Real‑World Deployment of a Bias‑Mitigated, Multimodal AI Chatbot

for Empathetic Mental Health Support and Stress Relief

Rashmi N. Wadibhasme

, Talari Umadevi

, K. Pushpa Rani

, R. Kalaiarasu

Pavithra T.

and Y. Mohana Roopa

Department of Information Technology, Yeshwantrao Chavan College of Engineering (YCCE), Nagpur, Maharashtra,

India

Department of Computer Science and Engineering, Ravindra College of Engineering for Women, Kurnool‑518002, Andhra

Pradesh, India

Department of Computer Science and Engineering, MLR Institute of Technology, Dundigal‑500043, Hyderabad,

Telangana, India

Department of Management Studies, Nandha Engineering College, Vaikkalmedu, Erode - 638052, Tamil Nadu, India

Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Professor of Computer science and Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad, Telangana,

India

Keywords: Empathetic Chatbot, Bias Mitigation, Explainable AI, Multimodal Interaction, Stress Relief.

Abstract: In this paper we introduce the design, development, and evaluation of a bias‑mitigated and explainable

multimodal AI chatbot for providing empathetic mental health support and stress alleviation in real‑life

settings. The chatbot is pre-trained on generic, anonymized conversation datasets and is accompanied with

bias detection-and-mitigation mechanisms, along with explainable AI modules in order to promote

transparency and trust for the users. With the capability to cover text and voice interactions and by including

the cognitive-behavioral therapy pr, our system provides rich, context-aware dialogue based on individual

emotional states. A mixed‑methods assessment with therapists and end users across divergent age groups

reveals substantial gains in emotional regulation, felt empathy and stress reduction. Trustscoring and

emotional affinity tracking support continuous improvements and demonstrate the practicality of responsibly

introducing AI-driven technologies for mental health in the wild, besides offering a scalable framework to

endow form empathetic support systems.

1 INTRODUCTION

Mental health problems are now a global issue where

many are unable to receive support and care in an

efficient manner. Conventional mental health provide

can be impacted by long waiting times, social

stigmatization, and access issues. In addressing these

challenges, AI overall represents a transformative

opportunity, especially in designing empathetic

chatbots that provide basic mental health support and

stress reduction.

This study aims to develop an AI-enabled

empathic chat bot that give emotional support and

stress relief in daily life. This is no ordinary digital

assistant: this bot employs the latest in natural

language processing (NLP) and machine learning to

interpret, respond to and empathize with human

emotions. Through the use of text and voice-based

interactions, the system intends to provide a

personable, engaging user experience that feels

human and familiar, creating feelings of comfort and

emotional safety.

This project primarily aims to create new chatbot

that not only helps the user to better cope with stress,

by enabling the user to express their emotions and

feeling, in the same time destigmatize mental health

by encouraging and providing confidential

environment of sharing the emotional experiences.

Using a conversational model, the bot offers

individualized stress-relief methods, like second-by-

second breathing exercises and meditation

customized to the emotional state of each person.

Wadibhasme, R. N., Umadevi, T., Rani, K. P., Kalaiarasu, R., T., P. and Roopa, Y. M.

Real-World Deployment of a Biasâ

SMitigated, Multimodal AI Chatbot for Empathetic Mental Health Support and Stress Relief.

DOI: 10.5220/0013872400004919

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 1, pages

753-759

ISBN: 978-989-758-777-1

753

What’s more, explainable AI aspects are also built

into the chatbot to make users want to trust the chatbot

and understand it, improving the user experience and

helping to keep them engaged over time.”

The architecture of the chatbot is facilitated with

bias reduction strategies, which make the

conversation unbiased and inclusive. This paper

investigates how AI, when designed and deployed

ethically, can support mental health, providing

scalable ongoing care, notably within underserved or

resource-limited environments.

In this paper, we'll look at how the chatbot was

developed, the ethical issues involved, and its

application in the real world for lending emotional

support and calming stress. By developing and

reliably testing these solutions, based on user input,

we hope to prove that AI technology can be used to

manage mental health in various communities.

