Design makes AI smarter
uxdesign.cc – User Experience Design — Medium | Elaine Lee
Be user centric and focused. Build a give-and-take relationship that engages users to teach the AI.
Designers today most likely have been designing for products that use some level of AI for automation. We have been designing in the first stage of AI, artificial narrow intelligence. To get to the second stage of AI, artificial general intelligence, we need user data. Lots of it. How do we get this information?
To better understand how design makes AI smarter, first take a quick look at the different stages of AI in You can be an AI designer. Or, come back to it later.
Collecting human input and data is crucial to achieving artificial general intelligence. We need to design experiences that incentivize engagement that improves the AI. At times, that means prioritizing AI over users, putting more effort on the users to teach the AI. 😮
Designers help artificial intelligence make meaning of user inputs and the implicit signals associated with those inputs. On top of the AI understanding the whats, we decipher the whys from the users’ point of view.
The flow below illustrates a feedback loop between an AI and a human.
Anticipate why users may drop off at a specific point and give them a clear benefit why they should keep moving forward.
Designers need to solve the friction between getting the info the AI needs to know and the info users are willing to provide.
Consider these factors in building a give-and-take relationship between the AI and users.
Be user centric. Create user value.
Focus on the user benefit as a starting point to figure out what the AI needs to learn. Relevant user value can draw people in. Immediate gratification can keep people interested. Apparent improvement or sense of moving forward can engage people in the feedback loop, gaining the data AI needs to get smarter and be relevant.
Prevent dead-ends and keep offering value to users in every step you want them to take.
Focus. Then, expand.
Delivering on the key user value builds trust and engagement between the AI and users. Focus on the key experience and remove unnecessary friction that blocks users from the happy path. Once that user path is solid, which increases engagement, identify what other experiences would enhance the key user value. What knowledge would the AI need to deliver on those experiences?
For example, eBay ShopBot learns about shopping behaviors, inventory, styles, and everything related to shopping. But, it still needs to learn about the world, the weather, current events, trends, and how they all affect decision making. With this extra information, we will be able to design a more thoughtful AI that enhances the users’ shopping experience.
Borrow from humans, but don’t stop there.
Take cues from human interactions, traits, thought processes, and our relationship with computers.
Emotional intelligence: We can teach the AI to be self-aware and socially-aware to bridge the gap and build trust between it and us. Designers are equipped to influence an AI’s emotional intelligence through empathy for the user throughout the product experience.
The AI may not understand the nuances behind what people say and do. But, we do for the most part. The AI needs to recognize the user inputs (words, emojis, gifs, actions, and non-actions) and designers can associate these inputs to possible explicit and implicit user emotions for the AI to learn.
For example, ok and ok… may not always equate to yes. It is our role to identify these nuances so we can teach the AI that ok… may have hesitation, skepticism, or passive aggressiveness attached to it. Paying attention to underlying user intents will help us design more emotionally aware responses from the AI.
Communication style: Currently, humans and computers have a command and respond relationship, with humans commanding. We want to allow the AI to also make these requests and have humans respond. The AI will be more successful in achieving that if it speaks the same language as the user, literally and figuratively.
How the AI represents itself through tone of voice, ways of communication, cultural references, and personality all affect the relationship. Study how the target user communicates with computers, friends, family, and service providers to develop a style for dynamic dialogue. Have the AI communicate in digestible bits, and show empathy and acknowledgement when appropriate. Make difficult questions easy for users to answer to increase the likelihood of getting user data.
Notice that not many AI assistants say sorry. It’s a personality design decision to stay positive and move forward. Provide users with next steps, compensation, suitable alternatives, and immediate gratification as other ways of acknowledging things didn’t go as the user has expected.
Give-and-take relationship: Give users value and take user feedback. The feedback loop is one of the biggest design challenge to improve the AI.
Clifford Nass and BJ Fogg’s study on reciprocity between users and computers found “users may be more likely to agree with, comply with, or help out [a computer] that has previously helped them. This ‘give-and-take’ may also lead to enhanced user performance and increased positive affect.”
Combined with the Hook Model by Nir Eyal, users will be more likely to invest in the AI if they clearly understand and experience the benefits from the investment.
Spotify gives us music we didn’t know we would like. Awesome. It takes explicit signals through thumbs up, thumbs down, and hearts, and implicit signals from passive listening. When we passively listen and not actively give feedback, we end up with songs that we aren’t into. As a result, we learn that we have to be active participants in giving our input so our Discover Weekly playlist will get back on track.
It will take time
It is fascinating and ambiguous designing for AI. The lines between product thinking and design thinking will start to blend.
We will feel constrained by how smart the AI is. And, be conflicted deciding how much we should favor AI over users and vice versa. Focus on getting user data.
We will have the pressure of creating a sense of magic and the frustration of explaining why it can’t be magic right now. Remember the bigger goal of getting to the next stage of AI.
We will need to be okay with launching experiences early to start collecting data, even if we don’t feel it’s ready. It will never be ready. We will let go and accept that the user experience will be unpredictable the smarter the AI becomes.
We will get lost. Then, after taking a look from afar, we see the very long term goal again and remember our important role in pushing AI forward, making it valuable and digestible to humans.
Be user centric, stay focused, maintain a healthy give-and-take relationship. Get data.
I’ve been working on an AI-powered personal shopping assistant in Messenger: eBay ShopBot (beta)
If you’re interested in following along whatever I discover while designing for AI, follow me here and 👏 the post. Thanks for the support 🤓