Since the inception of Generative Adversarial Networks (GANs) more than a decade ago, artists have been facing the rising impact of AI systems on their creative practice and livelihood. As a result, the imposition on creativity has led to adverse consequences for the creative community. In this work, we take a foundational approach to mitigating these impacts by proposing a rights-based framework to recognize creativity as a right. In Part 1, we argue for creativity as a human right, and in Part 2, we argue for creativity as a cultural right. We then combine these arguments to present a Co-creative Rights Enablement (CORE) Design Framework comprising rights, capabilities, and possibilities. This work advances creativity in the context of technical change and contributes a rights-based framework for developing co-creative AI systems that is applicable to co-creative system designers, policymakers, and design researchers.

Control is a critical yet underexplored concept in human-AI co-creativity and more broadly human-AI collaboration, where AI systems are expected to act as collaborative partners with creative autonomy. Existing frameworks for characterizing control remain limited and often fall short in capturing the tensions and complexities of co-creation dynamics. In this paper, we examine how experts conceptualize control and expect human-AI control dynamics by leveraging a recent framework on characterizing control as our theoretical probe. We conduct a semi-structured focus group study with nine experts in HCI, co-creativity, and AI. Our findings reveal that control is widely viewed as a dynamic, context-dependent construct that should adapt across different phases of co-creation, domains, and levels of trust in AI. Drawing on our findings, we propose a conceptualization of control along with actionable design implications for designing such AI systems. This work contributes to the literature on Human-AI collaboration, Computational Creativity, and HCI, advancing our understanding of control in co-creative human-AI partnerships.

For more than a decade now, artists' immersion in garnering expression via AI has largely been left to emerge after the fact, where the incorporation of their values into the system is a burgeoning exploration of the latter years.Accordingly, in this dissertation, I ask how we can design harmonious AI systems for creative expression that optimize the balance between artists and AI systems while pushing the boundary and generating creative possibilities for the co-creative process.I take a four-part research approach, beginning by promoting the responsible design of creative AI, then studying the creative possibilities of AI in creative practice, followed by proposing novel interaction mechanisms and concluding with a design philosophy that centers the artist in the Human-AI co-creative process.

Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e.g., domain experts) can quickly generate performant NLP models with support to both control (e.g., via specifying constraints) and understand learned models.