Abstract As a response to the climate crisis, scholarly literature has introduced new theoretical perspectives, such as posthumanism, which seek to reimagine the relationships between humans and nonhuman others, including environments, animals, and plants. Reimagining these relationships depends in large part on our ability to engage nonhumans in their otherness, or alterity, but doing so is challenging. Responding to calls throughout posthuman literature for experimental new modes of imaginative encounter with nonhumans, and inspired by speculative traditions from literature to design, we devise a methodology involving “creative experiments” aimed at disrupting, decentering, and disorienting the human-centered thinking that interferes with humans’ ability to perceive and engage with nonhumans as kin. Simultaneously deploying a number of disruptive tactics—including co-writing with generative AI; working within non-fiction genres that do not exist; to imaginatively express, rather than represent, organisms that can not speak; concerning their experiences of a non-verbal form (music)—we contribute a methodology of speculative writing with AI in pursuit of the secret life of plants.

Explainable AI (XAI) seeks to disclose how an AI system arrives at its outcomes. But the nature of the disclosure depends in part on who needs to understand the AI and the available explanation modalities (e.g., verbal and visual). Users’ preferences regarding explanation modalities might differ, as some might prefer spoken explanations compared to visual ones. However, we argue for broadening the explanation modalities, to consider also tangible and physical forms. In traditional product design, physical forms have mediated people’s interactions with objects; more recently interacting with physical forms has become prominent with IoT and smart devices, such as smart lighting and robotic vacuum cleaners. But how tangible interaction can support AI explanations is not yet well understood.

Synthesis, or the integration of hitherto separated elements, is a prominent concept in theories of design processes. Synthesis often happens when there is a need to make a decision, though it is often the result of a combination of different alternatives, instead of deciding in favor of one and eliminating another. In many design studies, synthesis has been investigated in the contexts of everyday design—bicycle frames, sewing machines, commercial architecture. We were interested in how it might apply in contexts of reflective design, whose pragmatics often depend on different interrelationships between users and technological products. In this paper, we argue that designing everyday use objects for reflection requires a synthesis of two apparently opposite forms: conventionally practical forms, since they are everyday use objects, and evocative forms, since they make users think. We provide two examples of everyday objects for reflection that we believe synthesize both conventionally practical and evocative forms, analyzing the design processes that led to these forms, and discussing how these reflective designs embody different forms of synthesis.

Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and system’s transparency, to name a few. All of these issues are related to how humans interact with AI or ML systems, through an interface which uses different interaction modalities. Prior studies address these issues from a variety of perspectives, spanning from understanding and framing the problems through ethics and Science and Technology Studies (STS) perspectives to finding effective technical solutions to the problems. But what is shared among almost all those efforts is an assumption that if systems can explain the how and why of their predictions, people will have a better perception of control and therefore will trust such systems more, and even can correct their shortcomings. This research field has been called Explainable AI (XAI). In this studio, we take stock on prior efforts in this area; however, we focus on using Tangible and Embodied Interaction (TEI) as an interaction modality for understanding ML. We note that the affordances of physical forms and their behaviors potentially can not only contribute to the explainability of ML systems, but also can contribute to an open environment for criticism. This studio seeks to both critique explainable ML terminology and to map the opportunities that TEI can offer to the HCI for designing more sustainable, graspable and just intelligent systems.