The birth of the GUI, generative design, hackers, privacy, participatory culture, networks, augmented humans, machine learning—this seminar addressed these topics and more through reading, discussion, and brief design exercises. As a reflection point for the semester, students contributed to the existing scholarship around one of these topics via original content—in this case, a seminar paper.
Traditional book publishing is a hierarchical system. Publishers select promising texts from the slew of works submitted by authors and turn those works into books to sell to readers (Haugland, 2006). Robert Darnton’s model of the traditional publication process shows how a book moves from author to reader in a cyclical way, passing through publishers, editors, and printers before a reader sees any part of the work (Darnton, 2007).
Figure 1. Based on Robert Darnton’s “The Communications Circuit,” 1982
Although Darnton’s model is based on publishing in 18th Century France, researchers still generally accept it as an accurate representation of the publication process (Pecoskie & Hill, 2015). The publication process places writers and readers at opposite ends of a circuit, leaving the reader out of the creative process.
The rise of Web 2.0 has disrupted, or shifted, the relationships in the communication circuit by allowing readers to get involved in the creation process. Readers have moved from passive absorbers of content to co-creators (Gauntlett, 2011). This bypassing of the traditional hierarchy reflects the broader effects of participatory culture: greater collaboration between people and more dispersed networks (Langlois, 2013). Authors can now reach audiences and generate followings before any interaction with traditional publishers. These projects are great sources of interesting content, and make for interesting publications when they are printed, but the traditional publication and copyright systems focus on traditional authors, rather than a community of co-creators. The methods used by the print publication and copyright systems can cause backlash and anger among project participants who feel exploited when a participatory project becomes a printed work. To avoid exploiting participants, designers should structure participatory projects to financially benefit the whole community, recognize participants’ contributions, and clearly communicate intentions.
Today, many projects occupy a liminal space between being a totally digital, Web 2.0 creation, and a traditional work sponsored by a publishing house. Stefan Bucher developed a large following with his online participatory project, The Daily Monster project, and used that following to push a traditional publishing house to pick up his 100 Days of Monsters book (Armstrong, 2011). Readers were actively involved before a publishing house, demonstrating the ability of participatory projects to disrupt the traditional publishing hierarchy. The project challenged publishing firms’ role as arbiters of what gets published. In this case, the book was a reward, a tangible and shareable artifact, for the community that had grown up around Bucher’s project. Community members felt like respected parts of the creation and publication processes.
While Bucher’s book did not include the work of participants, works like 50 Shades of Grey, Pantsuit Nation, and Postsecret are co-created artifacts that involve the work of whole communities. The rise of co-created projects has led to issues of how to reward these co-creators. Authors receive royalties and book deals from publishing houses that print and sell their work, but the traditional publication system focuses on traditional authors, failing to take into account the numerous co-creators that exist when a work develops on a participatory platform. Participatory projects published through traditional systems have the potential to further democratize and expand the print publication system, but co-creators must be fairly rewarded for their contributions.
There are several examples of participatory projects that have brought fame and money to a single author, while generating outrage and resentment among those who had been supporters and co-creators. 50 Shades of Grey is a famous example of a work that began as an online project supported and edited by a large community. Using the screen name “Snowqueen’s Icedragon,” author E.L. James wrote and released the work serially on fanfiction websites dedicated to Stephenie Meyer’s Twilight series. Community members, called beta-readers, provided comments, edits, and suggestions after every installment, guiding and assisting with the novel’s development. The work had a huge following in fan fiction communities before any major publishing firm took notice (Pecoskie & Hill, 2015). When James pulled the novel off fan fiction forums and published it as 50 Shades of Grey, she caused a great deal of uproar in the communities that helped create it, with members actively attacking the author on twitter (Brennan & Large, 2014). The book was a mainstream success, generating large profits and movie deals, but its publication did nothing to compensate the community that helped create it.
