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Whose design process?
The dynamics of designing with generative creation
“In computational art and design, many responses to the questions of what and why continue historic lines of creative inquiry centered on procedure, connection, abstraction, authorship, the nature of time, and the role of chance.” (Levin & Brain, 2021).
The first relevant attempts of using computers as a means of creative production happened in the 1960s when programmers and artists began experimenting with the machines to generate shapes and sounds. In the beginning, the introduction of computers to the creative community faced resistance, especially because of their mechanical, mathematical, and multidisciplinary nature. At the core of the debate was the comprehension that creating artifacts alongside computational systems involved ceding a part of the creative process — which before belonged entirely to humans— to the machine. Such a shift naturally challenged the conventional conception of ownership, as it raised an intuitive question:
Who should be considered the author of a given piece of intellectual production, art, or design when computers are involved? The human, the machine, or both?
With time, and as computers got more popular, such resistance diminished as creators embraced digital techniques, whether by choice or pressure of the market. In any case, an important first remark for our discussion is the realization that sharing authorship with machines is not something new brought by AI, but rather something that has been unfolding throughout the past decades and that was more recently intensified. This is also why we are now seeing a re-edition of the ownership debate I discussed before.
Nonetheless, the possibility of pairing with computational intelligence has since the beginning motivated professionals to explore these machines as a fruitful creative medium.
For designers, for example, accustomed to apprehending current technologies and repurposing them to the tasks at hand, such exploration came with many intents. Some of these were to extrapolate the capabilities delimited by available proprietary software (think of Adobe, Macromedia, Sketch, Figma), to obtain novel and unpredictable aesthetics, to enable the work with parameterization and optimization, or for building artifacts that respond more autonomously in real-time. Moreover, in recent years, many designers have been leveraging generative creation very literally, building or employing systems that can render flexible visual identities, parametric objects, responsive interfaces, and generative fonts.
In many ways, ceding a part of our creative processes to machines allowed us to design more efficiently, accurately, and with greater—or at least novel—expression.
We say something is generative when it is capable of producing an outcome or reproducing itself autonomously. Therefore, designing with generative creation implies intentionally employing an autonomous element that contributes to the achievement of a certain goal or to the synthesis of desired outcomes. Such autonomy can be granted in several ways: by letting systems make choices based on complex models, by relying on sufficiently smart Gen-AI agents, by designing with genetic algorithms, or simply by designing systems to respond to unexpected interactions.
This is why we say generative AI is generative. Because such agents are capable of autonomously generating outputs and synthesizing artifacts, regardless of how predictable the input is. Some even learn from previous interactions to respond in smarter ways and produce even better.
Formally, generative creation means employing systems or processes, which are put into execution with a certain degree of autonomy contributing to or resulting in a complete work. The critical point is that computational intelligence is intentionally used as an active participant in the creative process and not only to support the decisions made by humans (Groß et al., 2018; Grünberger, 2019; Galanter, 2003).
In design, working with such computational autonomy promotes a fundamental change in the creative process as designers become no longer executors of tasks, but conductors. A role that Groß and his collaborators consider to be that of an “orchestrator of decision-making processes” in their book Generative Design (2018). Essentially, bringing generative agents to our work means giving up total control, which is now partially conducted by a form of computational intelligence we need to manage.
To illustrate this fundamental change, Groß proposed a model for the design process around generative creation characterized by an emphasis on abstraction. The main change, according to him, is not only that traditional craft recedes to the background while abstraction and information become the protagonists, but also that designers need to constantly reflect on how to translate their ideas into information that autonomous agents can "understand".
Thus, the relevant question is no longer “How do I draw/sketch/paint?”, but “How do I abstract?”.
Groß's original illustration for the model focused on generative design through coding, but I amended it to highlight the possible role of generative AI agents (in orange).
Note that in opposition to the conventional process in which designers implement an idea directly, when generative creation is employed a process of abstraction is carried out to transform our ideas into pieces that can feed the generative engine. Until recently, to work with such technology designers would either need to program themselves or partner with engineers skilled in building systems around generative logic. Tools powered by Gen-AI, however, brought new interfaces capable of streamlining such processes, allowing designers to more easily partner with intelligent agents to render their intentions. Prompting in natural language became the quasi-standard interface for that, but almost every popular design tool nowadays either has or is developing its own GenAI features aiming to cater best to how we work (sketching, manipulating images, brainstorming visual ideas…).
Regardless of the approach, bringing generative creation into the design process turns it into a somehow “indirect” one, meaning that designers participate, even if only partially, through activities that happen on a level of abstraction above the craft, such as planning input to AI systems, prompting through natural language, creating algorithms or rules, programming, evaluating the results generated, and refining output until they get a satisfactory result. In fact, when suggesting that ‘How do I abstract?’ is the most relevant question now, Groß is not only illustrating the existence of a layer of abstraction that intermediates the design process but also demonstrating the continuation and intensification of the approximation between design, art, and computing we have been witnessing throughout the last decades.
I believe that for us designers, this trend poses more than ever the need to deeply comprehend the most influential concepts of design and technology of our times. Not only that, we might also need to become masters of abstraction, which would ultimately enable us to translate our design intentions, tasks, and goals into input for generative systems while assuring the quality we intend. Perhaps, this will even be a differentiator for designers in the future: "Who can partner better with the machine? Who is capable of establishing alignment between both human and AI stakeholders?". In other articles, I discussed the trajectory of art, mathematics and computing, which I believe comes as great inspiration for us.
