Jun 2019
The Confluence of Artificial Intelligence (AI) and Design
Technology plays a significant role in shaping the future of design and is changing the way we think about it. Factors such as Artificial Intelligence, Machine Learning, and Deep Learning are shaping the design scope by leveraging and improving user experiences.
Abstract
Technology plays a significant role in shaping the future of design and is changing the way we think about it. Factors such as Artificial Intelligence, Machine Learning, and Deep Learning are shaping the design scope by leveraging and improving user experiences. In the contemporary technological context, the touchpoints a designer needs to consider are growing in complexity. We are living in a time where new principles for human-AI interaction are becoming urgent.
Keywords
Artificial Intelligence, Interaction Design, Human-Computer Interaction, Decision-making
Introduction
New conventions in experience economy alongside new technological developments as AI are shaping how businesses invest in integrating UXD as a crucial part of their whole service design strategy plan.
Artificial Intelligence (AI) , Machine Learning (ML) and Deep Learning (DL) [1] will be the most important means to improve user experience design (UXD) (Yang, 2017). Businesses are chasing AI transformation either to enhance customer experience or to automate businesses and defining a system that extends human capabilities (Daugherty, 2018). To be able to exploit AI potential fully, the advent of AI requires the reimagine of design conventions (Van Allen 2017).
[1] Technically, deep learning is a subset of machine learning as machine learning if from artificial intelligence. However, their capabilities are different, meaning a different impact on how designers may design services for each type of technology. Basic machine learning models do become progressively better at whatever their function is, but they still some guidance. If a machine learning algorithm returns an incorrect prediction, then an engineer needs to step in and make adjustments. However, with deep learning models, the algorithms can determine on their own if a prediction is accurate or not. (Grossfeld, 2017)
There is a growing awareness that algorithmic advances to artificial intelligence and machine learning alone are insufficient when considering systems designed to interact with and around humans. (Riedle, 2019, p.2)
Background
To achieve social responsibility, designers will have to learn how to design AI-driven services that address issues of fairness, accountability, interpretability, and transparency. (Riedl, 2019)
The design field has spread across several domains, and AI is following a similar path, which makes them both ubiquitous in many senses. This fields' ubiquity lines up with the new age of the Ubiquitous Computing Era, where designing requires modifying our current practices by reinforcing between disciplines at every step of the creation process (Thébault, 2011).
Both pursue the goal of enhancing the human experience by extending their capability. It seems natural that both fields are beginning to confluence. Each one gathers data to interpret and predict human behavior and to anticipate what people might do next. However, they understand human significantly different (Girardin, 2017). The synergy between design and fields like AI will be essential to assure the success of the established design and AI knowledge.
Both fields have the power to shape each other. These developments will require the development of multidisciplinary practices among designers, data scientists, and engineers. The confluence of AI and design will exist in a symbiotic relationship with one another (Yang, 2017). Because design needs the context awareness and personalized customization enabled by AI and its subdomains, and AI needs user experience design to be perceivably valuable to the users.
Both fields have the power to shape each other. These developments will require the development of multidisciplinary practices among designers, data scientists, and engineers. The confluence of AI and design will exist in a symbiotic relationship with one another (Yang, 2017). Because design needs the context awareness and personalized customization enabled by AI and its subdomains, and AI needs user experience design to be perceivably valuable to the users.
Problem Awareness
It is no longer enough for UX designers to only improve the experience by paying attention to usability, utility, and interaction aesthetics. Instead, the best user experience may come from services that automatically personalize their offers to the user and context, and systems that leverage a more detailed understanding of people and the world in order to provide new value. (Dove, 2017, p. 278)
As more services are shifting to AI, it becomes clear that designers still have a lot to learn about how to make users feel in control of the technology (Dove, 2017; Schwartz, 2004; Yang, 2018). Because design problems require several knowledge domains and a broad range of skills, design problems are some of the most complex to tackle within AI (Grecu, 1998). Fundamentally, is because design problems and design solutions can be said to co-evolve (Dorst, 2001).
On one hand, as a non-static problem frame, AI in the current state of development, has great difficulties to adapt to the design process contexts. On the other hand, design problems are getting more complicated given the evolving ecosystem that design as created around itself. To be able to understand and master this complexity, designers will have to understand the technologies behind it (Hebron, 2016), with the proviso that understanding AI is not the same as learning it from a technological point of view.