More recently, members of the private Facebook forum, Pantsuit Nation, reacted angrily when the group’s founder secured a book deal for content posted in the group (Alter 2016). Articles condemned the group as “a sham” and “the worst” (Ryan, 2016 and Lewis, 2016). The main arguments critics made were over the lack of transparency surrounding profits, ownership, and compensation for content, as well as how a coffee table type publication fit the community’s goals (Alter, 2016).
While projects like 50 Shades of Grey and Pantsuit Nation have led to outrage and division among communities, other projects have successfully maintained community involvement and a sense of fair compensation. Projects that successfully made the transition without exploiting co-creators use publication as a continuation of community, recognize the work of co-creators, and maintain high-levels of transparency. For example, Frank Warren’s Postsecret project, which consists of a blog of anonymous, participant created postcards featuring secrets, has published six books to date and continues to grow in popularity. The proceeds from book sales go to keeping the project going, and the site itself ad free. Participants can see their cards printed, as they made them, in the books, an anonymous acknowledgement of their work (Fox, 2015). This structure allows Warren to use publication as a means of supporting the project, while not exploiting the work of participants.
Part of the outrage over these projects comes from the nature of the platforms participants use to create them. Participatory platforms operate on a “commons” based structure, where, unlike property, no individual has exclusive control over the use and disposal of the resources in the commons (Benkler, 2006). The rules in a commons based system range from anything goes to formal and articulated, just as they do on these participatory platforms. In fanfiction forums, members see fan fiction as a “gift culture,” with the love of the story being the main reward (Cuccinello, 2017). When a user pulls a publication off of a participatory platform to publish it, the action challenges the collective understanding of the commons. In the case of 50 Shades of Grey, E.L. James removed the book from the community forums that built it, thereby actively rejecting the general goals of the community and depriving those who helped create it of the compensation (free access to the work) that they expected (Brennan, 2014).
Traditional copyright is part of the problem in this situation. By granting copyright to single individuals or publishing houses, the law discounts the contributions of co-creators. These laws were created for an age when “the technologies of publishing were expensive” and fails to take into account the nature of creativity, in that “creators here and everywhere are always and at all times building upon the creativity that went before and that surrounds them now.” (Lessig, 2004) A more expansive, open source type license could protect these communities from exploitation. Licenses like Creative Commons licenses allow creators and co-creators to broadly copyright their creations, leaving them open to distribution and remixing, while requiring attribution and potentially limiting commercial use (creativecommons.org). Publishing firms use restrictive licenses to protect their profit structures, which limit reproduction and give them sole rights to distribute a work, but the rise of participatory projects make that structure problematic. Co-creators provide diverse and unique voices that expand the offerings available to consumers. Their contributions require time and dedication, input that deserves compensation and acknowledgement (Langlois, 2013 and Banet-Weiser et al, 2014). Participatory platforms can help make some of these changes. Incorporating adaptable copyrights into platforms would protect participants and the communities that use them, while allowing content to be remixed and published in print. Participatory projects published through traditional systems have the potential to further democratize and expand the print publication system, but co-creators must be fairly rewarded for their contributions.
Projects that use traditional publication systems as a means of financially sustaining the whole community have been successful at both moving into monetizing their content and keeping the community happy and actively participating. Often participants in collaborative projects are willing to overlook the monetization of their participation when it is done as means to keep the community going. YouTube, for example, has made millions of dollars of advertising on its site, but participants continue to post their content, seeing advertising as a fair tradeoff to having a free online environment (Gauntlett, 91). Participatory projects should benefit the entire community that creates them, even if it’s only through the joy participants receive by participating, and if a project is going to be used to generate profits, those profits should in some way benefit the community as well.
In addition to community support, participants in online collaborative projects look for recognition of their labor when projects move outside the community. In Making is Connecting David Gauntlett argues that most people contribute to participatory projects out of a desire to “feel active and recognized” or noticed by members of the community to which they contribute (22). Participatory projects that move from online to print publications have to maintain some way to acknowledge the work of participants. Postsecret, for example, prints postcards exactly as they appear, allowing participants to see their work as part of the community (Fox, 2015). Projects that do not acknowledge the work of the community lead to outrage and anger from community members. This was one of the arguments critics made about 50 Shades of Grey when it was published: community members challenge the authenticity of the book’s published history because it failed to acknowledge input from the fanfiction community (Brennan, 2014). If participants are not being compensated monetarily, recognition is incredibly important to maintain the community around a project.
