Don Norman

Well, people sometimes ask me what's the secret of my success – and the real answer is, I don't know. I have a strange background because I started off in love with technology. I was in love with radios at first because radios were the first  thing I ever encountered where I couldn't see how it worked. Mechanical devices you can sort of  see how it works; you can take it apart; you can turn here and see it turns there;

push here, you can see it lifts there. Radios – it's all invisible. So, I got really interested in this invisibility of electrons and electromagnetic waves. And so, I decided to go get an engineering degree so I could work on electrical engineering. And so, I went to MIT and got a degree in Electrical Engineering at MIT. And then, I thought I would go to industry, but for complex reasons,

I ended up going to graduate school at the University of Pennsylvania where again I was learning more about electrical engineering. But at that point computers were just starting to exist. There were only a couple. We had one at the University of Pennsylvania. In fact, I was really interested in computers, and the University of Pennsylvania was where the first digital computers were invented  in the United States, called the EDVAC and the ENIAC.

And so, I went there, but none of the  people who did the computers were there anymore. So, I didn't know what to do. I finished a Master's Degree in Electrical Engineering. But what I really wanted to do was computers, and they didn't have a  program for computers. "Wait," they said, "we're going to start one. In a couple of years, we're going to start one, and you can be the first graduate student." But I didn't want to wait. And just then  the Psychology Department started up. Well, actually it had been in existence for a long time, but they got a new chair,

and the new chair was a physicist and they were hiring new people like people who were mathematicians. And I talked to the chair, who said I didn't know anything about psychology. "Wonderful!" he said. "Come into the Department." So, I went to the department and I was  immediately told I didn't know enough mathematics.  I had six years of engineering mathematics,  more than anybody, any of the other students, but nope – I had to learn more math because this  was approaching things differently.

The field of psychology in those days was behaviorism; it was all people who denied that anything went on inside   the head or denied that that mattered at all. What they cared about was what the behavior was. And I said "No. I want to understand the *mechanisms*." And so, I helped develop a field eventually. I started off studying the things I was really  good at, which was the sensory system. I did my thesis on acoustics, psychoacoustics, on how  we hear sounds.

And my first job was at Harvard. So, I did lead this sort of privileged life. I somehow  or other got into MIT and then Penn. And then at Penn, when I graduated my advisor simply said, "So, where do you want to go now?" And we talked about schools, and we decided it should either be MIT or Harvard. And so, he got me appointments. I went to both places and decided uh, MIT I know already;  I'll go to Harvard. It doesn't work that way today.

At Harvard, I entered something called the  Center for Cognitive Studies, which was really interesting. I had never heard the word "cognitive" – I didn't know what it was about. And it was at Harvard that I first started to learn real psychology, and I discovered the British psychologists who were doing really interesting things, looking at mechanisms. And I was in the Center for Cognitive Studies. That was really wonderful and exciting, run by two people called Jerry Bruner and George Miller, two very, very well-known psychologists at the time.

But I was appointed to be an instructor in psychology,  and in my first department meeting George Miller introduced me to the faculty and B. F. Skinner, the world's most famous psychologist at the time, stood up and denounced me and denounced the work  I was doing. So, I've learned that's a good thing! If you want to do really important work, you're  going to make a lot of people unhappy. If you want to change the way people are doing things,  people will be *against* you.

In fact, if people are all supporting you and saying "That's wonderful!", you're doing the *wrong* thing. You're doing what everybody else is doing, and that's not the way to change the world. So, I wanted to change the world. I wanted to bring all the mechanisms I'd learned about as an engineer into the field of psychology. And so, I started studying *memory* and did a lot of work in  memory while I was at Harvard, trying to understand how the memory systems worked. And memory was almost never studied. It was really amazing.

And then, just then the University of California,  San Diego opened up, and so I was hired there. So, I was the second batch of people in the Psychology  Department. No student had graduated from the university yet. It was a small, isolated campus in  the northern part of San Diego. Today, it's a world-famous university, one of the top in the world. But at that time nobody had ever heard of it. But it was interesting because  they started by hiring Nobel Prize laureates.