On one hand, as a non-static problem frame, AI in the current state of development, has great difficulties to adapt to the design process contexts. On the other hand, design problems are getting more complicated given the evolving ecosystem that design as created around itself. To be able to understand and master this complexity, designers will have to understand the technologies behind it (Hebron, 2016), with the proviso that understanding AI is not the same as learning it from a technological point of view.
To create systems that successfully use the affordances and constraints of AI, the methodologies for design must themselves be redesigned. (Van Allen, 2017)
Changing the design context will raise complexity in the sense that problem framing is a crucial design practice (Dorst, 2015). Van Bodegraven and Dorst criticized the Human-centered Design (HCD) field by exposing the lack of skills and knowledge required to operate in a strategic innovation on today's context ( Van Bodegraven, 2017). We may consider an evolution on the HCD field by moving into coexistence with the formal method of putting human beings in the center of the design process as well as putting data in the center of the design process.
It will create a hybrid synergy between the two methods in order to achieve an increment on the efficiency of AI-driven services and establishing a new discipline that moves between humans and machines. In a not so distant future, designers will increasingly be designing with data either as a design material or for AI-driven services. These will become embedded within the design process. The center of design is now an organic and unpredictable intelligent evolving system (Dove, 2017; Fischer, 2002; Van Allen, 2017).
The once-static paper page evolved into digital, immaterial content, that is evolving into fluid interactions, often customizable for particular users and purposes. Either as a strategy or as a methodology, AI is shaping the traditional notions of information as something material and static in time and space. The design moved from a traditional static convention to a new living ecosystem [2] . The center of the design is now an organic and unpredictable evolving system. Big Data opened the door for new digital services developed and designed explicitly aiming to evolve and adapt as they learn from their user – behavioral data.
The once-static paper page evolved into digital, immaterial content, that is evolving into fluid interactions, often customizable for particular users and purposes. Either as a strategy or as a methodology, AI is shaping the traditional notions of information as something material and static in time and space. The design moved from a traditional static convention to a new living ecosystem [2] . The center of the design is now an organic and unpredictable evolving system. Big Data opened the door for new digital services developed and designed explicitly aiming to evolve and adapt as they learn from their user – behavioral data.
Behavioral data are fed into the system, and algorithms use statistical properties of this data to generate knowledge" and expose "these services, create new opportunities to design new experiences based on recommendations, predictions or contextualization, are now defining how humans and machines interact.
(Girardin 2017, p. 376)
(Girardin 2017, p. 376)
[2] As an example, Waze combines AI algorithms and real-time data to create living, dynamic, optimized maps that can get people to their destinations as quickly as possible ( Daugherty and Wilson, 2018, p. 6–7).
the Confluence of AI and Design
Big Data alongside the Internet and connected devices (Internet of Things - IoT) provide a whole new ecosystem for communication. They are changing the traditional ways of gathering, presenting, sharing, and using information. Beginning within experience economics, leveraged throughout technology and synthesized by design, the world is moving quickly towards hyper-personalized AI-driven applications. Fundamentally what has been established is that interactions will be ruled by time spans like
Pre-interaction – Interaction – Post-interaction
A possible method to address UXD within AI is by approaching Animistic Design, a method for "fostering effects, sensibilities, and thoughts that capitalize on the uncertain, the unpredictable and the nonlinear, and their capacity to trigger creative pathways" (Marenko 2016, p. 432). This method may become a way to reimagine digital interaction between the human and nonhuman. The method can help the design shifting from crafting task-oriented experiences for users, to building evolving, diverse, autonomous ecologies that support collaborative exploration and creativity for machine and human participants alike.
Another possible method to address the synergy between design and AI is by approaching Anticipatory Design (Lesche, 2017; Shapiro, 2015; Van Allen, 2017). Making decisions requires time and cognitive effort. Anticipatory design focuses on this premise and proposes a design method that personalizes the user flow by making and eliminating user choice with the determination of predicting user experiences. Anticipatory design is a design method that aims to be one step ahead of users by anticipating their behavior and preferences. Our brains can have two basics reactions – spontaneous and event related. The event related responses are triggered either in anticipation (pre-event) or evoked (post-event) (Figure 1).