Designers of participatory projects need to operate in an open and transparent way to maintain the trust of the communities they serve. The planned Pantsuit Nation book seems to avoid the issue of recognition. The creator, Libby Chamberlain, claims “to amplify the collective voices of the women who shared their stories” (Alter, 2016). However, the project’s lack of transparency has led to much of the outrage over the planned book. Both the authors of Pantsuit Nation and 50 Shades of Grey announced their book deals without involvement from the communities where the projects originated. Co-creators believed they were working under one set of community understandings and later on found out that their work would be used for individual profit under a completely different set of rules. The intentions and goals of the project should be clearly expressed to participants in order to avoid backlash.
Participatory platforms have a huge amount of potential to democratize the traditional publication system. This shift in power relations can push publishing towards a less elite and hierarchical system, giving more access to the means of production and reaching audiences. However, the traditional publication structure is not set up to reward the work of co-creators in participatory projects. To take advantage of the diversity of participatory culture, while not exploiting co-creators, authors who want to merge participatory projects with traditional print publication must structure their projects to financially benefit the whole community, recognize participants’ contributions, and clearly communicate intentions. Currently the copyright and publication systems disregard community needs when they pull works off of participatory platforms. Neither system acknowledges that creativity is a communal activity. This behavior has the potential to discourage participation and negatively impact these diverse communities. However, if the system begins to acknowledge co-creators, with both recognition and more open copyrights, it opens the door to more diverse voices and works available in print.
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How can designers partner with AI?
How can designers partner with AI to empower their leading position in the creative design process?
Machines began to replace humans in the first industrial revolution. In the decades coming, according to Wired founder Kevin Kelly, we should expect a second industrial revolution (Kelly, 2016). Artificial intelligence will undergird this new revolution in which a cognification of our environment will create more intuitive interactions between people and their environments.
In my review of the literature, it is obvious that artificial intelligence (AI) will replace many jobs, but I also found literature suggesting new roles for humans alongside AI. What will these new roles entail? What will this partnership mean for the workers and their daily activities? This study will consider the use of AI in the context of the creative design process as a way to complement designers rather than replace them.
Artificial Intelligence or AI “refers to non-sentient intelligence that can deliver complex tasks previously performed by people” (Fisher, 2015). The market increasingly recognizes the potential of AI.. In addition, AI is growing in its applications where many approaches live together such as the idea of enhancing the human brain by implementing technology, or also, ideas involving the development of machine learning. I focus this research on the latter.
Machine learning is a type of AI through which computers learn without the need of being programmed. Computer programs learn by analyzing and interpreting data which can be recognized by an AI without the need for specifications. A key purpose of machine learning and AI is automation. In an era focused on productivity, automation is appealing.
AI in a Human World - SWOT
I investigated some existing machine learning applications available for designers to use in their creative process. I built a SWOT analysis to consider AI technology in our current design production process.
AI surpasses the human designer in many areas with special regard to fearlessness. Fear is not part of the AI design process, producing bold and unexpected designs that a human might consider outside of good taste and traditional aesthetics (Rolston, 2016).
AI also has the potential for infinite memory— the idea that every piece of information that the AI encounters is stored forever within its memory. Infinite memory will result in a higher and faster response rate and an easy interpolation of the data. AIs can use this memory capacity to improve their relationships with users, possibly predicting human behavior and thus better answering user need.
The power of AI creation could grow exponentially based on the power of the machine. For humans, this power only increases as the team of humans increases in number. An AI could create hyper-personalized designs and tools at a rate with which human designers cannot keep pace (Rolston, 2016).
In recent years, computer recognition and computer learning systems have deconstructed massive datasets informing the machine about ways to perceive and manipulate design creations. Examples such as Prisma and Google AutoDraw, reveal that with simple codified heuristics, rules, best practices and principles, many perceptual activities that the human designer now do will done by the machine (Girling, 2016).
