Singapore

Charaka Vinayak Kumar, Ashok Urlana, Gopichand Kanumolu, Bala Mallikarjunarao Garlapati, Pruthwik Mishra

AAAI 2026

Behind the Words: A Comprehensive Study of Bias Detection Methods in LLMs

and fairness

ethics

bias

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Red-Teaming Financial AI Agents: Stress-Testing Governance Protections in LLMs Against Market Manipulation and Regulatory Evasion

A Fair-Baseline Protocol for Evaluating Multi-Agent Governance in Continuous AI Ethics Compliance

Vincent Chang, Thee Ho, Sunishchal Dev, Kevin Zhu, Shi Feng, Kellin Pelrine, Matthew Kowal

Emergent Persuasion: Will LLMs Persuade Without Being Prompted?

Frédéric Berdoz, Luca A Lanzendörfer, Kaan Bayraktar, Roger Wattenhofer

Data Attribution in Large Language Models via Bidirectional Gradient Optimization

AI safety and alignment evaluations (benchmarks) have become de facto governance tools in the rapidly evolving landscape of artificial intelligence. However, these critical instruments themselves lack systematic governance oversight, raising the central question: "Who evaluates the evaluators?" Through a systematic mapping of 298 papers and in-depth thematic analysis of 50 core studies, this paper identifies fundamental governance challenges in current AI evaluation practices. The bibliometric analysis reveals explosive growth in this field since 2023, with persistent gaps in transparency, accountability, and institutional independence. This position paper synthesizes current knowledge on evaluation governance and proposes a future agenda grounded in three core principles: participatory design with adversarial scrutiny, radical transparency through "Evaluation Cards," and institutional pluralism to ensure independent oversight. The paper argues for a paradigm shift from "evaluating AI" to "governing evaluations," offering actionable recommendations for researchers, policymakers, and practitioners to build a trustworthy evaluation ecosystem.

Who Evaluates the Evaluators? Governance Challenges in AI Safety and Alignment Evaluations: A Review and Future Agenda

Offensive Yet Efficient: Semantic Summarization via Obscene Lexicon

Stop Harm Before It Starts: iCRAFT for Agentic LLM Governance

Gaurav Chawla, Caleb DeLeeuw, Vanessa Dietze, Aniket Sharma

The Secret Agenda: LLMs Strategically Lie and Our Current Safety Tools Are Blind

Panatchakorn Anantaprayoon, Nataliia Babina, Jad Tarifi, Nima Asgharbeygi

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Red-Teaming Financial AI Agents: Stress-Testing Governance Protections in LLMs Against Market Manipulation and Regulatory Evasion

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Ethics and Bias