Pre-interaction – Interaction – Post-interaction
A possible method to address UXD within AI is by approaching Animistic Design, a method for "fostering effects, sensibilities, and thoughts that capitalize on the uncertain, the unpredictable and the nonlinear, and their capacity to trigger creative pathways" (Marenko 2016, p. 432). This method may become a way to reimagine digital interaction between the human and nonhuman. The method can help the design shifting from crafting task-oriented experiences for users, to building evolving, diverse, autonomous ecologies that support collaborative exploration and creativity for machine and human participants alike.
Another possible method to address the synergy between design and AI is by approaching Anticipatory Design (Lesche, 2017; Shapiro, 2015; Van Allen, 2017). Making decisions requires time and cognitive effort. Anticipatory design focuses on this premise and proposes a design method that personalizes the user flow by making and eliminating user choice with the determination of predicting user experiences. Anticipatory design is a design method that aims to be one step ahead of users by anticipating their behavior and preferences. Our brains can have two basics reactions – spontaneous and event related. The event related responses are triggered either in anticipation (pre-event) or evoked (post-event) (Figure 1).
Figure 1: A taxonomy of the brain retrieved from Bozinovski and Bozinovska (Bozinovski, 2003)
Within anticipatory systems, services can be designed to learn what to expect (expectatory). And trigger events to train users for feedback loops to learn from them if they like or not certain levels of automation (preparatory). With this configuration we can support anticipatory design with structure accuracy. The downside of investing in accuracy it will require the user to provide full access to all their data points in their lives. Otherwise, the service (algorithm) will not be able to guarantee full accuracy. This implies that the user is willing to open up everything to technology. At the core of this argument, anticipatory design's proposition is to provide an ecosystem that replaces users' inputs with automated system actions. This method promises to streamline the user process as much as possible. The decisions are sustained choices for the users concerning their interests with the ultimate goal of preventing users from unnecessary decision-making (tasks) and aiming to reduce user cognition overload (stress).
Nevertheless, in the final experience, the users might not even know or notice that the system has done or is making decisions on behalf of them. So, this raises questions on how should the system communicate with the user? And, in which level of feedback the user will be comfortable to delegate to the system and which level he will demand control of the system feedback?
This relatively new field can be the first step to help the designer to design for unpredictable courses (Van Bodegraven, 2017; Lesche, 2017; Shapiro, 2015; Zamenopoulos, 2007). Anticipatory design can be a critical method to support designers in living ecosystems that represent complex trade-offs to them. Since it can converge both user interface (UI) and UX with AI, primarily through ML.
Nevertheless, in the final experience, the users might not even know or notice that the system has done or is making decisions on behalf of them. So, this raises questions on how should the system communicate with the user? And, in which level of feedback the user will be comfortable to delegate to the system and which level he will demand control of the system feedback?
This relatively new field can be the first step to help the designer to design for unpredictable courses (Van Bodegraven, 2017; Lesche, 2017; Shapiro, 2015; Zamenopoulos, 2007). Anticipatory design can be a critical method to support designers in living ecosystems that represent complex trade-offs to them. Since it can converge both user interface (UI) and UX with AI, primarily through ML.
References
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Patrick Hebron. 2016. Machine Learning for Designers (1st ed.). O'Reilly Media, Inc.
Paul Daugherty and James Wilson. 2018. Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press, United States.
Pierrick Thébault, Henri Samier, David Bihanic, Lille Nord De France, and Le Mont-houy. 2011. Designing for the Ubiquitous Computing era : towards the reinvention of everyday objects and the creation of new user experiences. Int. J. Des. Innov. Res. X, (2011), 1–25.
Philip Van Allen. 2017. Reimagining the Goals and Methods of UX for ML/AI A new context requires new approaches. In The AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems, 431– 434.
Stevo Bozinovski and Liljana Bozinovska. 2003. Anticipatory Brain Potentials: An Electrophysiological Insight into the Anticipatory Behavior of Adaptive Learning Systems. In Anticipatory Behavior in Adaptive Learning Systems. https://doi.org/10.1007/b11711
Qian Yang. 2017. The Role of Design in Creating Machine-Learning-Enhanced User Experience. AAAI 2017 Spring Symp. Des. User Exp. Mach. Learn. Syst. Tech. Rep. SS-17-04 (2017), 406–411. Retrieved from http://aaai.org/ocs/index.php/SSS/SSS17/paper/view...
