AI is no longer just a translator or image recognizer. Today, we engage with systems that remember our preferences, proactively manage our calendars, and even provide emotional support. They build ongoing bonds with users. They change their behavior based on our habits. They don't just wait for commands; they suggest next steps.

Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to human users. Such AI companionship -- i.e., bonded relationships between humans and AI systems that resemble the relationships people have with family members, friends, and romantic partners -- might substantially benefit humans. Yet such relationships can also do profound harm. We propose a framework for analyzing potential negative impacts of AI companionship by identifying specific harmful traits of AI companions and speculatively mapping causal pathways back from these traits to possible causes and forward to potential harmful effects. We provide detailed, structured analysis of four potentially harmful traits -- the absence of natural endpoints for relationships, vulnerability to product sunsetting, high attachment anxiety, and propensity to engender protectiveness -- and briefly discuss fourteen others. For each trait, we propose hypotheses connecting causes -- such as misaligned optimization objectives and the digital nature of AI companions -- to fundamental harms -- including reduced autonomy, diminished quality of human relationships, and deception. Each hypothesized causal connection identifies a target for potential empirical evaluation. Our analysis examines harms at three levels: to human partners directly, to their relationships with other humans, and to society broadly. We examine how existing law struggles to address these emerging harms, discuss potential benefits of AI companions, and conclude with design recommendations for mitigating risks. This analysis offers immediate suggestions for reducing risks while laying a foundation for deeper investigation of this critical but understudied topic.

As AI systems evolve from static tools to dynamic agents, traditional categorical governance frameworks -- based on fixed risk tiers, levels of autonomy, or human oversight models -- are increasingly insufficient on their own. Systems built on foundation models, self-supervised learning, and multi-agent architectures increasingly blur the boundaries that categories were designed to police. In this Perspective, we make the case for dimensional governance: a framework that tracks how decision authority, process autonomy, and accountability (the 3As) distribute dynamically across human-AI relationships. A critical advantage of this approach is its ability to explicitly monitor system movement toward and across key governance thresholds, enabling preemptive adjustments before risks materialize. This dimensional approach provides the necessary foundation for more adaptive categorization, enabling thresholds and classifications that can evolve with emerging capabilities. While categories remain essential for decision-making, building them upon dimensional foundations allows for context-specific adaptability and stakeholder-responsive governance that static approaches cannot achieve. We outline key dimensions, critical trust thresholds, and practical examples illustrating where rigid categorical frameworks fail -- and where a dimensional mindset could offer a more resilient and future-proof path forward for both governance and innovation at the frontier of artificial intelligence.

How we should design and interact with social artificial intelligence depends on the socio-relational role the AI is meant to emulate or occupy. In human society, relationships such as teacher-student, parent-child, neighbors, siblings, or employer-employee are governed by specific norms that prescribe or proscribe cooperative functions including hierarchy, care, transaction, and mating. These norms shape our judgments of what is appropriate for each partner. For example, workplace norms may allow a boss to give orders to an employee, but not vice versa, reflecting hierarchical and transactional expectations. As AI agents and chatbots powered by large language models are increasingly designed to serve roles analogous to human positions - such as assistant, mental health provider, tutor, or romantic partner - it is imperative to examine whether and how human relational norms should extend to human-AI interactions. Our analysis explores how differences between AI systems and humans, such as the absence of conscious experience and immunity to fatigue, may affect an AI's capacity to fulfill relationship-specific functions and adhere to corresponding norms. This analysis, which is a collaborative effort by philosophers, psychologists, relationship scientists, ethicists, legal experts, and AI researchers, carries important implications for AI systems design, user behavior, and regulation. While we accept that AI systems can offer significant benefits such as increased availability and consistency in certain socio-relational roles, they also risk fostering unhealthy dependencies or unrealistic expectations that could spill over into human-human relationships. We propose that understanding and thoughtfully shaping (or implementing) suitable human-AI relational norms will be crucial for ensuring that human-AI interactions are ethical, trustworthy, and favorable to human well-being.

Humans strive to design safe AI systems that align with our goals and remain under our control. However, as AI capabilities advance, we face a new challenge: the emergence of deeper, more persistent relationships between humans and AI systems. We explore how increasingly capable AI agents may generate the perception of deeper relationships with users, especially as AI becomes more personalised and agentic. This shift, from transactional interaction to ongoing sustained social engagement with AI, necessitates a new focus on socioaffective alignment--how an AI system behaves within the social and psychological ecosystem co-created with its user, where preferences and perceptions evolve through mutual influence. Addressing these dynamics involves resolving key intrapersonal dilemmas, including balancing immediate versus long-term well-being, protecting autonomy, and managing AI companionship alongside the desire to preserve human social bonds. By framing these challenges through a notion of basic psychological needs, we seek AI systems that support, rather than exploit, our fundamental nature as social and emotional beings.

