Limitations One limitation of our model is its potential for data bias. This could limit the applications of the model. MLLMs could be used to create fake news articles or social media posts. Hyperparameters Number of layers 24 Hidden size 2,048 FFN inner hidden size 8,192 Attention heads 32 Dropout 0.1 Attention dropout 0.1 Activation function GeLU [1] V ocabulary size 64,007 Soft tokens V size 64 Max length 2,048 Relative position embedding xPos [2] Initialization Magneto [3] Table 1: Hyperparameters of causal language model of K The detailed instruction tuning hyperparameters are listed in Table 3. The models are trained on web-scale multimodal corpora.

The image pairs are mined from existing datasets.

The Association for the Advancement of Artificial Intelligence's 2025 Fall Symposium Series was held November 6-8, 2025, at the Westin Arlington Gateway in Arlington, Virginia. There were six symposia in the program: AI for Social Good: Emerging Methods, Measures, Data, and Ethics; AI Trustworthiness and Risk Assessment for Challenged Contexts; Engineering Safety-Critical AI Systems; First AAAI Symposium on Quantum Information and Machine Learning: Bridging Quantum Computing and Artificial Intelligence; Safe, Ethical, Certified, Uncertainty-aware, Robust, and Explainable AI for Health; and Unifying Representations for Robot Application Development. This report contains summaries of the symposia, which were submitted by most, but not all, of the symposium organizers. AI has demonstrated transformative potential across sectors such as aging, combating information manipulation, disaster response, education, environmental sustainability, government, healthcare, social care, transportation, and urban planning. Yet, the systematic development of AI For Social Good remains fragmented across those many research communities, with limited convergence around effective methodologies, equitable impact measurement, or access to important data and long-term engagement with targeted populations. The main objective for this symposium was to convene across disciplines and engage researchers, practitioners, and policymakers, with a particular focus on finding methods, measures and data that could be used in multiple settings. There were roughly 30 participants.

Item-side group fairness (IGF) requires a recommendation model to treat different item groups similarly, and has a crucial impact on information diffusion, consumption activity, and market equilibrium. Previous IGF notions only focus on the direct utility of the item exposures, i.e., the exposure numbers across different item groups. Nevertheless, the item exposures also facilitate utility gained from the neighboring users via social influence, called social utility, such as information sharing on the social media. To fill this gap, this paper introduces two social attribute-aware IGF metrics, which require similar user social attributes on the exposed items across the different item groups. In light of the trade-off between the direct utility and social utility, we formulate a new multi-objective optimization problem for training recommender models with flexible trade-off while ensuring controllable accuracy. To solve this problem, we develop a gradient-based optimization algorithm and theoretically show that the proposed algorithm can find Pareto optimal solutions with varying trade-off and guaranteed accuracy.

Multi-agent pathfinding (MAPF) is the problem of finding collision-free paths for a team of agents on a map. Although MAPF is NP-hard, the hardness of solving individual instances varies significantly, revealing a gap between theoretical complexity and actual hardness. This paper outlines three key research challenges in MAPF empirical hardness to understand such phenomena. The first challenge, known as algorithm selection, is determining the best-performing algorithms for a given instance. The second challenge is understanding the key instance features that affect MAPF empirical hardness, such as structural properties like phase transition and backbone/backdoor. The third challenge is how to leverage our knowledge of MAPF empirical hardness to effectively generate hard MAPF instances or diverse benchmark datasets. This work establishes a foundation for future empirical hardness research and encourages deeper investigation into these promising and underexplored areas.

Sentiment analysis is a helpful task to automatically analyse opinions and emotions on various topics in areas such as AI for Social Good, AI in Education or marketing. While many of the sentiment analysis systems are developed for English, many African languages are classified as low-resource languages due to the lack of digital language resources like text labelled with corresponding sentiment classes. One reason for that is that manually labelling text data is time-consuming and expensive. Consequently, automatic and rapid processes are needed to reduce the manual effort as much as possible making the labelling process as efficient as possible. In this paper, we present and analyze an automatic language-independent sentiment labelling method that leverages information from sentiment-bearing emojis and words. Our experiments are conducted with tweets in the languages English, Sepedi and Setswana from SAfriSenti, a multilingual sentiment corpus for South African languages. We show that our sentiment labelling approach is able to label the English tweets with an accuracy of 66%, the Sepedi tweets with 69%, and the Setswana tweets with 63%, so that on average only 34% of the automatically generated labels remain to be corrected.

Data Flows and Colonial Regimes in Africa: A Critical Analysis of the Colonial Futurities Embedded in AI Recommendation Algorithms Angella Ndaka, University of Witwatersrand, Johannesburg, South Africa Fátima Ávila - Acosta, Berlin Graduate School of Social Sciences at Humboldt University, Berlin, Germany Harnred Mbula, Centre for Epistemic Justice, Nairobi, Kenya Christine Amera, Centre for Epistemic Justice, Nairobi Kenya Sandra Tiyani Chauke University of Pretoria, South Africa Eucabeth Majiwa Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya Abstract In the last few years, Africa has experienced growth in a thriving ecosystem of Artificial Intelligence (AI) technologies and systems, developed and promoted by both local and global technology players. While the sociotechnical imaginaries about these syst ems promote AI as critical to achiev ing Africa's sustainable development agenda, some of them have subtly permeated society, recreating new values, cultures, practices, and histories that threaten to marginalize minority groups in the region. Africa predominantly frames AI as an imaginary solution to address complex social challenges; however, the narrative subtly ignores deeper power - related concerns, including data governance, embedded algorithmic colonialism, and the exploitation that propag ates new digital colonial sites. However, the development of current AI ethics in Africa is in its infancy and predominantly framed through lenses of Western perspective, with the social and ethical impacts of the AI innovations and application on African epistemologies and worldviews not prioritized. To ensure that people on the African continent leverage the benefits of AI, these social and ethical impacts o f AI need to be critically and explicitly considered and addressed. This chapter will therefore seek to frame the elemental and invisible problems of AI and big data in the African context by examining digital sites and infrastructure through the lens of power and interests. It will present reflections on how these sites are using AI recommendation algorithms to recreate new digital societies in the region, how they have the potential to propagate algorithmic colonialism and negative gender norms, and what this means for the regional sustainable development agenda. The chapter proposes adopting business models that embrace response - ability and consider the existence of alternative socio - material worlds of AI. These reflections will mainly come from ongoing discussions with Kenyan social media users in this author's user space talks, which take place every month. Keywords: Artificial Intelligence; algorithmic colonialism; Data; response - ability; digital sites Section 1: Introduction The growing global interest, combined with rising investments in AI skilling and infrastructure development, is a key driver of the expanding landscape of AI technologies and systems across Africa.

Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.

Throughout this paper, we assume that the entries of W are subgaussian.