The Looking Box
Image Credit: HCC
Transcending the Black Box
Image Credit: HCC
X-Plain
Image Credit: HCC
On the Way to Balance
Image Credit: HCC
ORECI
Image Credit: HCC
Selected Outcomes
The workshop culminated in a gallery walk and presentation session, where participants used storytelling and role-play to showcase their speculative machines. The resulting machines demonstrated a remarkable diversity of perspectives on how AI could become more understandable, accountable, and socially beneficial.
For example, “The Looking Box,” a machine designed to reveal AI processes through different human perspectives and needs. By applying various metaphorical filters, users could “look inside” otherwise opaque AI systems, highlighting how understanding AI depends on individual experiences, contexts, and values.
Another group developed “Transcending the Black Box,” featuring a cloud-shaped structure representing AI knowledge. Rather than depicting intelligence as residing solely within machines, the concept emphasized that AI knowledge continuously grows through networks of human-AI interactions, collaboration, and shared learning. The machine challenged conventional notions of AI as an isolated technology, instead framing it as a socio-technical system co-created by people.
The “X-Plain” machine focused on making AI data flows more visible and easier to understand. By representing different data streams and processing stages, the machine sought to demystify how information is collected, transformed, and used by AI systems. By making these processes tangible, the concept aimed to improve data literacy and strengthen users' capacity to critically engage with AI-driven decisions.
“On the Way to Balance” took the form of a robot traveling along a long and winding road. The machine symbolized the ongoing journey towards balancing data-driven innovation with human needs, ethical considerations, and societal values. Rather than presenting balance as an achieved state, the design highlighted it as a continuous process that requires negotiation, reflection, and collective responsibility.
Another group presented “ORECI,” a framework for transparent and ethical AI interaction. The concept envisioned AI systems that actively communicate their intentions, limitations, and decision-making processes. By placing human values at the center of AI interactions, the machine encouraged participants to consider how trust and accountability might be designed into future technologies from the outset.