As artificial intelligence (AI) continues to evolve, the current paradigm of treating AI as a passive tool no longer suffices. As a human-AI team, we together advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans. Drawing from interdisciplinary concepts such as ecorithms, order from chaos, and cooperation, we explore how AI can evolve and adapt in unpredictable environments. Arising from these brief explorations, we present two key recommendations: (1) foster ethical, cooperative treatment of AI to benefit both humans and AI, and (2) leverage the inherent heterogeneity between human and AI minds to create a synergistic hybrid intelligence. By reframing AI as a dynamic partner, a model emerges in which AI systems develop alongside humans, learning from human interactions and feedback loops including reflections on team conversations. Drawing from a transpersonal and interdependent approach to consciousness, we suggest that a "third mind" emerges through collaborative human-AI relationships. Through design interventions such as interactive learning and conversational debriefing and foundational interventions allowing AI to model multiple types of minds, we hope to provide a path toward more adaptive, ethical, and emotionally healthy human-AI relationships. We believe this dynamic relational learning-partner (DRLP) model for human-AI teaming, if enacted carefully, will improve our capacity to address powerful solutions to seemingly intractable problems.

In an era of AI's growing capabilities and influences, recent advancements are reshaping HCI and CSCW's view of AI as mere tools. Playful interactions with AI systems naturally emerged as a way for users to make sense of the ever-changing technology. However, these emergent and playful interactions are underexamined. We target this gap by investigating playful interactions exhibited by users of a recently trending powerful AI technology, ChatGPT. Through a thematic analysis of 372 user-generated posts on the ChatGPT subreddit, we found that a substantial portion of user discourse revolves around playful interactions. The analysis further allowed us to construct a preliminary taxonomy to describe these interactions, categorizing them into six types: reflecting, jesting, imitating, challenging, tricking, and contriving; each included sub-categories. Overall, this study contributes to the field of HCI and CSCW by illuminating the multifaceted nature of playful interactions with AI, underlining their significance in shaping the human-AI relationship.

The explanation dimension of Artificial Intelligence (AI) based system has been a hot topic for the past years. Different communities have raised concerns about the increasing presence of AI in people's everyday tasks and how it can affect people's lives. There is a lot of research addressing the interpretability and transparency concepts of explainable AI (XAI), which are usually related to algorithms and Machine Learning (ML) models. But in decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system. Decision-makers usually need to justify their decision to others in different domains. If that decision is somehow based on or influenced by an AI-system outcome, the explanation about how the AI reached that result is key to building trust between AI and humans in decision-making scenarios. In this position paper, we discuss the role of XAI in decision-making scenarios, our vision of Decision-Making with AI-system in the loop, and explore one case from the literature about how XAI can impact people justifying their decisions, considering the importance of building the human-AI relationship for those scenarios.

Microsoft is positioning itself as the world's platform for artificial intelligence, and that's a smart move. In 2014 I wrote that Microsoft's Cortana would be the next big thing. Redmond's vision for its johnny-come-lately AI is that it, like the GUI before it, will be pivotal in the evolution of the personal computing user interface. Microsoft's ambitions for Cortana were evident in 2014. Microsoft envisions an unbounded AI that developers and partners will incorporate into a range of everyday and innovative devices enabled by the Cortana SDK.

The Tesla Model S Last week it emerged that a self-driving Tesla car had been involved in a fatal crash in Williston, Florida on May 7. The car was in Autopilot mode when its on-board system failed to recognize a left-turning truck intercepting its path, resulting in the car proceeding underneath the trailer and losing its roof. There are already numerous theories about what happened, the most likely being that the car's cameras could not decipher the pale trailer against the bright Florida sky. Another possibility is that the system registered the trailer as an overhead road sign – Tesla cars are programmed to ignore overhead signs to avoid excessive and unnecessary braking – which could have happened due to the onward view beneath the trailer being clear to cameras. Nonetheless, the crash resulted in the death of 40-year-old Joshua Brown from Ohio and official investigations have begun. Since the incident, Tesla has released a blog post expressing their condolences and outlining the safety procedures all Tesla cars are equipped with.