Singapore

There is a growing debate about the implications of multiplicity—conflicting behavior among a set of “good”’ models—for algorithmic decision-making. On one hand, there are concerns over unfair treatment due to conflicting predictions and explanations, further exacerbated in the generative AI ecosystem. Yet, on the other hand, multiplicity also offers the potential to find less discriminatory and more interpretable models. In this tutorial, we aim to increase awareness of different perspectives on multiplicity and position them among broader discussions in the community. Specifically, the main goals of our tutorial are:

* Highlighting the phenomenon of multiplicity in machine learning and calling attention to its growing literature.
* Linking multiplicity with uncertainty and churn, presenting a unified view of various measures of instability.
* Discussing the implications of multiplicity for fairness and explainability in algorithmic decision-making.
* Recognizing exacerbated concerns and new forms of multiplicity in the age of generative AI.
* Engaging the community on when and how to address multiplicity in various practical scenarios.
* Identifying open questions and motivating future research directions on multiplicity.

AAAI 2026

The Many Faces of Multiplicity in Machine Learning

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

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.

tutorial

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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

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We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

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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.

BSLM: A Bi-Level Speech-Language Model for the Joint Modeling of Discrete and Continuous Tokens

Granular Control of Nonverbal Expressions for Achieving Natural Emotional Text-to-Speech

AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio Embedding Sequences

Can You Hear Naples? Building and Benchmarking a Neapolitan Speech Corpus

AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval

Generalizable speech deepfake detection via meta-learned LoRA

Toward High-Quality Cross-lingual Text-to-Speech Synthesis In Low-Resource Scenarios

Semi-supervised Acoustic Scene Classification under Spatial-Temporal Variability with a CRNN-based Model

Online Independent Low-Rank Matrix Analysis as a Lightweight and Trainable Model for Real-Time Multichannel Music Source Separation

As notoriously opaque deep neural networks (DNNs) become commonplace in powerful Artificial Intelligence (AI) systems, Interpretable Deep Learning (IDL) has emerged as a promising direction for designing interpretable-by-construction neural architectures. At their core, IDL models learn a latent space where some of their representations are aligned with high-level units of information, or concepts, that domain experts are familiar with (e.g., “stripped texture”,  “round object”, etc.). By introducing inductive biases that encourage predictions to be made based on these interpretable representations, IDL models enable the construction of expressive yet highly transparent architectures that can be vetted, analysed, and intervened on.

This tutorial aims to capitalise on the surge of interest in IDL by exposing AI researchers and engineers to the core foundations necessary to understand the general principles behind existing IDL models. By doing so, we aim to equip attendees with the knowledge necessary to comprehend the current state of this extensive body of literature, enabling them to build upon it for their research. Specifically, this tutorial will provide an overview of (1) core interpretability principles, (2) seminal works in the field, and (3) recent directions in IDL. Our tutorial will include hands-on demonstrations throughout our session and will conclude with a discussion of the key open questions in the field.

Our material will assume a basic knowledge of ML (e.g., foundations of supervised learning, experimental design, basic probabilistic modelling, etc.), with particular emphasis on a solid Deep Learning foundation (e.g., tensor calculus, neural networks, backpropagation, etc.). Concepts that may require mathematical tools/expertise beyond those one would expect to be shared among the AAAI community will be (re)introduced in our tutorial.

For further details and material, please refer to our tutorial’s site found at https://interpretabledeeplearning.github.io/.

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