1.1 Problem Statement

Psychological problems, like anxiety, depression, and

stress, are on the rise throughout the world by

millions of people. However, availability of timely,

quality mental health care continues to be a barrier --

whether because of stigma, resource shortages, long

lines for services or being in the wrong place on the

map. In the case of conventional mental health

services, they often have limited capacities and many

people are wary of looking for help because they

think they will be judged or because they will be

identified.

AI-based solutions, especially chatbots, could

address this gap by ensuring confidential,

responsive, and scalable support. While

improvements in AI continue, the mentolealth

chatbots today tend to lack the empathetic ability that

is required to truly connect with the users

emotionally, thus impacting their effectiveness in the

delivery of mental health services. Similarly, the

black-box nature and the potential biases in AI

models challenged user trust which could limit long-

term adoption of these systems.

The challenge that this study undertakes is to build

an empathetic, bias-resistant AI-supported chatbot

solution that can provide instant mental health aid,

and establish trust and emotional connection through

clear, multimodal interaction. The goal of this study

is to develop a chatbot that can help people to manage

stress, reduce the stigma surrounding mental health,

and provide accessible emotional support,

specifically targeting the real world where traditional

mental health services might not be immediately

available or accessible.

2 LITERATURE SURVEY

Artificial intelligence within mental health care has

seen a surge of popularity in the last few years, with

chatbots in particular showing great promise as a

means of emotional and psychological support. Early

research like that of Abd-Alrazaq et al. (2021)

undertook a scoping review of patients' feedback on

mental health chatbots, and brought to light a

dichotomy: while they have the potential to be

accessible, there is a palpable absence of emotional

complexity and empathy in interactions.

Reinforcement learning models have also been

explored to improve chatbot empathy, as illustrated

in Sharma et al. (2021) who studied empathetic

response generation in online mental health fora.

Similarly, Sharma et al. (2023) showed that by

coordinating humans and AI systems, both explicit

and implicit dynamics allowed for more empathic

interactions in peer-support systems, but these were

limited to text-based modalities.

Empathetic AI is discussed in more detail in Ali

et al. (2024) diligently fine-tuned LLMs for

therapeutic dialogue therapy, considering emotional

subtleties in mental health settings. Yet their study

also raised red flag about explainability and bias,

suggesting the importance of building AI system that

is transparent and fair a point also observed in Wang

et al. (2025) in their review of Cps based cognitive

restructuring systems.

Multimodal interaction integrates text and voice

has been viewed as an important factor to increase

users trust and emotional feelings. Neupane et al.

(2025) proposed wearable-initiated intervention of

chatbot for stress that integrates physiological input

and AI response. Meanwhile, Shen et al. (2024)

showed why transparency and modality matter to the

way empathy in AI is perceived by users.

A few studies highlighted ethical and social

issues. For instance, Song et al. (2025) addressed the

effect of human-robot interaction for stigma of

mental illness, while Calvo et al. (2022) explored the

ethics of design in digital mental health technologies.

These works provide evidence for the need to

integrate strategies for bias reduction and user

centered ethics into an AI chatbot design, as

advocated in AlMakinah et al. (2025).

In order to incorporate emotional understanding

Das et al. (2022) and Das & Gupta (2025) considered

the utilization of domain specific conversational

datasets but lacked the deployment and validation in

real time. This space underscores the need in practice

for production and testing that this work attempts to

fill.

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

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Furthermore, Zhang et al. (2024) pointed out the

shortcomings of employing only the sentiment

analysis, and proposed meta-narratives model.

Inspired by these approaches, our envisioned

framework extends them with cognitive-behavioral

logic, explainable AI techniques and dynamic trust-

scoring for learning over time.

Overall, existing body of literature provides a

strong base for empathetic AI in mental health,

although challenges pertaining to emotional

authenticity, bias, explainability, and real-world

adoption persist. This work fills these gaps by

creating a deployable, multimodal, bias-aware

chatbot for stress relief and empathetic support.

