Automation Based Creative Design: Research and Perspectives

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Occasionally, I was asked to interview with an early career cognitive scientist a discipline mostly is concerned with an individual's brain function.

I consulted former colleague and design anthropologist Dr. Susan Squires, and asked for her take on why I was repeatedly being asked to list methods and to "geek out" on terminology in these research interviews.

  • Madwoman On the Bridge and Other Stories;
  • Back to the future.
  • 2. The redefinition of jobs and business processes.
  • The Automation of Qualitative Methods - EPIC;

She observed that those who are measuring my worth by lists of methods may be confusing data collection with research, and that to them, the more methods one has or can discuss, the better and more senior they will be at what they define as "research. These research managers seem to have a need to quantify methods or other attributes. For the most part, ethnography is omitted from the conversation, or described as a single method rather than as a toolkit of methods. The people in control of interviews do not understand the basics of ethnographic methodology and what anthropology does, or can do for them, instead assuming it to be a part of a discipline that can be streamlined, made 'lean,' easily quantified, and lumped in with their other methods.

In design, the idea of 'simplify' has worked well visually in terms of clearing up visual space, but it has done nothing to handle complexity. I spoke about this with Daniel Drennan El-Awar, a graphic designer and former professor in graphic design and media design.

He reminded me that designers today are still influenced by Bauhaus, an experimental German post-World War I modernist movement founded on the idea of reduction and simplicity of both form and color palette, which additionally incorporated white space. Building on that notion, as a design movement from the early part of the 20th century, Bauhaus could be considered a form of automation, which was also debated at the time. Today, we still see the Bauhaus influence in our furnishing movements, architecture, and other areas of design that rely on the notion and visual language of clean, white space, and simplicity as aspirational qualities—even though for the most part Bauhaus did not produce livable spaces that endured for communities of people.

Its reductionist principles are simply unsuited to address social complexity. Developed by an engineer at Motorola as a process improvement strategy, Six Sigma was adopted across a wide range of industries, especially business.

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Much of the focus of Six Sigma is on quantitative measurement, and more recently, Six-Sigma has been adopted as a way to streamline UX design, by reducing it to standardized principles, and applying these across all forms of design. Organizations wanting a fast formula for 'better' processes can easily be persuaded to include the reductionist rules and methodology of Six Sigma, which advocates rigid methods that are perceived to be a reliable formula.

When designers and engineers work together in engineering based organizations, any process that provides quantified results, which support a quantitative, mathematical, and reductionist approach, seems to be preferred. It makes logical sense that Six Sigma's process would derive from an engineering mindset, and it makes logical sense that when faced with users and their needs, engineering would look for an engineering solution to solving said 'problems' with regard to people.

Engineering, in particular as it is practiced in Silicon Valley, also has an aspirational vision described in Science Fiction, and which is built visually on the design forms founded in the s and s based on Bauhaus. Although we've moved along in years, many of our design and engineering visions are inspired by a year-old imagination of the future, which is often described in a context years ahead of where we are now.

The current path of User Experience design and by association, User Experience Research has been deeply co-opted by Six Sigma principles, by designers and design firms steeped in Bauhaus training, and practitioners required to function within the scope of an engineering industry that prizes mathematics and quantification as their only acceptable metric.

If ethnographers and other qualitative researchers join these companies in a research capacity, we are expected to embrace three 'beliefs': 1 design 'simplicity' and Bauhaus; 2 a Six Sigma method of quantification as the only method for measuring results; and 3 an engineering vision derived from science fiction, which provides a working solution that only requires measurement of effectiveness because it's functionality has been previously demonstrated to 'work' in Science Fiction narratives. Ethnographic praxis does not easily lend itself to any one of these disciplines or methods precisely because people are complex, take agency in their own ways, and are part of a reality that is the now: the nested and interrelated systems in which we live and exist, overlap, fragment, spiral, swirl, and intertwine with each other, or miss and do not overlap at all.

All three of these dominating 'beliefs' from design, manufacturing, and engineering lend themselves easily to forging automated processes, where rules can be put in place and enacted in projects that fulfill the vision of the aspirational future from both design and engineering.

However, the outcomes from these over-focused, rigid, and brittle processes will be vulnerable, as systems become inflexible to change and human agency. We are up against a triad of methodology and beliefs that were created for things, but are now being applied to people. People are creative and prefer to take agency when we are trying to solve problems, cooperate, and engage with the world.

The metrics-based 'automated process' pattern is building a world that will be easy to automate initially, but that may be—and already is in some instances—ineffective and potentially dangerous as brittleness and inflexibility increases, and human agency decreases, limiting our options and choices for problem solving and cooperation.

As EPIC practitioners, we must consider how to offer our tools, knowledge and skills to organizations and industries that value what we have to offer, and that are not yet automated, or which may be but, after many mistakes, now understand the grave nature of getting it wrong.

We will do best in companies who have expanded their belief systems to include us, and our toolkits, in order to create more flexible, adaptive systems, which will preserve and encourage the entire range of human creativity.

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Photo: "Bauhaus" by cdschock CC by 2. Sally A. Spicer Foundation. Sally I agree with your concerns and complaints about reducing research to automated and repeatable methods.