And then they went down this – very senior  professors and then more junior professors and  post-doctoral fellows and graduate students  and eventually undergraduates. By coming from the top down, they got some really good people. So, I started with a friend, Peter Lindsay. We decided we wanted to *change* psychology. And we wrote a book called "Human Information Processing". And we taught a course in basic to freshman  about thinking about psychology,

– what is the mechanism that makes all this work? And that's a field that eventually became Cognitive Psychology and then Cognitive Science because I  thought psychology was too narrow and too limited and I wanted that students ought to learn about  artificial intelligence and computer science and   neuroscience and linguistics and philosophy  and the social sciences. And so, we started the Cognitive Science Department, which was a combination of everything.

Now, along the way we had a major problem with one of our nuclear  power reactors. It's called Three Mile Island; that's the location; it was on the river and this is the island – it's three miles below the starting point. And that's where the nuclear reactor was, and basically it destroyed itself. And I was called up by a committee from the  Nuclear Regulatory Commission

to be on this panel to go in and understand why the operators made their errors that made the problem even worse. And we went in and we studied it, and the people I was with were human factors experts. And I thought they were excellent. And what we decided was those operators were very smart, they made very sensible decisions, but if you wanted to design a plant that *caused errors*, you couldn't have done a better job than they did.

So, it was the *design* that was the problem. And that made me say, "Oh, I'm a psychologist, so I understand people. And I'm an engineer so I understand technology. *That's* where I ought to be – I ought to be trying to  understand how to design technology that makes it better for people." And so, I started doing that, and I started working with NASA on aviation safety. And then, as computers came out we developed a research group that started to work on

making computers more understandable, easier to use, easier to understand. Some of the fundamental principles we developed are the principles that in the early Apple computers – the Human Interface Guidelines book that Apple developed, which has been wonderful, was done by people at Apple, but some of them were my students and following a lot of the work that we'd developed at San Diego. And, in fact, I took a year's sabbatical.

I went to England, and I was in England – Cambridge, England – and I discovered that I couldn't  even open the doors. The doors – I would pull and they had to be pushed, or I would push and they had to be lifted, or I would lift and they had to be slided to the right. And it was crazy! Why was it so difficult to use everyday things? And so, I realized that the principles that we'd been  studying for nuclear power reactors or complex  

airplanes – aviation safety – and, for that  matter, the new computers applied even to   doors and light switches and water faucets,  or water taps as they call them in Britain. And so, I wrote a book called "The Psychology  of Everyday Things" and that was my beginning. And, actually, I didn't even know there  was a design profession that existed. And I was writing the book where I met a few  real designers who told me, "Hey, you know, you're pretty naive about what's going on here."

And I therefore fortunately discovered that *before* we published the book, so I modified the book. But that also got me more interested in doing things that made a difference to the world that people would *use*, and so I took an early retirement from the university – so, I retired from the university – my first of five retirements. I retired in 1993. And I went to Apple, where I became  first an Apple Fellow, which meant "Hey! I had no responsibilities. I could do anything  that I wanted!"

And I thought that Apple had really great, fantastic stuff but that it was suffering from problems. And I said – I gathered a couple of people around  me and said, "Let's try to fix these problems!" And so, we invented the term *user experience* and I called myself the User Experience Architect of Apple. And this user experience group went about  trying to make sure that Apple maintained

the high quality of usability and understandability in its  products. And that's when I first started to meet real designers, the designers who were working – they were all industrial designers – in the Design Group at Apple. So, that's kind of how I got here. So, why am I so well-known? Well, there's a couple of reasons. I think one of them is  because I speak in *everyday language*. I did a lot of work with one of my colleagues, a professor

in the Psychology Department at UC San Diego. And we published a lot of papers together. He was a really  deep thinker, really intelligent, did tremendous groundbreaking work. And he complained a bit about my writing because when we wrote together he said, "The problem with Don's writing is he writes so clearly that you can tell that you're wrong." It's amazing how many academics like the  complex, convoluted style of reading

that's really hard to understand because – also – if it's really hard to understand, this must be very profound, right? And so, people don't even notice  that what they're talking about is pure nonsense. Well, I have trouble understanding  a lot of this stuff and I struggle with it. And when I finally understand it, then  I can write it and because I know the   problems that I had in trying to understand it,  so I write it in clear, understandable language.