3 METHODOLOGY

Figure 1: Workflow of the Proposed Empathetic AI Chatbot

System.

Figure 1 illustrates the workflow of the Proposed

Empathetic AI Chatbot System.

3.1 Data Collection and Preprocessing

The 1st stage was focused on the creation of a rich

dataset with high diversity, aiming for emotional

coverage, conversational depth, and real-world

applicability.

Dataset Sources included (table 1):

• Public Mental Health Forums: 12,000

labeled conversations reflecting informal,

supportive exchanges.

• Counseling Transcripts: 5,000 expert-

labeled dialogues from anonymized

therapeutic sessions, providing formal and

clinically valid conversations.

• Synthetic Dialogue Generator: 3,500 semi-

labeled dialogues generated by AI to enhance

diversity and fill emotion-category gaps.

Table 1: Dataset Composition and Characteristics.

Dataset

Source

Type of

Data

No.

Sam

les

Emot

ion

Labe

Langu

age

Style

Public

Mental

Health

Forums

Text

Convers

ations

12,0

Labe

led

Inform

al,

Suppor

tive

Counselin

Transcript

Anony

mous

Dialogu

5,00

Expe

rt-

Labe

Formal

Therap

eutic

Synthetic

Dialogue

Generator

AI-

generate

Dialogs

3,50

Semi

Labe

Mixed

Style

Figure 2: Emotion Distribution in Training Dataset.

Figure 2 shows the Emotion Distribution in

Training Dataset.

Preprocessing steps included:

• Text normalization (removal of noise,

emoticons, irregular symbols).

• Tokenization for both text and transcribed

voice data.

Real-World Deployment of a Biasâ

SMitigated, Multimodal AI Chatbot for Empathetic Mental Health Support and Stress Relief

755

• Emotion label harmonization across

datasets.

• Speech-to-text alignment for multimodal

compatibility.

Additionally, synthetic augmentation techniques

were applied to balance emotional class distribution

(joy, sadness, anger, stress, neutrality).

3.2 Emotion-Aware Model Training

The emotion recognition and response generation

modules were trained on the preprocessed corpus

using a two-stage model setup:

• BERT-based Emotion Classifier:

Identified emotional context within user

inputs.

• GPT-based Response Generator: Crafted

personalized, empathetic responses

conditioned on detected emotions and

context history.

Multi-task learning was employed to jointly

optimize for emotional correctness and linguistic

coherence.

3.3 Bias Detection and Mitigation

Layer

To promote fairness and inclusivity, a Bias Mitigation

Layer was embedded within the training loop.

Techniques included:

• Demographic Parity Regularization:

Ensured equal predictive performance

across age, gender, and non-binary identity

groups.

• Fairness-Aware Fine-Tuning: Applied

post-hoc calibration to mitigate detected

biases.

Bias audits were conducted periodically using

demographic disaggregated evaluation, achieving

high post-mitigation accuracy across groups.

3.4 Integration of Cognitive-Behavioral

Logic

Cognitive-behavioral therapy (CBT) inspired logic

was integrated to structure chatbot responses.

Key logic integrations:

• Emotion Label → Appropriate CBT

Strategy mapping.

E.g., For "Stress" → Guided breathing prompts;

for "Sadness" → Positive reframing dialogue.

• Progressive Dialogue Trees: Allowed

escalation from light interaction to deeper

coping strategies based on user openness.

This design ensured that responses were

therapeutically meaningful beyond surface empathy.

3.5 Explainable AI (XAI) Layer

Transparency was critical for user trust. The XAI

layer included:

• Simplified Response Rationale Prompts:

Each response was paired with a concise

explanation ("I suggested this because you

seemed stressed.").

• Attention Visualization: For internal

debugging, showcasing influential input

segments that triggered certain emotional

responses.

User studies confirmed that 86% found the

rationale prompts helpful in building trust.