Qian Yang, Nikola Banovic, and John Zimmerman. 2018. Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18, 1–11. https://doi.org/10.1145/3173574.3173704
Theodore Zamenopoulos and Katerina Alexiou. 2007. Towards an anticipatory view of design. Des. Stud. 28, 4 (2007), 411–436. https://doi.org/10.1016/j.destud.2007.04.001
Barry Schwartz. 2004. The Paradox of Choice, Why More Is Less. Retrieved from https://www.researchgate.net/publication/200552951_The_Paradox_of_Choice_Why_More_Is_Less
Betti Marenko and Philip van Allen. 2016. Animistic design: how to reimagine digital interaction between the human and the nonhuman. Digit. Creat. 27, 1 (2016), 52–70. https://doi.org/10.1080/14626268.2016.1145127
Brett Grossfeld. 2017. A simple way to understand machine learning vs deep learning. Retrieved from https://www.zendesk.com/blog/machine-learning-and-...
DL Grecu and DC Brown. 1998. Dimensions of machine learning in design. Artif. Intell. Eng. Des. Anal. Manuf. April (1998), 117–122.
Fabien Girardin and Neal Lathia. 2017. When User Experience Designers Partner with Data Scientists. In Aaai 2017 Spring Symposia, 376–381.
Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017. UX Design Innovation. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems - CHI '17, 278–288. https://doi.org/10.1145/3025453.3025739
Gerhard Fischer. 2002. Beyond "Couch Potatoes": From consumers to designers and active contributors. First Monday 7, 12 (2002). https://doi.org/10.5210/fm.v7i12.1010
Joël Van Bodegraven. 2017. How Anticipatory Design Will Challenge Our Relationship with Technology A Future Without Choice. In The AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems, 435–438.
Kees Dorst. 2015. Frame Creation and Design in the Expanded Field. She Ji J. Des. Econ. Innov. 1, 1 (2015), 22–33. https://doi.org/.1037//0033-2909.I26.1.78
Kees Dorst and Nigel Cross. 2001. Creativity in the Design Process: Co-Evolution of Problem–Solution. Des. Stud. 22, (2001), 425–437. https://doi.org/10.1016/S0142-694X(01)00009-6
Louisa Lesche. 2017. Anticipatory Design. University of Ravensburg-Weingarten. https://doi.org/10.1007/s11165-007-9046-9
Mark O. Riedl. 2019. Human-Centered Artificial Intelligence and Machine Learning. Hum. Behav. Emerg. Technol. Volume 1, Issue 1 (2019), 1–8. Retrieved from http://arxiv.org/abs/1901.11184
Mieke van der Bijl-Brouwer and Kees Dorst. 2017. Advancing the strategic impact of human-centred design. Des. Stud. 53, (2017), 1–23. https://doi.org/10.1016/j.destud.2017.06.003
Patrick Hebron. 2016. Machine Learning for Designers (1st ed.). O'Reilly Media, Inc.
Paul Daugherty and James Wilson. 2018. Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press, United States.
Pierrick Thébault, Henri Samier, David Bihanic, Lille Nord De France, and Le Mont-houy. 2011. Designing for the Ubiquitous Computing era : towards the reinvention of everyday objects and the creation of new user experiences. Int. J. Des. Innov. Res. X, (2011), 1–25.
Philip Van Allen. 2017. Reimagining the Goals and Methods of UX for ML/AI A new context requires new approaches. In The AAAI 2017 Spring Symposium on Designing the User Experience of Machine Learning Systems, 431– 434.
Stevo Bozinovski and Liljana Bozinovska. 2003. Anticipatory Brain Potentials: An Electrophysiological Insight into the Anticipatory Behavior of Adaptive Learning Systems. In Anticipatory Behavior in Adaptive Learning Systems. https://doi.org/10.1007/b11711
Qian Yang. 2017. The Role of Design in Creating Machine-Learning-Enhanced User Experience. AAAI 2017 Spring Symp. Des. User Exp. Mach. Learn. Syst. Tech. Rep. SS-17-04 (2017), 406–411. Retrieved from http://aaai.org/ocs/index.php/SSS/SSS17/paper/view...
Qian Yang, Nikola Banovic, and John Zimmerman. 2018. Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI '18, 1–11. https://doi.org/10.1145/3173574.3173704
Theodore Zamenopoulos and Katerina Alexiou. 2007. Towards an anticipatory view of design. Des. Stud. 28, 4 (2007), 411–436. https://doi.org/10.1016/j.destud.2007.04.001
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