Shaping the Future with AI: Research, Education, and Community Impact.

Responsible AI · Ongoing

Bridging Minds and Machines: Human Perspectives and Responsible AI for an Inclusive Future

This project investigates diverse human viewpoints on AI to ensure its development aligns with societal needs and values.

Ongoing
Aug 21, 2024 - Ongoing

Abstract

This project investigates diverse human viewpoints on AI to ensure its development aligns with societal needs and values.

Description

Today, technology—particularly artificial intelligence (AI)—has profoundly transformed every aspect of our lives, shaping not only how we utilize technology but also how we make decisions. Among the most groundbreaking AI advancements are large language models (LLMs), which have revolutionized the processing, understanding, and generation of human-like text. These models have seamlessly integrated into various systems, including social media, significantly influencing our social sphere. The impact of AI spans across domains such as healthcare, education, and manufacturing, reshaping industries and redefining the way we interact with the world.

However, despite the remarkable progress AI has achieved, there exist limitations, particularly issues of bias. Numerous cases have highlighted how biased AI systems have unfairly impacted lives, such as in the justice system and banking sector, where decisions influenced by these systems have led to unjust outcomes. This emphasizes the urgent need to develop technology that is inclusive, fair, and ethical AI—commonly referred to as responsible AI. Achieving this goal requires a deep understanding of both the technological foundations and their broader societal implications.

In this project, we aim to explore the implications of AI from both technological and societal perspectives, focusing on how AI can be refined to reduce bias and promote fairness, thereby mitigating instances of unfairness that may arise as a result of the use of the biased AI systems.

Persons Involved:

  1. Tek Raj Chhetri
  2. Abhash Shrestha
  3.  Asish Pandey

Research Outputs

Publications (3)

Whose fairness? Structural concentration in AI bias research
Preprint Published 2026
Dual-Metric Evaluation of Social Bias in Large Language Models: Evidence from an Underrepresented Nepali Cultural Context
Preprint Published 2026
Integrating Knowledge Graphs and Large Language Models for Bias Mitigation
Workshop Paper In Review 2026

Datasets (2)

AI Bias Research Landscape

This dataset contains 692 curated bibliographic records of peer-reviewed and preprint publications on artificial intelligence (AI) and algorithmic bias, published between 2012 and 2026. Each record includes publication metadata (paper title, DOI, authors, author regions, affiliations, publication year, and research domain), author ORCID identifiers, and OpenAlex-derived metadata, including OpenAlex IDs, citation counts, referenced works, open-access status, and open-access URLs. Six records lack a DOI and therefore do not contain OpenAlex-sourced identifiers, citation counts, or open-access data; abstracts for these six records were curated manually rather than retrieved from OpenAlex. The dataset is provided in CSV format. This dataset is the underlying corpus for an interactive atlas of AI bias research, available at https://biasatlas.cair-nepal.org, and for the accompanying paper "Whose fairness? Structural concentration in AI bias research"

Public Abhash Shrestha and … Bias, Fairness
EquiText-Nepali: A Dataset for Gender, Race, and Sociocultural Bias in Nepali Text

EquiText-Nepali is a curated dataset designed to evaluate and expose social biases, specifically related to gender, race, and sociocultural or religious identity within Nepali-language texts. It aims to support the development of fair and inclusive language technologies by offering annotated examples for bias detection, masked language model probing, and fairness benchmarking.

Public Ashish Pandey and … Bias, Fairness, LLM

Software & Tools (2)

SABRE-KG: Semantic Anti-Bias Retrieval Engine

This project presents an experimental pipeline for detecting and mitigating gender bias in Large Language Models (LLMs) using prompt-based evaluation and bias intervention techniques.

Public Python MIT
Bias Atlas

The Bias Atlas offers interactive visualizations of bias research across regions, institutions, and also provies co-author networks analysis. It also presents insights into author distributions and cross-country collaborations, providing a broader view of how bias research is shaped globally. In addition, the dashboard highlights the progression of domain-specific studies, allowing users to track developments and emerging trends in bias research.

Public NextJS Apache License 2.0
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