That is surprisingly unusual in the scientific  literature or the design literature. And the second thing is I do try to take a *systems point  of view* – I try to take a *broader* point of view to try to understand the important implications of  what I'm doing, and that is surprisingly rare. And the other important thing which I think is  very important, which I recommend highly to you,

is to *live a long life*. In fact, when I went to Harvard there was a   faculty member there, a very famous person in  sensory psychology – S. S. Stevens – and he said: In science, you're always fighting with your  competitors – that's the way science works because we learn to – when we read a new paper, we immediately look to see where the flaws are,

and we want to point them out to  other people, and that's important, actually, because this back and forth is how  science gets better and better and better.  But, therefore, you always have opponents. And he said, "The way that you succeed   over your opponents is to outlive them." And he was right because a lot of the opponents that he had died, and so he was sort of victorious. And then, when he died – today only the specialists in the field

– of a few fields in sensory psychology remember him because, well, when you die you get forgotten. So, I highly recommend you live a  long life and I've managed to do that quite well.

The approach that I apply to design has  evolved over a considerable amount of time. And the name that I use to  describe it has also been evolving. But the reason for that is because I'm also  changing what it is that we approach as designers. So, in the very beginning, when I first started becoming a designer – which is the 1980s – I was concerned about the early adoption of computer systems

which were really almost impossible for anybody to understand; even the experts who designed them were making errors in using them. And there's a famous case where the early Unix  systems had a text editor that was called "Ed" – for "Editor". You could type away and type your  program or your text, whatever you're doing, and spend several hours typing it, and you have this wonderful document. And then you — "Ah, I'm finished!"; then you turn off the machine and go home.

And you come back the next morning to continue and... It wasn't there. Well, why wasn't it there? Because you didn't *save* it. And, well, you mean... The system doesn't bother to tell you  when you try to turn it off that "Hey, do you want to save the information?" It was little things like that that were so frustrating. In the early days, what we did is we tried to study  the people who used these complex systems. And it was not just computer systems. I actually started off studying

nuclear power systems – some of the nuclear power accidents where the control rooms  were so badly designed that if you wanted to cause an error, you could not have done a better job  in designing something to cause errors. And then *aviation safety* where lives were at stake – many lives were at stake. And there were a huge amount of research and work done, and that was a really good  place to work. I worked with the American National Aeronautics and Space Administration – NASA. Most people think of NASA as shooting rockets up into space,

but they forget the first two letters – "NA" – are "aeronautics". And so, NASA is the world's leader often in aviation safety. And that's where I started. So, we were looking, though, at the *users* of these systems, and so we called them "users". And I was at the University of California, San Diego at the time. And my research group, Stephen Draper and I edited a book that we called "User Centered System Design".

And, of course, the initials of "User Centered System Design" is the name of my university: UCSD. But we emphasized, first of all,  focusing on the users – the people –  and, second of all, that it was a system. The system stuff, though, kind of got dropped. And we decided we didn't really like the focus on users. Why are we calling you "a user"? You're a *person*. Why don't we call you "a person"?

And so, we started to call it *human-centered design*. And many people ask me what the difference is between user-centered design and people-centered design. And all I can say is it's the same  thing – it's just a little bit later in time. Well human-centered design – and sometimes I even call it "people-centered design"   – because I don't call you "a human"; I call you "a person" – has evolved to having four major principles – namely, - Focus on the people. - Make sure you solve the right underlying problem.

- Think of everything as a system. And - Do iterative work. Because we're never going to get it right;  people are far more complex, and societies are even more complex than individual people. So, we have to experiment, do a test, modify, and continually improve. So, that's the basis of all of these. Now, I've come to start the term *humanity-centered design*.

And why is that? Well, focusing upon the individuals is often not quite right. You have to focus upon the groups of people, the societal issues. You have to focus upon a larger set of things. When you do a system analysis, that's what happens. And so, that's why I've decided I  will start calling a lot of what I do *humanity-centered design*. But I still consider all of these to be one similar approach.

And, in fact, some people just call it "design thinking". Design thinking has other attributes. For example, when I say we have four major principles, I don't say anything about how we actually get  the clever new idea that's the breakthrough. How do I actually do the thinking and the creative work? And so, a lot of the emphasis in the design thinking world are the techniques and methods that  we use to come up with novel, important

and robust and doable solutions. So, but all of these have to come together. So, in the design world, we must have many, many different methods and many different techniques and many different approaches. But what unites all of us in the field that I am talking about is the focus upon people, society, humanity.