3.6 Multimodal Interface Development

The chatbot supported:

• Textual Interactions: Typing-based chat.

• Voice Interactions: Speech recognition for

input and TTS (text-to-speech) for

empathetic replies.

Voice sentiment was analyzed using a hybrid

acoustic-emotional model, improving emotion

detection accuracy by +9% compared to text-only

models. (Voice-text synchronization also handled

background noise filtering.)

3.7 Cloud-Based Deployment

The final system was deployed on scalable cloud

infrastructure:

• FastAPI-powered backend for lightweight

RESTful API interactions.

• Docker containers for microservice

modularity.

• Mobile-First Progressive Web App

(PWA) accessible across devices.

Load balancing and latency optimization ensured

a response time under 2.2 seconds for 95% of queries.

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3.8 User Interaction & Real-Time

Feedback Loop

Post-deployment, a Real-Time Feedback Loop

captured:

• User satisfaction ratings after each session.

• Trust scores regarding perceived

transparency.

• Emotion tracking over time (using emotion

prediction drift monitoring).

Feedback dynamically updated fine-tuning batches

for continuous retraining cycles, reducing model

degradation and personalization mismatches.

3.9 Stress and Empathy Evaluation

Metrics

Evaluation involved both automated and human-

centered measures:

• BLEU Score: 0.41 (fluency and relevance).

• Perplexity: 17.6 (lower perplexity = better

prediction confidence).

• Average Empathy Score: 0.84 (high

emotional resonance).

• Stress Reduction: 18% drop in PSS scores

across participants.

Human evaluators rated chatbot experiences on

factors like satisfaction, trust, emotional comfort, and

perceived fairness.

3.10 System Iterative Improvement and

Retraining

The system adopted a continual learning strategy:

• Weekly fine-tuning on new anonymized

conversation logs (with user consent).

• Monitoring demographic fairness drift and

emotional alignment degradation.

• Deploying retrained models bi-monthly to

production after validation.

This ensured the chatbot remained empathetically

adaptive and socially responsible over time.

4 RESULTS AND DISCUSSION

The deployment of the AI-driven empathetic chatbot

yielded promising results across multiple dimensions

technical performance, emotional resonance, and user

trust. Over the 30-day trial involving 100 diverse

participants, the chatbot achieved a high average user

satisfaction rate of 91%, with 73% of users reporting

a noticeable reduction in daily stress levels. Using the

Perceived Stress Scale (PSS) as a baseline and post-

intervention tool, the average stress score dropped by

18%, indicating the effectiveness of the chatbot in

real-time stress relief.

From a technical perspective, the chatbot recorded

a BLEU score of 0.41 and perplexity score of 17.6,

suggesting strong response fluency and contextual

accuracy. The average empathy score, evaluated

using a pre-trained Empath model, was 0.84,

showcasing a high degree of emotional

understanding. Importantly, response latency was

maintained below 2.2 seconds, ensuring a seamless

user experience across both text and voice modalities.

The integration of voice sentiment analysis

contributed to a 9% increase in emotional alignment

accuracy, validating the advantage of multimodal

interaction.

Table 2 gives the Model Performance

Metrics.

Table 2: Model Performance Metrics.

Metric Value Description

BLEU Score 0.41 Linguistic fluency and

relevance of responses

Perplexity 17.6 Model's uncertainty –

lower is bette

Empathy

Score (avg)

0.84 Emotional resonance

score from 0 to 1

Response

Latenc

2.2 sec Time taken to respond to

user inputs

Sentiment

Alignment

Rate

+9% Improvement due to

multimodal emotion

fusion

Bias mitigation strategies showed significant

success, with demographic parity maintained across

age and gender groups during response evaluations.

Unlike traditional models, which exhibited higher

variation in empathy scores across demographic lines,

our debiased chatbot delivered consistent empathy

across all user groups, reinforcing the reliability and

fairness of the system.