Automation Based Creative Design - Research and Perspectives -

I would disagree with putting design Bauhaus or any other design pedagogy in the same bucket with Six Sigma. You are comparing apples and oranges. However the digital design that you talk about in start-ups or big corporates is mostly led by HCI, IT or MBA folks, none of them have a design or Bauhaus education, even if they are designing or directing the design verticals.

In my experience it is not due to design education but lack thereof. Their hallmark methods such as Six Sigma and Lean are generally used for production, whose primary objective is to be efficient. Design processes are not about efficiencies, they are intuitive and generative. Unfortunately the design practice of physical objects is very different compared to the field of UX where the user experience has been reduced to screen experience, again due to lack of proper design training. The field is also a wild wild west, many professionals come san experience or with very little.

Hence the challenge that you face is also faced by trained designers of physical objects. On the other hand your observation about design, as simplifier of complexity is true, to an extent. A good designer is able to understand the complexity and reach the crux of the issue that needs to be solved. Our research is ongoing, and in , we will release a detailed report. What follows here are four interim findings elaborating on the core insight that the road ahead is less about automating individual jobs wholesale, than it is about automating the activities within occupations and redefining roles and processes.

These preliminary findings are based on data for the US labor market. We structured our analysis around roughly 2, individual work activities, 5 5. Those capabilities range from fine motor skills and navigating in the physical world, to sensing human emotion and producing natural language. The bottom line is that 45 percent of work activities could be automated using already demonstrated technology.

The magnitude of automation potential reflects the speed with which advances in artificial intelligence and its variants, such as machine learning, are challenging our assumptions about what is automatable.

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In many cases, automation technology can already match, or even exceed, the median level of human performance required. According to our analysis, fewer than 5 percent of occupations can be entirely automated using current technology. However, about 60 percent of occupations could have 30 percent or more of their constituent activities automated. In other words, automation is likely to change the vast majority of occupations—at least to some degree—which will necessitate significant job redefinition and a transformation of business processes.

Mortgage-loan officers, for instance, will spend much less time inspecting and processing rote paperwork and more time reviewing exceptions, which will allow them to process more loans and spend more time advising clients. Similarly, in a world where the diagnosis of many health issues could be effectively automated, an emergency room could combine triage and diagnosis and leave doctors to focus on the most acute or unusual cases while improving accuracy for the most common issues.

As roles and processes get redefined, the economic benefits of automation will extend far beyond labor savings. Lawyers are already using text-mining techniques to read through the thousands of documents collected during discovery, and to identify the most relevant ones for deeper review by legal staff. Similarly, sales organizations could use automation to generate leads and identify more likely opportunities for cross-selling and upselling, increasing the time frontline salespeople have for interacting with customers and improving the quality of offers.

Conventional wisdom suggests that low-skill, low-wage activities on the front line are the ones most susceptible to automation. It encompasses not only occupations, work activities, capabilities, and their automatability, but also the wages paid for each occupation.

In addition to analyzing the relationship between automatability and compensation levels, the inclusion of wages allows us to compare the potential costs to implement automation with labor costs, which inherently reflect supply, demand, and elasticity dynamics. Our work to date suggests that a significant percentage of the activities performed by even those in the highest-paid occupations for example, financial planners, physicians, and senior executives can be automated by adapting current technology.

These include analyzing reports and data to inform operational decisions, preparing staff assignments, and reviewing status reports. Conversely, there are many lower-wage occupations such as home health aides, landscapers, and maintenance workers, where only a very small percentage of activities could be automated with technology available today Exhibit 2. Capabilities such as creativity and sensing emotions are core to the human experience and also difficult to automate. The amount of time that workers spend on activities requiring these capabilities, though, appears to be surprisingly low.

Just 4 percent of the work activities across the US economy require creativity at a median human level of performance. Similarly, only 29 percent of work activities require a median human level of performance in sensing emotion. While these findings might be lamented as reflecting the impoverished nature of our work lives, they also suggest the potential to generate a greater amount of meaningful work.

This could occur as automation replaces more routine or repetitive tasks, allowing employees to focus more on tasks that utilize creativity and emotion. These interim findings, emphasizing the clarity brought by looking at automation through the lens of work activities as opposed to jobs, are in no way intended to diminish the pressing challenges and risks that must be understood and managed.

Clearly, organizations and governments will need new ways of mitigating the human costs, including job losses and economic inequality, associated with the dislocation that takes place as companies separate activities that can be automated from the individuals who currently perform them. Other concerns center on privacy, as automation increases the amount of data collected and dispersed. The quality and safety risks arising from automated processes and offerings also are largely undefined, while the legal and regulatory implications could be enormous.

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To take one case: who is responsible if a driverless school bus has an accident? Nor do we yet have a definitive perspective on the likely pace of transformation brought by workplace automation. Critical factors include the speed with which automation technologies are developed, adopted, and adapted, as well as the speed with which organization leaders grapple with the tricky business of redefining processes and roles.

These factors may play out differently across industries. Those where automation is mostly software based can expect to capture value much faster and at a far lower cost.