This paper introduces the Creative Intelligence Loop (CIL), a novel socio-technical framework for responsible human-AI co-creation. Rooted in the 'Workflow as Medium' paradigm, the CIL proposes a disciplined structure for dynamic human-AI collaboration, guiding the strategic integration of diverse AI teammates who function as collaborators while the human remains the final arbiter for ethical alignment and creative integrity. The CIL was empirically demonstrated through the practice-led creation of two graphic novellas, investigating how AI could serve as an effective creative colleague within a subjective medium lacking objective metrics. The process required navigating multifaceted challenges including AI's 'jagged frontier' of capabilities, sycophancy, and attention-scarce feedback environments. This prompted iterative refinement of teaming practices, yielding emergent strategies: a multi-faceted critique system integrating adversarial AI roles to counter sycophancy, and prioritizing 'feedback-ready' concrete artifacts to elicit essential human critique. The resulting graphic novellas analyze distinct socio-technical governance failures: 'The Steward' examines benevolent AI paternalism in smart cities, illustrating how algorithmic hubris can erode freedom; 'Fork the Vote' probes democratic legitimacy by comparing centralized AI opacity with emergent collusion in federated networks. This work contributes a self-improving framework for responsible human-AI co-creation and two graphic novellas designed to foster AI literacy and dialogue through accessible narrative analysis of AI's societal implications.

Despite the growing interest in collaborative AI, designing systems that seamlessly integrate human input remains a major challenge. In this study, we developed a task to systematically examine human preferences for collaborative agents. We created and evaluated five collaborative AI agents with strategies that differ in the manner and degree they adapt to human actions. Participants interacted with a subset of these agents, evaluated their perceived traits, and selected their preferred agent. We used a Bayesian model to understand how agents' strategies influence the Human-AI team performance, AI's perceived traits, and the factors shaping human-preferences in pairwise agent comparisons. Our results show that agents who are more considerate of human actions are preferred over purely performance-maximizing agents. Moreover, we show that such human-centric design can improve the likability of AI collaborators without reducing performance. We find evidence for inequality-aversion effects being a driver of human choices, suggesting that people prefer collaborative agents which allow them to meaningfully contribute to the team. Taken together, these findings demonstrate how collaboration with AI can benefit from development efforts which include both subjective and objective metrics.

In the rapidly evolving landscape of artificial intelligence, significant advancements are being made, impacting a broad spectrum of fields ranging from Education [Becker et al.(2018)] to road transit [Banks and Stanton(2019)]. Looking ahead, these advancements are poised to significantly influence the dynamics of team environments. While research on teams only a few years ago highlighted the potential usefulness of AI integration in both research and practical settings, it also acknowledged the limitations of AI technologies in fully mimicking and comprehending the complex aspects of human-team interactions at the time [Seeber et al.(2020)]. However, with recent developments in generative AI and Large Language Models i.e., (OpenAI's GPT-4 [OpenAI(2023)], Google's Bard [Manyika and Hsiao(2023)] and Gemini [Team et al.(2023)]), we are approaching a level where AI-human teams can collaborate more effectively e.g., [Lakhnati et al.(2023)]. This progression prompts a critical question: How can we harness the evolving capabilities of AI to effectively enhance and integrate it into human-AI team dynamics, particularly in settings where traditional automation tools face limitations?

Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence (AI). However, existing studies may focus on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To better understand real-world needs, we interviewed eighteen data workers from both industry and academia to learn where and how they would like to collaborate with AI. Surprisingly, though the participants showed excitement about collaborating with AI, many of them also expressed reluctance and pointed out nuanced reasons. Based on their responses, we first characterize stages and tasks in the practical data storytelling workflows and the desired roles of AI. Then the preferred collaboration patterns in different tasks are identified. Next, we summarize the interviewees' reasons why and why not they would like to collaborate with AI. Finally, we provide suggestions for human-AI collaborative data storytelling to hopefully shed light on future related research.

I have been both a filmmaker and jewelry designer for over two decades. My NFT collection -Infinite Insomnia- involves collaborating with Ai entities to create never-before-seen experimental films as fine art. This is done by devoting meticulous hours working with the Ai to generate scenes and sounds, which are then woven together in a traditional fashion to tell the desired story. The post production in my workflow is identical to that of my IRL film works (editing, sound design, color timing). The front end is also very similar with the story development, writing, and planning phases.

Not for the student, but for the teacher, who plays a short piano melody. Without missing a measure, the student follows with an improvised, yet derivative, cello run. The student plays the same run again, and then again. "I have it looping, actually, so you can hear the response over and over again," says the teacher, Jesse Engel, a computer scientist with Google Brain. "And you can hear some similarities with what I played, but it's not doing the job of trying to replicate what I played. It's trying to continue it in a meaningful way."

Not for the student, but for the teacher, who plays a short piano melody. Without missing a measure, the student follows with an improvised, yet derivative, cello run. The student plays the same run again, and then again. "I have it looping, actually, so you can hear the response over and over again," says the teacher, Jesse Engel, a computer scientist with Google Brain. "And you can hear some similarities with what I played, but it's not doing the job of trying to replicate what I played. It's trying to continue it in a meaningful way."

Not for the student, but for the teacher, who plays a short piano melody. Without missing a measure, the student follows with an improvised, yet derivative, cello run. The student plays the same run again, and then again. "I have it looping, actually, so you can hear the response over and over again," says the teacher, Jesse Engel, a computer scientist with Google Brain. "And you can hear some similarities with what I played, but it's not doing the job of trying to replicate what I played. It's trying to continue it in a meaningful way."