Without tensions no glory.
“AI research has now been around for about 65 years, and the consequences of design decisions on AI outcomes have been a lively debate for 20-plus years, if not longer. Governments, companies, and investors are now pouring in copious resources to advance AI techniques and create ‘AI-powered’ products. Amid the hype, however, people question whether breakthroughs are reproducible and transferable to practice, and who benefits from them. Keeping up with the latest trends has become increasingly challenging, even for the experienced. And the definition of accepted terminology itself is ever changing. As we – HCI researchers and UX practitioners – struggle to keep up with where the field is going, it is easy to lose sight of its past, repeat mistakes, and stumble on unintended consequences.”
Henriette Cramer and Juho ~ ACM Interactions Volume XXVI.6 ★
Taking the human perspective in all technology achievements.
“Our people-centered design principles support the goal of providing and informing with data to allow people more opportunities in their work. In our experience, there are three key principles organizations need to hold up as pillars for any AI implementation: transparency, explainability, and reversibility. (…) There are three methods that companies can take to put these principles into action in their AI projects. These methods aim to reduce the risk of introducing poorly tuned AI systems and inaccurate or biased decision-making in pilots and implementations.”
David A. Bray et al. ~ MIT Sloan Management Review ★
The big four going UCD and tech.
“Artificial intelligence technology can result in artificial stupidity if it’s poorly designed, implemented, or adapted. What’s crucial? Ensuring it’s designed to help humans think better.”
Jim Guszcza ~ Deloitte Insights ★
Always keep your principles.
“Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts.”
Luciano Floridi and Josh Cowls ~ Harvard Data Science Review Issue 1 ★
Become more strategic, ‘creative’ and human, as we always should have been.
“The word automation conjures an image of a factory full of robots, a modern marvel symbolizing both technological progress and the regression of working-class opportunities and lifestyles. But our notion of automation generally remains ossified in this physical, machine-replaces-labor frame. We don’t think of automation in the realm of knowledge work beyond the most mundane and mindlessly repeatable tasks. But automation, powered by machine-learning advances in artificial intelligence (AI), is coming. It’s actually already been here for decades, going back to relatively primitive software innovations that eluded our ability to connect the dots back to industrial robotics before it. Perhaps surprisingly, modern AI automation has been making original art for years and has collaborated with a human team on an original painting that sold at Christie’s for $432,500. Beyond art making, AI automation can also write procedural content such as stock blurbs and minor league sports stories.”
Dirk Knemeyer a.k.a. /knemeyer | @dknemeyer and Jonathan Follett a.k.a. /jonfollett | @jonfollett ~ ACM Interactions (XXVI.3) ★
UX designers have to become computational thinkers as well.
“UX designers have years of experience in creating the best design elements, and most of the time the results of which carries a UX designer to be largely positive in terms of increased interaction and achieving the bottom line. However, there is a gap between the positive change brought by UX designers and what should be the utopian final script interaction. The results may be better, but the UX design in this world cannot guarantee that every user will like everything on the website or application. There will always be some people who adore in other parts of the conversion path with a focus on UX. The main reason for this is not enough customization in the UX design to optimize the interests of each user separately. Each user is different and needs a different treatment. UX design works on a global level but there is still a gap and potential that can be achieved and brands help to invest more in significant UX design.”
Melissa Crooks a.k.a. /msmelissacrooks ~ home toys ★
AI is eating the HCI world.
“There has been a revolution, but it snuck up on us so gradually that you’d be forgiven if you missed it. It’s called artificial intelligence, and it will have a profound impact on how we design digital products in the near future.”
Lars Holmquist ~ Interactions XXIV.4 ★
HCI as an academic field is waking up, too.
“A potential revolution is happening in front of our eyes. For decades, researchers and practitioners in human-computer interaction (HCI) have been improving their skills in designing for graphical user interfaces. Now things may take an unexpected turn—toward natural language user interfaces, in which interaction with digital systems happens not through scrolling, swiping, or button clicks, but rather through strings of text in natural language. This is particularly visible in recent developments in chatbots, that is, machine agents serving as natural language user interfaces to data and service providers , typically in the context of messaging applications.”
Asbjørn Følstad and Petter Bae Brandtzæg ~ Interactions XXIV.4 ★
Important topic addressed amongst digital designers.
“Over the next two decades, connected products will demand an unprecedented amount of user trust. Technologists and designers will ask the public for yet more of their attention, more of their data, more of their lives. AIs will know users’ deepest secrets. Co-operating devices will automate security and safety. Autonomous vehicles will even make life-or-death decisions for passengers. But ours is an industry still unwilling to grapple with the ethical, social, and political angles of this future. We mistakenly believe that technology is neutral; that mere objects cannot have moral relevance. And so we make embarrassing blunders – racist chatbots, manipulative research, privacy violations – that undermine trust and harm those we should help.”
Cennydd Bowles a.k.a. /cennydd | @cennydd ~ interaction17 videos
Moving ‘Lick’ forward into the design world.
“As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. To have your say about how best to use it, you need a good understanding about its applications and related design patterns. This article illustrates the power of machine learning through the applications of detection, prediction and generation. It gives six reasons why machine learning makes products and services better and introduces four design patterns relevant to such applications. To help you get started, I have included two non-technical questions that will help with assessing whether your task is ready to be learned by a machine.”
Lassi Liikkanen a.k.a. /lassial | @lassial ~ Smashing Magazine ★
Innovation always happens at the edges.