At the crossroads of a crucial moment for the future of technology, I believe every designer should have the following in mind. On one hand, working with generative creation can indeed help us make our work faster and more efficient, and unlock our creative processes to paths we wouldn’t consider otherwise. Aesthetically, for example, one of the main strengths of generative creation is its ability to offer new directions to design projects and break with habitual and predictable choices of form and representation — something that occurs because AI agents can be adopted as co-drawers with whom designers need to “negotiate” creation (Agkathidis, 2015).
When it comes to how we design with generative systems, it helps to consider the existence of two possible approaches. In the first approach, which authors Zhang and Funk call concept-based ideation, creative work begins conceptually, in our heads, probably before computational tools are used. Since the main challenge in this approach is to turn an already existing abstract idea into a satisfactory outcome, generative creation comes as a tool that designers employ to materialize concepts or execute tasks according to their expectations.
In the second approach, material-based ideation, these systems are seen as the creative “material” that will be experimented with, and which will point out concepts to be elaborated. More practically, this means that the creative work will gradually take a more concrete shape and direction only after a series of experiments with different software tools, AI agents, and systems are executed. In his book Analog Algorithm (2021), Christoph Grünberger acknowledges that this is essentially a practice in which total control is abandoned in favor of results, which can mean greater quality, speed, or aesthetic novelty. Because of this, the author states that designers start behaving mainly as interpreters and curators.
On the other hand, generative systems whether powered by AI or not, are not neutral or immaterial entities but rather exist as a complex chain of interests, ownership, capital, and usage of natural resources with very practical and concerning implications. If today’s drive of weaving technology into creativity continues a thread that has been unfolding for decades already, embracing this paradigm without thorough criticism would mean simply disregarding the urgency of our times. At this very moment, creative professionals of all kinds are trying to understand their place in a future of economic uncertainty in which AI seems capable of delivering aesthetic quality with unmatched speed. In perhaps more philosophical terms, the subject also gets us questioning and reflecting on the purpose of what we do… in a world in which machines can create, what is creativity after all? And perhaps more interestingly: who’s the author of an artifact produced through generative techniques?
My views on technology are those of socio-technical systems, meaning I don’t see how the tech tools our societies create can be considered apart from their own organization, motivations, and human aspects in general. Therefore, in a moment of pressing climate challenges, economic turmoils, and increasing global tension, I cannot help but see the race for AI as a symptom of a logic that pushes human needs aside, seeks profit at all costs, and disregards environmental and social long-term consequences. Yet, this is a trend we need to observe and I believe that shaping a critical view of such a complex topic requires a comprehensive set of skills that won’t likely be covered by a single expertise. Therefore, this text is first and foremost a call for multidisciplinarity, critical thinking, and collaboration.
In the words of authors Brian and Levin:
“With the fracturing of civic life after social media, the malignant growth of digital authoritarianism, and the looming threat of environmental catastrophe, the sheen has come off Silicon Valley and the folly of technological solutionism has become clear.
To the extent that we continue to prototype new futures within the framework of late capitalism, […] there is a new urgency for artists and designers to have a seat at the tables where technological agendas are set”.
In this sense, the authors argue that “technologically literate” designers have an essential role to play in “checking society’s worst impulses”. Not only because we can ring the alarms when freedom and imagination are threatened, but also because we are in a better position to try and make space in conversations and organizations for as many perspectives as possible. Hopefully, also the most critical ones. In fact, many designers and artists have been engaging in political, experimental and subversive initiatives through generative poetics aiming to challenge their main applications and also to repurpose their use. An example is Lucas Rochelle's QT.bot, an AI agent that generates speculative queer futures along with possible scenes, defying the "normalizing character" of classification agents.
Another example is the set of projects Design Against the Machine, developed by professor Boris Müller's students when challenged to explore "speculative future scenarios through creative, experimental websites, co-created with AI". One of the projects, Luminari uses the help of GenAI to picture a "Solar Renaissance Eco-Utopia", in which humans beat climate change through miraculous improvements in photovoltaic technologies, and manage to establish a healthy relationship with the environment. In another perhaps more provocative project, The User Manual, we are invited to consider a world in which machines are not only made to be operated by humans but also by other machines. Who is the user in this case? Who should we have in mind then when planning the User Manual?
As a final note: I believe designers can and should be active critical agents in this debate, instigating reflections and pushing for positive change. It is true that in the last few years, the design field has been facing challenging times, a series of layoffs, and a social reality full of complex problems—most notable among them is the explicit and concerning complacency to right-wing extremism from the leaders of companies that have been shaping our profession for decades. Not surprisingly, our community lives through a moment of apathy as it seeks out its place once more. Yet, if the future is not being designed by those who care, then we’re guaranteed to have no positive future.
If you liked this text, consider taking a look at the other articles of the series on code, shape and meaning.
For more reflections on technology and society, consider subscribing to my newsletter de.material! I am starting it!
References:
Agkathidis, Asterios. Generative Design. 1. ed. London: Laurence King Publishing, 2015.
Beecher, Karl. Computational Thinking: A beginner’s guide to problem-solving and programming. Swindon: BCS Learning & Development Limited, 2017.
Blais, Joline, IPPOLITO, Jon. At the Edge of Art. Thames & Hudson, 2006.
Brain, Tega; Levin, Golan. Code as Creative Medium: A Handbook for Computational Art and Design. Cambridge: MIT Press, 2021.
Galanter, Philip. What Is Generative Art?: Complexity Theory as a Context for Art Theory. 2003.
Groß, Benedikt et. al. Generative Design: Visualize, Program, and Create with JavaScript in p5.js. 2018
Pearson, Matt. Generative Art. Manning, 2011.
Taylor, Grant. When the Machine Made Art: The Troubled History of Computer Art. London: Bloomsbury Academic, 2014.
Zhang, Yu; Funk, Mathias. Coding Art: The Four Steps to Creative Programming with the Processing Language. New York: Apress, 2021.