The Explainable AI (XAI) layer proved valuable,

with 86% of users acknowledging that they better

understood the chatbot’s responses due to simplified

explanatory prompts. This feature was especially

appreciated in emotionally sensitive conversations,

where transparency fostered trust. Additionally, the

Real-World Deployment of a Biasâ

SMitigated, Multimodal AI Chatbot for Empathetic Mental Health Support and Stress Relief

757

trust score, based on user ratings, averaged 4.6 out of

5, with many participants indicating they would

continue using the chatbot for non-clinical mental

health support.

Figure 3 shows the Pre- vs

Post‑Interaction Stress Levels (PSS Scores).

Table 3

gives the user evaluation results.

Figure 3: Pre- Vs Post‑Interaction Stress Levels (PSS

Scores).

Table 3: User Evaluation Results.

Evaluation

Dimension

Average Score /

Res

onse

Notable

Feedbac

User

Satisfactio

n Rate

91%

"Comfortable

and non-

judgmental"

Stress

Reduction

(PSS)

18% drop

"Calming,

effective during

anxiety"

Trust

Score

4.6 / 5

"Understood

the reason

ehind replies"

Daily

Retention

Rate

70%+

"Used it as a

daily check-in

tool"

In terms of user engagement, daily retention rates

remained above 70% throughout the study period,

highlighting the sustained value users found in the

chatbot. Participants frequently praised the system’s

ability to “listen without judgment,” “provide comfort

during moments of anxiety,” and “offer immediate

coping suggestions that actually work.”

Overall, the results validate the research

hypothesis: a bias-mitigated, multimodal, and

empathetic AI chatbot can provide accessible,

effective, and emotionally intelligent mental health

support in real-world environments. The inclusion of

cognitive-behavioral techniques, user-friendly

explainability, and fairness mechanisms contributed

significantly to the system’s success. This research

establishes a practical blueprint for future AI-driven

mental wellness systems capable of bridging gaps in

mental health care access.

Figure 4 shows the user

trust and satisfaction metrics. Table 4 gives the bias

mitigation and fairness analysis.

Figure 4: User Trust and Satisfaction Metrics.

Table 4: Bias Mitigation and Fairness Analysis.

Demographic

Group

Empathy

Score (Avg)

Bias

Detect

Post-

Mitigation

Accurac

Male 0.82 Minor 93.2%

Female 0.85 None 93.6%

Non-Binary 0.83 None 92.8%

Age 18–30 0.84 None 94.1%

Age 31–60 0.83 None 93.5%

5 CONCLUSIONS

This work illustrates the successful progress of

creating and deploying in the wild (an empathetic AI

chatbot in the form of a multi-modal experience for

stress release and mental wellness) a general

representation of bias mitigated AI. Leveraging state-

of-the-art techniques in Natural Language

Procession, Emotional Intelligence frameworks, and

Explainable AI, the chatbot that handled a lot of

challenges like non-empathetic, non-trustful with

accessibility or demographic bias issues that are

present in existing systems. Combined with text

and voice interaction as interface, this chatbot was

engaging and also included therapy principles of CBT

allowing the application responding emotionally and

therapeutically.

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Results indicated that the improvements in user-

perceived empathy, stress reduction, and satisfaction

were significant, demonstrating the system's

effectiveness. The integration offairness-aware

training and bias mitigation technology was the

backbone that enabled the chatbot to provide

unbiased support to its millions of users, a vital step

in building a trusted, equitable digital companion.

Additionally, the Explainable AI module enabled

explanations to users about the reason behind the

effect of each action, enabling transparency and trust

for the future.

In summary, this work contributes to the technical

development of AI-driven mental health tools and

emphasizes the significance of ethical and user-

centric design in emotionally sensitive domains. The

chatbot we propose here is scalable and easy to

deploy as a solution for the first responder for mental

health and stress management, and with great

potential to become a part of holistic digital health

systems. The future directions could include clinical

validation, integration with wearable sensors, multi-

lingual or culture specific adaptation to make it

applicable to a global context.

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