The main weakness for the AI lies in its creative intelligence (Carl Benedikt Frey’s and Michael A. Osborne, 2013). It is unlikely for an AI to come up with valuable ideas and different ways to solve complex problems, especially in a context where values change depending on the time, the people, their personalities, and cultures.
Such ideas are hard to codify. Both AutoDesk and Logojoy.com exemplify the limited creative intelligence of the AI. Both applications provide hyper-personalization but in a way that is limited to the input of the designer’s or user’s choice of parameters, variables, and preferences. The human touch, and subjective considerations of the human experience with its thoughts and cognition, are still crucial in the design process.
The design field is an ideal area for AI to both exploit and create opportunities, as most modern design activity relies on grids, templates, and rules which are easily codified and thus accessible to the AI. The Grid is a perfect example of these codified rules used by AI to design the UI for hyper-personalized websites. The Grid claims to empower designers by freeing them from technical tasks allowing them to focus, instead, on the creative and strategic direction of the work.
Designers in an AI World - SWOT
Designers currently surpass artificial intelligence through creative and social intelligence. Creative and social intelligence defines design jobs in which skills such as empathy, problem understanding, problem solving, negotiation, and persuasion are required (Carl Benedikt Frey’s and Michael A. Osborne, 2013).
Creative intelligence is the ability to come up with valuable ideas and figure out ways to solve different kinds of problems (Girling, 2016). This intelligence expresses a range of ideas that are difficult to codify because of the complexity of context where those ideas evolve. Creative intelligence also reflects the ability of humans to formulate the right questions that open a range of different answers and solutions. It is the ability to move from the very practical to more conceptual, unique ideas.
Social intelligence allows human designers to recognize emotions in real-time to display the complexity of humans’ social, cultural, and emotional behaviors, especially in the unique dynamics created in every situation. Human designers have an important place in those functions that require high social intelligence and a great understanding of culture and humanity (Girling, 2016).
Designers are very dependent on the set of rules that are at the foundation of the practice thus making their job vulnerable to automation (Fisher, 2015). The International Typographic Style is the perfect example of a design style that can be produced following specific rules such as using a grid, an asymmetric layout, and a sans-serif font. Considering these rules, the Swiss Style would be easier to translate through an algorithm than a more complex style such as the New Wave. Automation would also lend itself to other rules like proportions, sizes, resolutions, colors, white space. It is this prescriptive work that is easily codified and replaceable through the application of algorithms (Peart, 2016). Along with these rules, there are also methods such as the use of scripts or templates that designers follow in their creative design process to answer to specific audiences, often mass-market audiences, which is a normalizing process and opens the door to automation (Rolston, 2016).
In addition, humans make errors. We often see in investigation reports the phrase, “due to human error,” as the response to a problem. These errors often result from stress that impacts productivity while not having any beneficial effect on the resulting quality (Cross, 2001). In the design industry, stress often arises from competitivity thus creating a need for fast productivity, short deadlines and long hours that impact the efficiency of the design and the quality of the work as well as the risk of making errors such as underestimating the brief, saving in the wrong format and losing work.
Though many designers are detail-oriented and creative, they’re not usually trained in emotional awareness, statistics, or conversational messaging. Designers will need to learn these skills from other fields in order to keep up with these new roles that designers are going to take on. The designer of the future will be both scientific and creative, and designers today need to understand what that means for how they build smart systems (Van Hoof, 2016).
The opportunity behind AI is that the automation of those design processes will redirect the designer’s energy, conservation, and creativity to items that add value to their work while allowing more freedom for the designer to endorse more interesting work. Indeed, all the tedious and repetitive tasks could then be undertaken by the AI, freeing up time for the designers’ cognitive space to focus on more important creative and intellectually demanding activities (Girling, 2016; Rolston, 2016; Schwab, 2017).
Designers will also be able to work on more conceptual and subjective work and thus no longer be limited to the design of the structures holding and displaying the information (Fisher, 2015). The development of AI will generate new spaces for designers to experiment and design for and provide designers a new palette of tools at their disposal (Peart, 2016).
