Recent breakthroughs in artificial intelligence (AI) have brought about increasingly capable systems that demonstrate remarkable abilities in reasoning, language understanding, and problem-solving. These advancements have prompted a renewed examination of AI awareness not as a philosophical question of consciousness, but as a measurable, functional capacity. AI awareness is a double-edged sword: it improves general capabilities, i.e., reasoning, safety, while also raising concerns around misalignment and societal risks, demanding careful oversight as AI capabilities grow. In this review, we explore the emerging landscape of AI awareness, which includes metacognition (the ability to represent and reason about its own cognitive state), self-awareness (recognizing its own identity, knowledge, limitations, inter alia), social awareness (modeling the knowledge, intentions, and behaviors of other agents and social norms), and situational awareness (assessing and responding to the context in which it operates). First, we draw on insights from cognitive science, psychology, and computational theory to trace the theoretical foundations of awareness and examine how the four distinct forms of AI awareness manifest in state-of-the-art AI. Next, we systematically analyze current evaluation methods and empirical findings to better understand these manifestations. Building on this, we explore how AI awareness is closely linked to AI capabilities, demonstrating that more aware AI agents tend to exhibit higher levels of intelligent behaviors. Finally, we discuss the risks associated with AI awareness, including key topics in AI safety, alignment, and broader ethical concerns.

Language technologies have made enormous progress, especially with the introduction of large language models (LLMs). On traditional tasks such as machine translation and sentiment analysis, these models perform at near-human level. These advances can, however, exacerbate a variety of issues that models have traditionally struggled with, such as bias, evaluation, and risks. In this position paper, we argue that many of these issues share a common core: a lack of awareness of the factors, context, and implications of the social environment in which NLP operates, which we call social awareness. While NLP is getting better at solving the formal linguistic aspects, limited progress has been made in adding the social awareness required for language applications to work in all situations for all users. Integrating social awareness into NLP models will make applications more natural, helpful, and safe, and will open up new possibilities. Thus we argue that substantial challenges remain for NLP to develop social awareness and that we are just at the beginning of a new era for the field.

Do large language models (LLMs) exhibit any forms of awareness similar to humans? In this paper, we introduce the concept of awareness to LLMs, arguing that awareness is an essential aspect of trustworthiness for LLMs to enhance their interaction with humans while ensuring ethical responses. We define awareness in LLMs as the ability to perceive and understand themselves as AI models and to exhibit social intelligence. We identify four key dimensions of awareness: capability, mission, emotion, and perspective. To assess LLMs on these dimensions, we introduce a specialized dataset, AwareLLM dataset. Our findings reveal that LLMs demonstrate a decent degree of awareness, though they still lack substantial capability awareness.

The implementation of energy communities represents a cross-disciplinary phenomenon that has the potential to support the energy transition while fostering citizens' participation throughout the energy system and their exploitation of renewables. An important role is played by online information sources in engaging people in this process and increasing their awareness of associated benefits. In this view, this work analyses online news data on energy communities to understand people's awareness and the media importance of this topic. We use the Semantic Brand Score (SBS) indicator as an innovative measure of semantic importance, combining social network analysis and text mining methods. Results show different importance trends for energy communities and other energy and society-related topics, also allowing the identification of their connections. Our approach gives evidence to information gaps and possible actions that could be taken to promote a low-carbon energy transition.

In years past, the emotional intelligence of employees was low on the list of qualifications, especially for leaders. However, with the way that the market has changed, how someone connects with others on an emotional level is becoming much more important. Company leaders need to understand the emotional connection audiences have with services, products and companies as a whole. The most recent Fjord Trends 2020 even talks about "liquid people" and why it is important to set up human insights teams instead of consumer insights. All this leads to the conclusion that now, more then ever before, if you want to successfully lead your company or grow your startup, you will need to understand what emotional connections are and how they can propel you forward.

But for all their fancy sensors and intricate data-crunching abilities, even the most cutting-edge cars lack something that (almost) every 16-year-old with a learner's permit has: social awareness. While autonomous technologies have improved substantially, they still ultimately view the drivers around them as obstacles made up of ones and zeros, rather than human beings with specific intentions, motivations, and personalities. But recently a team led by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has been exploring whether self-driving cars can be programmed to classify the social personalities of other drivers, so that they can better predict what different cars will do -- and, therefore, be able to drive more safely among them. In a new paper, the scientists integrated tools from social psychology to classify driving behavior with respect to how selfish or selfless a particular driver is. Specifically, they used something called social value orientation (SVO), which represents the degree to which someone is selfish ("egoistic") versus altruistic or cooperative ("prosocial").

But for all their fancy sensors and intricate data-crunching abilities, even the most cutting-edge cars lack something that (almost) every 16-year-old with a learner's permit has: social awareness. While autonomous technologies have improved substantially, they still ultimately view the drivers around them as obstacles made up of ones and zeros, rather than human beings with specific intentions, motivations, and personalities. But recently a team led by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) has been exploring whether self-driving cars can be programmed to classify the social personalities of other drivers, so that they can better predict what different cars will do -- and, therefore, be able to drive more safely among them. In a new paper, the scientists integrated tools from social psychology to classify driving behavior with respect to how selfish or selfless a particular driver is. Specifically, they used something called social value orientation (SVO), which represents the degree to which someone is selfish ("egoistic") versus altruistic or cooperative ("prosocial").

AI and AAAI are coevolving. As AI matures, its focus is shifting from inward-looking to outwardlooking. Some of the new concerns of the field are social awareness, networking, cross-disciplinarity, globalization, and open access. AAAI must reflect and support those concerns. AI is now a mature discipline.