Conceptual impressions surrounding this post have yet to be substantiated, corroborated, confirmed or woven into a larger argument, context or network. Objective: To generate symbolic links between scientific discovery, design awareness and consciousness.
What is considered Abstract Technology?
In the context of design consciousness and its relationship with AI, the term abstract technology typically refers to technologies that are not immediately tangible or physical but instead operate on conceptual, systemic, or algorithmic levels. These technologies influence how we perceive, create, and interact with the world—especially through information structures, computational logic, and invisible infrastructures.
1. Algorithmic Systems
These are sets of rules or computations that govern behavior and decision-making, especially within AI. The design of an algorithm is abstract—its impact is visible (e.g., in search results, recommendations), but the system itself remains invisible.
Example: A recommendation engine shaping user behavior through invisible logic.
2. Data Structures and Ontologies
These are conceptual frameworks for how information is categorized, stored, and retrieved. In AI, how data is labeled, sorted, and related (e.g., in a neural network or database) deeply impacts both functionality and user perception.
Example: The classification system behind an AI image recognition tool.
3. Machine Learning Models
Models like GPT, diffusion models, or reinforcement learning agents are built from abstract representations of language, behavior, or the world. Their structure and training processes are not directly visible but are central to how AI interprets or generates meaning.
Example: GPT "understanding" a query through high-dimensional vector space.
4. User Interface Logic and UX Flows
The structure and flow of digital interfaces are abstractly designed—users experience outcomes, but the logic trees and decision structures behind those interfaces are conceptual in nature.
Example: AI-assisted design tools predicting what a user wants to create before they know it themselves.
5. Ethical Frameworks and Governance Systems
Abstract technology also includes the normative structures embedded in AI—how ethics, bias mitigation, or transparency are programmed (or not) into systems.
Example: The ethical constraints embedded into autonomous vehicle decision-making.
6. AI Consciousness Simulations (Theoretical)
When exploring design consciousness, we may refer to the design of systems that simulate or model consciousness. These are extremely abstract, as they involve representations of awareness, intention, or self-modeling in AI systems.
Example: An AI system designed to reflect on its own decision-making or simulate empathy.
Relationship with Design Consciousness
In design consciousness, creators and thinkers become aware of not just how things look or function, but how systems shape meaning, agency, and experience. Abstract technologies are crucial here because they mediate perception and agency invisibly, often outside user awareness.
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Designer & AI Relationships: From Tools to Co-Evolutionary Partners
The relationship between designers and artificial intelligence (AI) is undergoing a profound transformation, moving beyond the simple framework of human control over technical instruments. As AI systems become increasingly generative, adaptive, and context-sensitive, the dynamics of design practice shift toward more nuanced partnerships. These relationships may be framed along a continuum that spans from traditional tool-use to co-evolutionary collaboration, with ethical stewardship acting as an essential counterbalance throughout.
AI as Instrument and Extension
The most foundational relationship between designer and AI is that of tool and user. In this paradigm, AI serves as a powerful extension of human intention, enabling designers to automate routine or technical aspects of their work—such as layout optimization, prototyping, and parameter exploration. This framework reflects what Shneiderman (2020) has described as human-centered artificial intelligence, where the role of AI is to enhance human performance while maintaining reliability, safety, and trustworthiness. The designer, in this sense, retains full creative agency, positioning AI as an advanced but still subordinate instrument.
AI as Collaborative Partner
As AI technologies evolve, however, they increasingly operate in ways that exceed the tool metaphor. Contemporary design practice often involves AI systems capable of generating novel patterns, forms, or solutions that surprise even their creators. In these contexts, AI can be conceptualized as a collaborative partner, one that contributes to the ideation process alongside human creativity. Davis et al. (2021) argue that co-creativity arises through interaction dynamics, where the iterative exchange between human and machine generates outcomes neither could have produced independently. This collaborative model destabilizes traditional hierarchies of authorship, suggesting a more dialogical form of creativity.
AI as Creative Catalyst
Beyond collaboration, AI can also act as a provocateur or catalyst for new ideas. By producing outputs that diverge from established human expectations, AI systems challenge designers to reconsider assumptions, aesthetic conventions, and conceptual boundaries. Colton and Wiggins (2012) position this role within the broader field of computational creativity, where AI functions less as a passive servant and more as an active agent capable of generating novelty. In this way, the relationship becomes less about efficiency and more about inspiration, allowing designers to leverage AI as a source of unexpected creative disruption.
AI and Co-Evolutionary Design
The relationship between designers and AI is not static but inherently adaptive. Designers train, fine-tune, and guide AI systems, while at the same time AI reshapes the practices, skills, and sensibilities of designers themselves. This co-evolutionary dynamic reflects what Candy and Edmonds (2018) describe as the future of practice-based creative research, wherein human and machine adapt to one another in an ongoing feedback loop. Here, the boundaries between human agency and machine affordances blur, pointing toward a hybrid model of design where creativity emerges from mutual adaptation.
The Ethical Counterpart
While much discussion emphasizes creativity and efficiency, the designer’s role in relation to AI must also be understood as one of ethical responsibility. AI systems are not value-neutral; they reflect the biases of their training data and the intentions of their creators. Designers thus act as ethical stewards, embedding considerations of justice, equity, and accountability into their collaborations with AI. Brennen (2020) highlights the urgent need to address misinformation, manipulation, and social harm in the digital era—an imperative that extends to the domain of design. In this respect, the relationship between designer and AI becomes one not only of co-creation but also of critical oversight.
Conclusion
The relationship between designers and AI cannot be reduced to a single static model. Instead, it spans a spectrum that begins with instrumental tool-use, extends through collaborative and catalytic partnerships, and evolves into reflexive co-adaptation. Overlaying all of these dimensions is the ethical imperative, where designers must critically engage with the implications of their collaborations. As AI continues to evolve, so too will the relational landscape of design, pointing toward futures where human creativity and artificial intelligence are inseparably intertwined.
References
Brennen, J. S. (2020). Making sense of lies, misinformation, and conspiracy theories in the digital age. Media and Communication, 8(2), 1–4.
Candy, L., & Edmonds, E. (2018). Practice-based research in the creative arts: Foundations and futures from the front line. Leonardo, 51(1), 63–69.
Colton, S., & Wiggins, G. A. (2012). Computational creativity: The final frontier? In Proceedings of the 20th European Conference on Artificial Intelligence (pp. 21–26).
Davis, N., Hsiao, C.-P., Yashraj Singh, K., Li, L., & Magerko, B. (2021). Creative sense-making: Quantifying interaction dynamics in co-creative systems. International Journal of Human–Computer Studies, 149, 102605.
Shneiderman, B. (2020). International Journal of Human–Computer Interaction, 36(6), 495–504.
The author generated this text in part with GPT-3, OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.
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Edited: Find your truth. Know your mind. Follow your heart. Love eternal will not be denied. Discernment is an integral part of self-mastery. You may share this post on a non-commercial basis, the author and URL to be included. Please note … posts are continually being edited. All rights reserved. Copyright © 2025 C.G. Garant.
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