The role of artificial intelligence (AI) in modern mediation

The rapid advancement of Artificial Intelligence (AI) is transforming contemporary mediation by introducing technology as an active “Fourth Party” alongside disputing parties and the human mediator. This study critically examines the role of AI in modern mediation, with specific reference to Sri Lanka's community-based dispute resolution framework. Moving beyond traditional Online Dispute Resolution (ODR), AI-supported Dispute Resolution (AIDR) integrates tools such as predictive analytics, sentiment analysis, and natural language processing to enhance efficiency, objectivity, and accessibility. Using a qualitative research design, the study draws on semi-structured interviews with Community Mediation Board chairpersons, volunteer mediators, Mediator Training Officers from the Ministry of Justice, and legal technology researchers, supplemented by an analysis of relevant academic literature and policy documents. The findings reveal that AI can support mediation through administrative automation, intelligent case summarization, real-time translation, emotional diagnostics, and data-driven reality testing. However, significant ethical concerns emerge, including algorithmic bias, transparency deficits, confidentiality risks, and the inability of AI to address emotional reconciliation. The study argues for a hybrid, human-in-the-loop model in which AI augments rather than replaces the mediator, ensuring both efficiency and ethical integrity in dispute resolution.

Authors

Institutions

Publication Details

Published
2025-12-30
DOI
https://doi.org/10.82471/ywf8s-gzj04
Primary Topic
Dispute Resolution and Class Actions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The role of artificial intelligence (AI) in modern mediation

G. K.Y. S Siriwardana
Dispute Resolution and Class Actions
article

The role of artificial intelligence (AI) in modern mediation

G. K.Y. S Siriwardana
article en

Abstract

The rapid advancement of Artificial Intelligence (AI) is transforming contemporary mediation by introducing technology as an active “Fourth Party” alongside disputing parties and the human mediator. This study critically examines the role of AI in modern mediation, with specific reference to Sri Lanka's community-based dispute resolution framework. Moving beyond traditional Online Dispute Resolution (ODR), AI-supported Dispute Resolution (AIDR) integrates tools such as predictive analytics, sentiment analysis, and natural language processing to enhance efficiency, objectivity, and accessibility. Using a qualitative research design, the study draws on semi-structured interviews with Community Mediation Board chairpersons, volunteer mediators, Mediator Training Officers from the Ministry of Justice, and legal technology researchers, supplemented by an analysis of relevant academic literature and policy documents. The findings reveal that AI can support mediation through administrative automation, intelligent case summarization, real-time translation, emotional diagnostics, and data-driven reality testing. However, significant ethical concerns emerge, including algorithmic bias, transparency deficits, confidentiality risks, and the inability of AI to address emotional reconciliation. The study argues for a hybrid, human-in-the-loop model in which AI augments rather than replaces the mediator, ensuring both efficiency and ethical integrity in dispute resolution.

University of Kelaniya (LK)
Peace, Justice and strong institutions
Openalex Percentile: Top 15%
Dispute Resolution and Class Actions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.