AI vs. Human Matchmakers: Evaluating Customer Perceptions, Trust, and Ethical Concerns in AI-Driven Marriage and Matchmaking Services

Abstract

Artificial intelligence (AI) is reshaping industries, including the marriage and matchmaking sector, through AI-driven chatbots that facilitate partner selection, compatibility analysis, and user engagement. This study examines customer perceptions, trust dynamics, and ethical concerns associated with AI-powered matchmaking services, particularly in comparison to human matchmakers. Using a mixed-methods approach combining qualitative surveys and statistical analysis, this research investigates trust factors, data privacy apprehensions, and AI’s limitations in processing emotional intelligence. Results indicate that while AI enhances efficiency through data-driven recommendations and pattern recognition, scepticism persists due to concerns about algorithmic bias, lack of human empathy, and ethical transparency. Moreover, AI chatbots struggle with nuanced interpersonal cues, raising questions about their reliability in emotionally sensitive decisions. The study suggests that a hybrid AI-human matchmaking model, integrating machine learning-driven suggestions with human oversight, could improve user trust and adoption. By addressing privacy safeguards, regulatory compliance, and AI explainability, the matchmaking industry can responsibly harness AI’s potential while preserving human intuition. These insights contribute to the broader discourse on AI’s role in human-centric decision-making and its ethical deployment in relationship-based industries.

Introduction

AI-driven matchmaking has revolutionized partner selection by using machine learning, neural networks, and behavioral analytics to predict compatibility with greater efficiency and scalability than traditional human matchmakers. However, challenges around trust, ethical validity, algorithmic bias, data privacy, and AI’s inability to replicate human intuition and emotional intelligence remain significant concerns.

Traditional matchmaking relies on subjective factors like emotional intelligence and cultural values, while AI systems base decisions on large datasets and predictive models. Despite AI’s high predictive accuracy, many users distrust algorithmic recommendations due to fairness issues, lack of transparency, and impersonal nature. Bias in training data can reinforce stereotypes and exclusion, raising ethical questions about fairness and discrimination.

User trust in AI matchmaking is influenced by explainability, perceived authenticity, and emotional engagement, with many favoring human matchmakers who better understand emotional nuances. Data privacy is a major concern given the sensitive personal information analyzed by AI platforms, highlighting the need for strong ethical governance and regulatory compliance.

This study combined a user survey and literature review to assess attitudes toward AI matchmaking. Key findings include:

• Male and non-binary users show higher AI matchmaking adoption compared to females, who prefer human matchmakers.

• Trust in human matchmakers is generally higher than in AI, with 67% trusting humans highly versus 27% for AI.

• Ethical concerns focus on AI bias (30%) and data privacy (23%), alongside skepticism about AI’s lack of intuition.

• Satisfaction ratings favor human matchmakers in success, compatibility, and satisfaction, while AI is rated better for ease of use.

The study concludes that while AI matchmaking offers convenience and predictive power, greater transparency, fairness, ethical safeguards, and hybrid AI-human models are essential to improve trust, user satisfaction, and inclusivity.

Conclusion

This research evaluated user perceptions, trust levels, and ethical concerns surrounding AI-driven matchmaking services in comparison to traditional human matchmakers. Based on the analysis of 30 survey responses, the findings highlight several key insights:

References

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Copyright

Copyright © 2025 Dr. Rashmi Shekhar, Aashish Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.