it efficient?
These are new questions for interaction design that will be fun and important to answer. But with more and more travel being handled by agents, it promises not just to become safer and more efficient overall, but also to give riders more time to do things that interest them, only occasionally needing to manage the vehicle. These issues are important enough to warrant two chapters in this book. See Chapter 8, “Handling Exceptions,” and Chapter 9, “Handoff and Takeback,” in Part II.
When I spoke with a representative from ShotSpotter, she explained that the service is helpful for more reasons than just decreased police response time. It also helps ensure that the “shots fired” signal reaches the police at all. One factor is the bystander effect, in which people presume that surely someone else has reported it already. This might affect people living anywhere. But citizens living in high-crime areas, she explained, can often fear being labeled a snitch and suffering consequences for reporting crimes. ShotSpotter takes this responsibility that no one wants unto itself.
They’ll Do the Embarrassing Things
Going on first dates can be harrowing. Who knows if that person is genuinely charming, or a well-practiced sociopath? You should have a backup, someone who will check up on you. But then again, you don’t want to burden friends with remote chaperone duties for every single date you go on. Enter the safety agent that is kitestring.io. You tell Kitestring when to check up on you, information about the date you’re going on, and an emergency contact. When time is up, Kitestring sends you a text to make sure you’re OK. If you don’t answer, or reply with your fake safe word, it forwards the information about the date to the emergency contact, presumably so they can take immediate action to find you and ensure your safety. If you answer with the real safe word, Kitestring erases the information about the date and stops checking up on you. It’s not a replacement for being careful, but an additional tool in your arsenal.
They Will Allow Play . . .
In some domains, users are happy to let the agent run, and only think about it when there’s a problem. Managing a long-term investment portfolio is one where you specifically don’t want to look at it every day. But in other domains, you’ll need to recognize that users will still want to play.
Here I’m thinking of iOS AutoCorrect. (It’s closer to assistive tech, but is still an instructive example for our purposes here.) In this low-level typing function, if users mistype a word (or correctly type an unusual word that is not in its dictionary), the operating system will offer to replace the word with its best guess of the intended word. Spell-checks have been around almost since the beginning of computers, but AutoCorrect has two differences. First, the interaction design is such that most of the time, people don’t realize their words have been autocorrected until after they’ve sent text messages or made status updates. Second, being on a mobile OS with an on-screen keyboard means there are plenty of mistakes to be corrected.
Most of the time, AutoCorrect works pretty seamlessly, changing people’s mitsakes to mistakes before they realize they’ve happened. Some of the time the corrections are nonsensical. And a few times they can be genuinely funny.
But if you are a playful user of language, AutoCorrect is much more of a damned nuisance than a help. There’s a whole philosophical tangent I’ll avoid about why being a playful user of language is important at any age, but even if you’re personally prudish with your words, note that it’s a fact of teenage life and part of the currency of subcultures. It’s a way to create and celebrate a shared identity. Consider the recent popularity of “turnt,” “bae,” “yas,” and “nudnik.” OK, that last one is from the 1920s, but a flapper with an iPhone, can you imagine? Since both declining the autocorrections and adding to its dictionary slow your text entry down significantly, it’s much easier to just turn the feature completely off. Better would be an agent that gauges your degree of playfulness, or updates itself with language trends of your peers, and backs off accordingly.
(Note that we are on the second edition of Damn You Auto Correct compilations from the blog of the same name, so it’s kind of a thing.)
. . . and They Will Encourage Discovery Through Drift
IBM has been working on its deep learning engine Watson since 2005.2 Although its first public implementation was to compete against humans on the game show Jeopardy!, since winning that show in 2011, the project has evolved in some other directions. Chef Watson is one spinoff that came online in 2015. At first, it seems like any other recipe database: a search form lets you input ingredients to find recipes.
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