As designers move into  these very large societal problems, why do we need designers? What is the role of designers? First of all, we know that current-day designers are not appropriate for these large projects. I mean, yes, their talents are badly needed, but they need to add to their talents to have ways of management because the biggest problems are going to be that

of management, of synchronizing the work of all of the different disciplines that have to come together. There are many other  disciplines that also say that they do designs. For example, engineering – engineers do design as civil engineers, mechanical engineers. They, in fact, have design courses; they train people to do design. Computer science – they say that we do design. And they're right. And we actually need these disciplines because they do a different kind of design; they do what I call *engineering design*.

We need that. We need to have things that work well, that don't collapse, that are safe to use, but we have a different emphasis. Our emphasis is *designing for people*. So, we understand people, and we make sure that things are appropriate for people, and that's what's unique. If we now look at the large societal projects, well, there are other areas too that work in that. So, project management is really good at making sure that everything is synchronized and done in time. There's a huge number of issues that have to be solved, and they are experts at this.

We need to partner with them. There are other disciplines, too, like public health. That's a very important discipline. All sorts of other disciplines are going to be necessary for these projects, and why design again? Because design focuses upon the needs and experiences of the people for whom this is being designed and moreover  will insist that we are designing *by the people*, not by us experts trying to tell the people  what they need,

but making sure that the people have significant and major say in how this is being done. That's not easy – very, very difficult. But that's the role that we can play.

There's one interesting result  that we have adopted in our work. It comes from a political scientist  who wrote this wonderful paper called   "Muddling Through". And the notion was that when you  have these big problems that require big solutions, you cannot just go in and try the big solution,

because it's too expensive, takes too much time, disrupts too many people's lives. So, what muddling through is about – the technical term really is not "muddling through"; it's *incrementalism*. And what you do is you say, "OK, this is a big problem. I see all the different things that we have to work on to solve it. It's going to take a really long time, so... let me just wait until I see an opportunity to do *something small*."

And you wait until the time is right for a very small intervention, a very small thing that you might do. Maybe we're talking about getting clean water; so, maybe I say, "One of the problems is access to wells – good wells that are deep enough to be clean. And we could also put filters in. And the wells? Well, we could build wells. But how do we do the pumps? Well, there are some interesting techniques for doing pumps that are operated by people. So, let's see if that would work. So, you wait until there's an opportunity.

And then you do a very small test in a small part of the community. And if that's successful, then it allows you to do a second one and a third one and a fourth one. If it isn't successful, well, you *learn* from it, and it's small enough that people don't get all upset, saying, "Oh, we've wasted a lot of money!" No. We don't call them success or  failures; we call them *learning experiences* because they're small enough that people could understand  that you've learned from that and can now move on,

make it better and apply other work. And so, this muddling through of doing step by step by step, and it's useful, though; you know what the final goal should be, so you make sure that the steps are all going in the right direction. But there are a few other benefits. First of all, you're doing small steps, which are more *likely* to succeed and more likely to get the community to say: "Yes, this is useful. This is what we've been trying to do ourselves, and so, yeah we support it."

Second, as you accumulate them, you get more and more community support behind what you're doing. *And*, because it's going  to take a long time to solve the whole problem, when you try to do the solution all at once and then it takes 10 or more years, you know, the world *changes a lot* in 10 years. And so, the solution you thought of in the beginning may not be the right solution. And, in fact, the *problem* may have changed so that you're now solving the wrong problem, and it's too big a task force to change.

When you do small incremental steps, then you can keep modifying it as the world changes, as the issues change, as the technologies that you might have available change, and as even the culture changes, so people  are more willing to do new or different things, you can *modify* the approach and keep up with all those changes. So, we're a big fan of incrementalism. Don't try to tackle the whole problem.

Find the community leaders. Work with them. And then *move slowly* in small steps. That's what we call "muddling through".

AI can hallucinate and its behavior cannot be predicted most of the time. We can't anticipate what the user will be presented with as we can in a traditional interaction system where we design the interactions and the messaging step by step conventional user experiences are controllable. We understand and decide what happens next.