“Research papers from the AAAI User Experience of Machine Learning Symposium ~ Consumer-facing predictive systems paint a seductive picture: espresso machines that start brewing just as you think it’s a good time for coffee; office lights that dim when it’s sunny and office workers don’t need them; just in time diaper delivery. The value proposition is of a better user experience, but how will that experience actually be delivered when the systems involved regularly behave in unpredictable, often inscrutable, ways? Past machine learning systems in predictive maintenance and finance were designed by and for specialists, while recommender systems suggested, but rarely acted autonomously. Semi-autonomous machine learning-driven predictive systems are now in consumer-facing domains from smart homes to self-driving vehicles. Such systems aim to do everything from keeping plants healthy and homes safe to “nudging” people to change their behavior. However, despite all the promise of a better user experience there’s been little formal discussion about how design of such learning, adaptive, predictive systems will actually deliver. This symposium aims to bridge the worlds of user experience design, service design, HCI, HRI and AI to discuss common challenges, identify key constituencies, and compare approaches to designing such systems.”
Mike Kuniavsky a.k.a. @mikekuniavsky, Elizabeth Churchill a.k.a. @xeeliz, and Molly Wright Steenson a.k.a. @maximolly
AI is eating the UX world for breakfast.
“It’s a glimpse of the kind of personalization of language that could transform UX over the next few years, as AI becomes an integral part of research and design.”
Katharine Schwab a.k.a. /katharineschwab | @kschwabable ~ FastCo.design ★
A little more on ethics would help.
“We’re on the cusp of a new era of design. Beyond the two-dimensional focus on graphics and the three-dimensional focus on products, we’re now in an era where designers are increasingly focusing on time and space, guided by technological advances in artificial intelligence, robotics, and smart environments.”
Katharine Schwab a.k.a. /katharineschwab | @kschwabable ~ FastCoDesign ★
We’re getting some clear messages on this topic lately.
“As I began to explore how AI would affect design, I started wondering what advice I would give my daughter and a generation of future designers to help them not only be relevant, but thrive in the future AI world. Here is what I think they should expect and be prepared for in 2025.”
Rob Girling ~ O’Reilly Radar ★
Algos as the augmentation tools for designers.
“I’ve been following the idea of algorithm-driven design for several years now and have collected some practical examples. The tools of the approach can help us to construct a UI, prepare assets and content, and personalize the user experience. The information, though, has always been scarce and hasn’t been systematic. However, in 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks and artificial intelligence. Now is the time to rethink the modern role of the designer.”
Yury Vetrov a.k.a. @jvetrau ~ Smashing Magazine ★
We still don’t what hit us designers.
“Computers can search through immense solution spaces for the ideal design; we might someday talk about ‘discovering a design’ through the joint efforts of human and computer neurons, rather than ‘creating a design’.”
Jon Bruner /brunerjon | @JonBruner ~ O’Reilly Radar ★
After digital disruption we’re now moving into computational disruption.
“Artificial Intelligence promises everything from self-driving cars to self-writing newspapers, but AI may be missing its greatest opportunity in healthcare, where AI-driven ‘conversational interfaces’ hold untapped potential to influence the health and wellbeing of billions of people.”
Thomas Sutton a.k.a. /thomasthinks | @thomas_thinks ~ frog Designmind ★
The blend of creativity, design, and deep understanding of digital technology.
“A primary goal of this research project was to find a set of guiding principles, metaphors and ideas, that inform the development of future theories, experiments, and applications. By combining different domains into one narrative, we formulate a new school, or praxis for creativity: CreativeAI. Its desire is to explore and celebrate creativity. Its goal is to develop systems that raise the human potential. Its belief is that addressing the “what” and “why” is as important as the “how”. Its conviction is that complex ethical questions are not an afterthought, but an opportunity to be creative collectively. Finally, CreativeAI is a question, rather than an answer. Its only demand is more collaboration and creativity. It is an invitation for play!”
Samim Winiger a.k.a. /samimwiniger | @samim & Roelof Pieters a.k.a /roelofpieters | @graphific ~ Medium ~ courtesy of karsalfrink
Get used to it.
“For many years, interacting with artificial intelligence has been the stuff of science fiction and academic projects, but as smart systems take over more and more responsibilities, replace jobs, and become involved with complex emotionally charged decisions, figuring out how to collaborate with these systems has become a pragmatic problem that needs pragmatic solutions. Machine learning and cognitive systems are now a major part many products people interact with every day, but to fully exploit the potential of artificial intelligence, people need much richer ways of communicating with the systems they use. The role of designers is to figure out how to build collaborative relationships between people and machines that help smart systems enhance human creativity and agency rather than simply replacing them.”
(Patrick Mankins a.k.a. @patrickmankins ~ FastCo Design) ★
The new era of design for smartness is on the horizon.
“The addition of sensing and connectivity to products is rapidly changing what we learn from them, how we perceive them, and how we use them. Those same technologies are also feeding backwards, changing how we design products. (…) Wireless sensors and fast processors are popping up everywhere, allowing us to generate volumes of real-time data about human behavior and our world. At frog we define sensing as the ability to harness these real-time data streams to identify patterns, generate insights, and design better experiences for people. As engineers crack the technical challenges, from ultra-cheap sensors to exabyte-scale data processing, designers must discover how we can adapt these technologies to human life.”
(Tue Haste Andersen & Simone Rebaudengo ~ frog DesignMind) ★