Finally, designers will also have a set of new roles such as working to define design-practice ethics that evolve with the use of AI. Designers will evolve in roles involving more directing, selecting, conducting, and curating while seeing diminishment in transactional making and creating (Peart, 2016; Girling, 2017). Designers will use their AI tools to solve design problems by creating a range of design solutions and then contribute by choosing the best solution(s) and implementing new parameters to obtain the best results. This process will liberate the designer from stress as the designer becomes the supervisor (Cross, 2001).
One of the biggest threats of AI is the replacement of designers by machines. For the remaining jobs, designers will have to be trained to work with AI, and also, develop the new multi-disciplinary skills required for the new role of designer—enhanced skillsets as multi-disciplinary professionals.
In terms of design content, AI will create an exponential growth in competition (Peart, 2016) leading to an unbalanced competition between human designers that are able to work on a handful of projects per day while the AI provides infinite numbers of prototypes and outcomes from numerous processes.
AI and Designer Partnership
By understanding the different advantages and disadvantages between AI and designers, we can consider a partnership where strengths from both parts could join to maintain the leading position of the designer in the creative design process. A computer program could analyze the millions of designs on Behance and understand what trends and styles of imagery are gaining traction. Draft pictures could be generated to create a constantly moving library of style that could be used as a starting point for the work of the designer. This example shows the work of an AI that uses machine learning to understand design influences through patterns, symbols, icons, colors, and typography in order to create a thematized library of work to help the designer with visual ideas. The designer could then quickly discern the design possibilities for a concept.
A computer could also instantaneously help the designer while producing work. Through utilization of the software, the AI would create alternatives of the design while facilitating the iteration of the design and investing more time in the ideation phase. In that ideation phase, the AI would have the ability to push the designer on an unexpected path while helping him enhance his creativity.
This new relationship between the AI and the designer would take place gradually. Based on designer instructions, the AI could generate increasingly precise work after each critique from the designer. By letting the machine take control of the most tedious and repetitive jobs, the designer will have time to work more deeply on the concept and the content of those design while improving the efficiency of the workflow. With this extra time, designers will be able to create work that is out of the norm, not codified, and beyond the cultural-cliches (easily codified). Historically, designers have focused on creating smart objects. Now, I believe that we should focus on becoming smart designers and teaching generations of designers to become smart by specializing in work requiring human creative and social intelligence, human empathy, and creative storytelling. Those smart designers will be up-to-date with the latest technologies and processes, focus on creating content and design to solve problems, and design for the future of communications while helping to support social and cultural changes. Their major focus would be to create content being able to be viewed and accessible for everyone while ensuring their privacy.
In this partnership we can also see a huge opportunity for designers in the design of interactions we will have with these emerging AI systems. How do we design AI design tools? How do we communicate to the machine, and how does this interface look? Following my research question, how should we design these interfaces to help empower our creativity in this AI-designer partnership? In this new space carved out by AI efficiency, there will be room for real amplifications of designers’ creativity. We have already seen this happening when the first Mac computer came out; at first designers felt that applications were going to replace their jobs. However, designers such as April Greiman and the Design Type Foundry Emigre used the new medium to express their creativity. They also experimented with this new tool and developed pioneering work that Greiman called “hybrid design.”
Researching and analyzing both the work of designers and AI through current examples of machine learning systems, and also through the application of a SWOT analysis or simply through the lense of the creative design process, allowed me to consider the implementation of AI in the designer creative process. Today, AI applications and systems in the design realm suggest what we can expect in the coming years. Research revealed that AI could be both a partner and an empowering tool for the designer to focus on a more curative and intelligent position. This study confirmed the potential for AI to be created in a way that does not simply replace the human designer, but on the contrary, enhances and empowers the role of the designer. I believe it is important that the design community be involved in defining emerging AI to ensure that it will become a tool that partners with humans rather than an autonomous machine. The only way to maintain the importance of the human designer is to embrace technology in a way that will allow us to discover new work opportunities beyond the ones being replaced.
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