With AI, we have to accommodate surprises and equally important, communicate the risk for upfront setting the right expectations about performance, indicating whether the product learns over time, clarifying that mistakes will happen and that user input will teach the product to perform better and so on. Don't omit this transparent initial communication as AI systems operate with uncertainty, and if your users expect deterministic

behavior from a probabilistic system, their experience will be degraded. Another major problem is trust and transparency. People have a hard time trusting objects that feel magic. We can't trust what we can't understand. AI systems are not transparent to us for multiple reasons. For once, most of us lack the technical knowledge to actually understand what goes on under the hood. Second, and more importantly, many times with generative AI,

we have no idea where the information is coming from. Tools like GPT and Bard have the power of constructing answers that feel legitimate and sound very pertinent, but can be entirely made up and inaccurate. AI doesn't tell us how they've constructed an answer which would help. And maybe as designers, we should push for this transparency in generative AI, exposing a high level thinking process that gives us the information sources

and general reasoning that's behind an answer. Another reason that adds to the mistrust is how AI is communicated in the media. A Google algorithm that classifies people of color as gorillas, a Microsoft chatbot that decides to become a white supremacist in less than a day, a Tesla car operating in autopilot mode that resulted in a fatal accident. We've seen these isolated but terrible experiences. Sometimes the AI systems are described as black boxes,

so maybe the solution is opening them up. Companies such as Google, Airbnb and Twitter already released transparency reports about government requests and surveillance disclosures. A similar practice for AI Systems could help people have a better understanding of how algorithmic decisions are made. The last problem I want to mention is ownership and intellectual property. This, for me is a fascinating, a very important debate.

I want to start by saying that with so much AI generated art, what we'll all witness is a significant shift in what we value as a society. And to illustrate this argument, I want to give the example of Mona Lisa. Borrowing this from Mark Rolston. If you think about it, AI could basically recreate a one on one replica of Mona Lisa without any identifiable differences. Could actually make it better, more symmetrical, more technically impressive, and so on.

But it won't be Mona Lisa. If we know and in the future, AI art will probably be labeled as per regulations that are not yet in place but are needed. Adobe is already adding those labels. More will follow If we know that it has been generated by AI and we look at it, we probably won't feel too much. But if we're in front of Mona Lisa at the Louvre, we know this was made by da Vinci 500 years ago.

We can see the fascination of the people around us, their excitement. We ourselves can experiment our own version of interpreting and looking at it. We're humbled by being in front of this work of art that has been worshiped by humanity for hundreds of years. It has the potential of being an almost religious, fundamentally human and very touching experience, which is very unlikely to be experienced towards AI art. We will value what is human made.

We will know that something was created from a person's experience, suffering, imagination, hope, scarcity is not what will make art valuable. Its creator is. And then there's another layer to this debate. If AI creates art based on everything it knows from the work of other artists is that such a different expression of creativity from that of a person who has been in art school, studied Picasso, Matisse, Mondrian.

And then their style is influenced by the art history and the works they studied. Something to think about. I'm not saying theft is acceptable, which takes us back to the need for a more transparent, cited, source exposing system for generative AI. But I want to give a different angle on how art is built.

When I looked out of the window of my  house, almost everything I could see was artificial. The homes, the streets, the cars, the city of San Diego, in the distance, of a wonderful Mission Bay, which was a recreational area – well, it was a swamp and it was changed by people to make it comfortable and bulldozed  and it's now a recreational area.

And the city is obviously human-made and the streets and the cars – sure. Even the trees were planted. This made me realize that things like the seasons are artificial. Where I live we have four seasons of the year, but they're artificially defined by the position of the Earth as it goes around the Sun, not by temperature changes. Some places only have two seasons; if you live on the equator, there's not much difference from month to month over the course of a year.

Many countries on the equator have either no seasons at all or just two – one when it rains and one when it doesn't. But the other thing that's even more important are things like laws, country boundaries, the way we live, the customs that we all obey, the way  we interact with each other, our cultures. These are all artificial. And if they're artificial, it means they were designed,

designed by people over the course of many, many centuries – a long period of time – not by professional designers, but by people who were trying to arrange things to make it better for whatever their goal was. If almost everything is artificial and therefore designed, then maybe designers can change things for the better.