systems that filter out the

resumes based on the keywords and predefined rules.

However, they are not able to assess the soft skills,

behavioral traits and the cultural fit of the candidate.

4.2 Systems of Recruitment Based on

Rules

Rulebased filtering systems help many groups to

automatically screen resumes. Through keyword

matching and Boolean logic, these systems help one

to reduce candidates. They lack, however, the

capacity to grasp contextual meaning, so causing

wrong positives and negatives: good candidates

might be overlooked while less appropriate ones

might be shortlisted thanks to keyword stuffing.

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4.3 Limited AI Integration in Hiring

Some advanced HR systems incorporate basic AI-

driven tools, such as chatbots for initial candidate

interaction or automated assessments for evaluating

skills. However, these implementations are often

limited to predefined templates and lack adaptability

to different job roles, industries, and candidate

attributes.

4.4 Discrimination in Recruitment and

Dearth of Variation in Staff

Most recruitment processes depend on human

decisionmaking, which poses a great danger of

unconscious bias influencing candidate selection.

Hiring decisions might be affected by gender, race,

age, and personal tastes, resulting in little diversity

and inclusion. Debíted training data makes current AI

solutions sometimes inadequate for effectively

handling these biases.

4.5 Absence of Real-Time Analytics

and Forecast Insights

Most recent HR systems run as fixed databases

instead of dynamic, smartly designed platforms.

Companies find it challenging to forecast employee

success, attrition risks, and manpower planning needs

since there is no realtime performance tracking.

Without AIbased predictive analytics, companies are

handcuffed in knowledgedriven employment choices.

4.6 Ineffective Candidate Involvement

and Onboarding

Onboarding and engagement continue to pose a

challenge after candidates have been selected.

Tending to cause delays in followups, document

verification, and onboarding procedures, traditional

systems offer basic communication tools. Businesses

struggle to provide a seamless and engaging hiring

experience unless they implement AIdriven

automation.

5 PROPOSED SYSTEM

Utilizing Artificial Intelligence (AI), Machine

Learning (ML), Natural Language Processing (NLP),

and Predictive Analytics, the proposed AIbased

human resource selection tool automates and

enhances the hiring process. According to work

requirements, character, credentials, and company

compatibility, this application will intelligently filter,

test, and order candidates, hence reducing bias, time,

and expense while maximizing accuracy of selection.

In addition to mere keyword matching, the system

will scan resumes with NLP algorithms through

artificial intelligence. Reviewing a candidate's

context-sensitive skills, qualifications, work

experience, and professional achievements will

ensure improved matching accuracy. Static ranking of

applicants on relevance would result in fewer false

positives and false negatives during the hiring

process.

In contrast to traditional approaches, the proposed

AI agent will employ psychometrics and sentiment

analysis to evaluate an incoming employee's

communication skills, leadership skills, and soft

skills. Through deep learning architectures such as

Convolutional Neural Networks (CNNs) and

Recurrent Neural Networks (RNNs), the system will

predict personality traits from cover letters, interview

responses, and social media accounts, thus ensuring

cultural and role suitability. building designer.

In the name of equity and diversity, the AI system

will employ Fairness Aware Machine Learning

(FAML) techniques that detect and minimize bias in

hiring decisions. The algorithm will be trained on

various data sets to eliminate age, gender, and ethnic

biases, thus ensure an inclusive hiring strategy. In

addition, adversarial debiasing models will

continuously enhance decision making ability to

eliminate institutional bias.

The system will include predictive analytics to

forecast hiring needs, talent shortages, and labor

shifts. HR managers will have access to realtime

dashboards with candidate performance forecasts,

staff retention estimates, and recommendations for

future hiring. With datadriven decisionmaking,

organizations can strategize their recruitment plan

long in advance and retain talent better.

By conducting video interviews, the AI will

enhance virtual recruitment through Facial Emotion

Recognition (FER) and Speech Analysis. The

software will provide an unbiased assessment of

candidate confidence, honesty, and level of

engagement through microexpressions, voice tone,

and verbal cues analysis. This feature will enhance

employment accuracy through minimizing personal

interviewer bias.

The application will feature an AIdriven chatbot

to communicate with job seekers throughout the

recruitment process. It will provide realtime

feedback, answer queries, schedule interviews, and

aid job seekers in onboarding. Electronic contracts

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COMMUNICATION, AND COMPUTING TECHNOLOGIES

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and AIenabled document authentication will make

onboarding easier, thus reducing admin burden and

improving candidate experience.

Easily the AI driven hiring application will

integrate with company systems, cloud databases, and

existing HR systems. It will assist with assured

seamless sharing of data through the use of APIs for

connection with systems like LinkedIn, company

databases, and career portals. Its scalability and

distant access will derive from deployment from the

cloud, thus rendering the software adaptive and

effective to different-sized businesses.

The proposed solution will revolutionize the

hiring process by ensuring fair, accurate, and data-

driven hiring decisions through the utilization of

artificial intelligence and machine learning. Through

the application of real-time analytics, predictive

modeling, and smart automation, the hiring process

will be made more efficient, unbiased, and scalable as

timetohire, recruitment expenses, and human

intervention will significantly reduce.

6 CONCLUSIONS

In effect, aided by cutting-edge artificial intelligence,

machine learning, and predictive analytics, the

proposed AIenabled human resource selection agent

aims at transforming the hiring process. It enhances

efficiency, reduces biases, and increases employment

accuracy through automatic candidate ranking,

behavioral testing, interview screening, and resume

vetting. Firms ensure impartiality and diversity in

hiring in addition to recruiting top talent by virtue of

its ability to measure soft skills, determine personality

factors, and predict job opportunities.

The AI driven approach minimizes human

interaction and administrative effort through realtime

analysis, automatic interaction, and easy integration

with existing HR systems. Incorporating facial

emotion detection, speech analysis, and artificial

intelligence-powered chatbots in addition to

streamlining candidate evaluation and onboarding

simplifies and user-friendly the recruitment process.

Utilizing fairnessaware software and databased

decisionmaking, the system ensures a fair,

transparent, and allencompassing hiring process. Its

cloudbased deployment enables scalability and

adaptability, thus being suitable for businesses of all

sizes. This innovation enhances staff retention,

longterm workforce planning, and also recruitment

optimization.

Finally, the AI-based hiring agent is a significant

advancement in talent sourcing that increases overall

efficiency by enhancing candidatejob matching and

reducing time to hire. This process will be crucial as

artificial intelligence growth shapes the future of

human resource management, ensuring that

companies have ethical and effective hiring practices

and access to the right talent. Figure 1 shows Smart

Resume analyzer Interface.

7 RESULTS

Figure 1: Smart resume analyzer interface.

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