AI can reproduce Mona Lisa in the styles of Picasso, van Gogh, and Monet. Source: Gene Kogan

Can an A.I. System be a Designer? (Part 3)

How A.I. informs design, and vice-versa

Previous: Can an A.I. system be a designer? (Part 2)

Introduction

This series has addressed a variety of topics all surrounding the subject of artificial intelligence (A.I.) in the field of Design. The first article explored the question in the title- “Can an A.I. System be a Designer”, paying close attention to the topics of creativity and empathy. The second article closely explored modern machine learning and how it has contributed to more powerful A.I. systems. This article is going to explore how the Design industry influences the applications of A.I., and vice-versa. It is also going to go into more detail about how and why designers are using artificial intelligence to improve new and existing systems. This article will look at successes and failures in the A.I. space, as well as the current state of the A.I. industry.

Sensationalism — Get used to it.

Sensationalist news articles about A.I. are sprouting up everywhere claiming to do X, Y, and Z faster than other A.I. Systems- let alone humans. This anxiety-provoking material is typically blown out of proportion- and though there’s something to be said for exponential growth, A.I. has existed for over 30 years. There are more A.I. startups today than ever, all trying to develop that magical system of algorithms that will solve all of the world’s problems.

However, it is important address the consequences of this technology- especially the major consequence of technological unemployment- when disruptive technology swallows up a job market and leaves people without jobs. Capitalistic economies typically reward innovation (e.g. artificial intelligence/robotics) but don’t update laws to reflect the implications of innovation- e.g. Power Law dynamics in industries. This has attributed to the massive reduction in the middle class in the US.

Some have addressed “generative design” systems as a catalyst that could potentially wipe out entire creative industries if popularized (talk about sensationalism). If you practice divergent/convergent design, then you are most likely familiar with the three major types of research- exploratory, evaluative and generative. “Generative A.I.” implies that an artificial intelligence system would output solutions. Already, Generative A.I. has proven itself in both research and in commercial applications across the following domains: Generative Graphics, Photos, Audio, Visual, Text, Code, Materials, etc. (Kobielus, 2017) The point is that the innovation and disruption in the M.L./A.I. market is not going anywhere- but it’s important for designers to examine how A.I. works, why and when it should be used, as well as it’s flaws. Check out how OpenAI has developed Generative Models: https://blog.openai.com/generative-models/

Data— The lifeblood of A.I.

I was recently listening to a Podcast about privacy in the US. Nick Merrill was talking about how he received a National Security Letter from the government which essentially allowed them to track his every move. He was put under a gag order where he could not speak to anyone about the letter. So, what did he do? He did what he called “self surveillance” and reported even the most minuscule and irrelevant things on his Facebook feed, wherever, essentially corrupting the validity of the data the government was working with. Call it machine error.

Image Courtesy: Whatsthebigdata

The fallacy about A.I. working as an independent generative solution is that just because data is available does not mean that it is valid or reliable. Otherwise, wouldn’t U.X. designers (or even Psychologists) just stick to online questionnaires and surveys to back their research and give up on methods like carefully formulated experiments or contextual analyses all together? Semantic technology (as well as the recent abundance of data) which has allowed the manipulation of implicit, explicit and tacit data is what has given A.I. it’s recent burst in power. The point is: A.I. functions off of data, and quantitative data cannot be the sole thing informing design. Even if semantic technology allows AI to turn data into meaningful insights- it’s completely irresponsible to rely solely on that output as a solution.

Designing with A.I. comes with it’s own set of ethical issues. In the right hands, it is a powerful tool. In the wrong hands (which brings me back to A.I. for generative design) it can lead to disastrous user experiences. Not to throw them under the bus, but Facebook’s News Feed dilemma caused a huge ruckus due to unsupervised A.I. Remember the Twitter bot that communicated based on the genetic makeup of Twitter? Yea. Let’s not let A.I. generate global solutions based off of big qualitative data.

TayTweets was an A.I. introduced to behave based on Twitter big data using machine learning.

Let’s consider both of these to be early lessons for the repercussions of designing with artificial intelligence. In the last article I identified Google’s heuristics for “human-centered machine learning”. In this article, I’ll address Microsoft’s: Fairness, Accountability, Transparency and Ethics. Check them out here: https://www.microsoft.com/en-us/ai/our-approach-to-ai

Designing with AI — not against it

There is already technology that adapts to the “way we want it” by using A.I. These are experiences that bend to the way our data makes it seem like we want them. They are adaptive. It’s what makes the advertisements on our Facebook wall catered towards us- this system is learning from the data in our browsing history. It defines our YouTube ads. It makes websites look and act differently depending on X, Y, and Z data sets. Consider it to be age-responsive design on steroids. This technology I am referring to would allow you to provide personal inputs into a system and then the system would adapt to your preferences.

Now blow that up on a massive scale. Imagine, walking into a room and having it shift to your preferences based on your personal inventory of data. I’m sure this wouldn’t be hard to imagine in something like virtual reality. Imagine a television that provided only the shows you wanted, for cheap, without advertisements. Imagine a personal assistant who knew exactly what lights to turn on in your house and how dim they should be at 7:30 AM.

Abstract Landscape by Szecsődi Bálint using displacement modifiers in Blender

What I am talking about here is how A.I. can be incorporated into designs to personalize the user experience using data that is voluntarily provided- and this is only one benefit of A.I. in the tool set of designers. It takes UX designers to make sure that people actually want this technology (e.g. that it’s worth the investment) and that the technology is working properly once implemented.

Check out how Google is doing this: https://design.google/library/ux-ai/

Finally, not every situation calls for artificial intelligence. It’s not a one-size-fits all. For some clients, we’ve used A.I. simply to provide things like smart weather updates based on the users location data if it helps them reach their goals more efficiently. Facebook has built their recommendation engine off of A.I. that has helped improve the social experience of sharing places to visit.

Conclusion

I hope that this article provided insights into why an A.I. is only as good as the data that it responds to and works with, and that it is up to designers to judge the reliability and implications of the data. Moreover, it is a great responsibility for designers to make sure that A.I. is guided ethically and responsibly. I have no doubts that A.I. can help mold individual experiences on greater and great scales but without human guidance this technology is as only as good as the data provided to it.

Will Robots Replace Designers? No. It’s more like an exoskeleton for designers. Algorithm-driven design tools can help us to construct a UI, prepare assets and content, and personalize the user experience. https://algorithms.design/

1000 years in the future we may have generative A.I. that are able to design with robust reasoning techniques, with constraints, and lessons in mind, for the benefit of humanity- but for now, we can enjoy the slow steps of designing more engaging experiences with artificial intelligence. There’s no need to replace the role of the designer, especially when there are so many major problems in the world that need to be solved. Let’s not create more problems. There’s no rush.

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