Singapore

AAAI 2026

Data as a Lever: A Neighbouring Datasets Perspective on Predictive Multiplicity

data imputation

rashomon sets

multiplicity

active learning

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.

workshop paper

Adapting Multiverse Analysis for Prediction: A Decision-Maker’s Dashboard

On Arbitrary Predictions From Equally Valid Models

Intervention Efficiency and Perturbation Validation Framework: Capacity-Aware and Robust Clinical Model Selection under the Rashomon Effect

Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set

Evaluating the Ability of Explanations to Disambiguate Models in a Rashomon Set

The rapid proliferation of large language models (LLMs) in applications targeting children and adolescents necessitates a fundamental reassessment of prevailing AI safety frameworks, which are largely tailored to adult users and neglect the distinct developmental vulnerabilities of minors. This paper highlights key deficiencies in existing LLM safety benchmarks, including their inadequate coverage of age-specific cognitive, emotional, and social risks spanning early childhood (ages 0–6), middle childhood (7–12), and adolescence (13–18). To bridge these gaps, we introduce SproutBench, an innovative evaluation suite comprising 1,283 developmentally grounded adversarial prompts designed to probe risks such as emotional dependency, privacy violations, and imitation of hazardous behaviors. Through rigorous empirical evaluation of 47 diverse LLMs, we uncover substantial safety vulnerabilities, corroborated by robust inter-dimensional correlations (e.g., between Safety and Risk Prevention, p = 0.86) and a notable inverse relationship between Interactivity and Age Appropriateness (p = -0.48). These insights yield practical guidelines for advancing child-centric AI design and deployment.

SproutBench: A Benchmark for Safe and Ethical Large Language Models for Youth

Interpretability and robustness remain major challenges for modern Large Language Models (LLMs), especially in settings where conventional evaluation or auditing tools are limited. To address this, we propose Inverse Language Modeling (ILM), a unified training framework that jointly enhances robustness to adversarial perturbations and enables a novel form of gradient-based interpretability. Rather than reconstructing exact input prompts, ILM encourages LLMs to develop gradient-aligned internal representations that allow the model to approximate plausible input patterns underlying a given output. This approximate inversion provides a new mechanism for analyzing model behavior, identifying potential triggers for unsafe generations, and supporting lightweight governance and red-teaming workflows. Our results show that ILM can simultaneously improve robustness and produce meaningful inversion signals, laying a foundation for LLMs that are not only more resilient but also more transparent and analyzable.

Inverse Language Modeling towards Robust and Grounded LLMs

Recent multilingual language models promise support for “100+ languages,” yet speakers of Indigenous and other underrepresented languages still often do not see themselves in these advances. In this work, we take a deliberately simple, secondary-benchmark perspective: rather than proposing a new model or dataset, we re-evaluate an off-the-shelf multilingual natural language inference (NLI) model on public benchmarks that explicitly include Indigenous languages of the Americas. Concretely, we use the AmericasNLI benchmark for ten Indigenous languages and XNLI for English and Spanish, and we evaluate the widely used joeddav/xlm-roberta-large-xnli model under a fixed, zero-shot protocol. Our goal is to answer three questions: (i) How large is the performance gap between high- resource and underrepresented languages under the same model and task? (ii) Are these gaps consistent across languages, or do some communities fare systematically worse than others? (iii) What kinds of qualitative errors arise, and what do they suggest about cultural and linguistic mismatch? Our experiments reveal a striking discrepancy: while English and Spanish reach almost perfect accuracy on XNLI (around 99.8% on our runs), the same model averages only about 43% accuracy across ten Indigenous languages in AmericasNLI, with none exceeding 47%. We also show qualitative NLI failures in Quechua that point to difficulties with morphology, idioms, and discourse-level inference. We argue that even such a simple re-analysis can serve as a low-cost yet high-impact tool for making inequities in multilingual NLP visible, especially for communities that rarely appear in headline benchmarks.

Advancing NLP Equity: A Secondary Benchmark Evaluation of Multilingual Language Models for Underrepresented Languages

Guardian models monitor and regulate the outputs of user-facing AI systems. However, current guardian models fall short in two key ways. First, they are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerable to evolving harms, cross-lingual safety failures, and cultural misalignment. Second, most guardian models rely on rigid, predefined safety categories that do not generalize across diverse linguistic and sociocultural contexts. Ensuring robust safety requires flexible, runtime-enforceable policies and benchmarks that reflect local norms, harm scenarios, and cultural expectations. We introduce UbuntuGuard, the first African policy-based safety benchmark built from adversarial queries authored by 155 domain experts across sensitive fields, including healthcare, education, government, and finance. From these expert-crafted queries, we derive context-specific safety policies and reference responses that capture culturally grounded risk signals, enabling policy-aligned evaluation of guardian models. We evaluate six state-of-the-art guardian models, including static, dynamic, and multilingual variants, under multiple scenarios. Our findings reveal that existing English-centric benchmarks overestimate real-world multilingual safety, cross-lingual transfer provides partial but insufficient coverage, and dynamic models, while better equipped to leverage policies at inference time, still struggle in fully localized African-language contexts. These findings highlight the urgent need for multilingual, culturally grounded safety benchmarks to enable the development of reliable and equitable guardian models for low-resource languages.


UbuntuGuard: A Policy-Based Safety Benchmark for Low-Resource African Languages

Static leaderboards and single turn judgments correlate weakly with deployment outcomes, especially in multilingual and resource constrained settings. This position paper argues that credible evaluation hinges on verifiability: ex ante specifications that permit observable checks, repeatable scoring, and auditable evidence. We propose a minimal standard that makes verifiability first class while remaining compatible with existing workflows. The standard has four artifacts: a task schema, a validator entry point, a run card, and required reporting fields. We ground the proposal in prior work on coverage and transparency and on specification based checks. We present a prototype evaluation task for schema constrained instruction following with robustness probes and a multilingual protocol, and we attach measurement and governance procedures that link scores to validity arguments. The goal is to replace generic win rates with verifiable claims about task success that better predict real use across languages and contexts.

Next from AAAI 2026

Adapting Multiverse Analysis for Prediction: A Decision-Maker’s Dashboard

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Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set