Designing the BX: Architectures of collaboration between bots and humans

Transcript

1. BX (The parrot is completely unrelated to this talk) https://vimeo.com/22825752
2. Capitalism seeks the perfect worker drone Technocapitalism: working on making humans work more like drones and making bots seem more like humans.
3. BX as in Bot eXperience • How do we design digital environments that allow software agents and humans to engage in meaningful cooperation? • How do we create workflows and points of articulation in which agency is shared, delegated and transferred between human and bots for the sake of common goals? • How do we design buttons that can be pressed by bots and humans both? Also: How do we allow bots to grow and adapt and learn in virtuous loops?
4. About me
5. Honest: a sales channel based on conversations.
6. Honest: a sales channel based on conversations between humans.
7. Honest: a sales channel based on augmented conversations between humans. “In the midst of ‘AI awakening’ where machines are becoming good at many ‘human’ jobs, people are worried that AI will ultimately lead to mass unemployment by replacing them. On the contrary, we believe that we can achieve the most creative and productive outcomes when humans and machines work together to enhance each other’s complementary strengths and skills.” https://medium.com/@howtogeneratealmostanything/how-to-generate-almost-anything-2161f29578b4
8. By "augmenting human intellect" we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems. AUGMENTING HUMAN INTELLECT: A CONCEPTUAL FRAMEWORK https://www.dougengelbart.org/pubs/augment-3906.html
9. Increased capability in this respect is taken to mean a mixture of the following: more-rapid comprehension, better comprehension, the possibility of gaining a useful degree of comprehension in a situation that previously was too complex, speedier solutions, better solutions, and the possibility of finding solutions to problems that before seemed insoluble.
 
 
 Douglas C. Engelbart
10. Architectures of collaboration between humans and bots Humans: empathy, nuance, communication Bots: “Spreadibility”, speed, talk with external services in their own language
11. “Tweenbot is a fairly simple robot for a fairly simple concept, but there are many things we could learn from watching the robot roll through our cities. Are some cultures more open to helping robots? Does age play a factor? How about education, etc? “ https://vimeo.com/22825752 How do we design company cultures and dynamics that are more open to helping bots https://singularityhub.com/2011/05/09/build-your-own-tweentbot-video-warning-open-in-html-only/#sm.000006zsod9lotdxbq2d3gkiqldt4
12. That might be part of the near-future: being surrounded by things that are helping us, that we struggle to build a model of how they are doing it in our minds. That we can’t directly map to our own behaviour. A demon-haunted world. This is not so far from most people’s experience of computers (and we’re back to Byron and Nass) but we’re talking about things that change their behaviour based on their environment and their interactions with us, and that have a certain mobility and agency in our world. The problem for humans: work with colleagues which mental models we cannot comprehend How do we create a work environment in with the bot part of the team is welcome and empathised with?
13. Each of them working across a little domain within your home. Each building up tiny caches of emotional intelligence about you, cross-referencing them with machine learning across big data from the internet. They would make small choices autonomously around you, for you, with you – and do it well. Surprisingly well. Endearingly well. They would be as smart as puppies. Watch your metaphors: build bots that fit into pre-existing structures (thought and bureaucratic) Make them signal out their willing to learn, openness to collaboration. Language matters! Also at which point they’re presented, the feedback they give. Clearly defined domains of expertise, as small as possible feedback loops. They’re junior employees that level up working alongside human veterans.
14. "But the wow moment for me was when he talked about a notional kind of RSS reader -- an "RSS-I" reader, for interactive RSS. The idea is to take all the little decisions that all the services you use have asked you to make (Amazon recommends a book, your mailing list wants you to approve a post, Flickr wants you to add a buddy, your blog wants you to approve some comments) and stream them into a special reader, so that they're all in one place, and you can keep track of your decisions, make them in one go, and not have to run all over the Web.” https://boingboing.net/2007/03/29/rssi-an-rss-feed-for.html
15. Design workflows, “decisional joints” Understand where along the way there’s opportunity and necessity of a switch of leadership between human and bot stakeholders
16. This is the basic problem with AI: Its algorithms are not neutral, and the reason they're biased is that society is biased. "Bias" is simply cultural meaning, and a machine cannot divorce unacceptable social meaning (men with science; women with arts) from acceptable ones (flowers are pleasant; weapons are unpleasant). A prejudiced AI is an AI replicating the world accurately. Algorithmic bias on the corporate level. A bot learns based on the assumptions of the corporate culture that creates them. Beware!
17. What’s the equivalence of a big red button for a bot? How do you accomodate bot intelligence, how do you design “deep interfaces” that can be used for non-human agents?
18. So • Leverage the idea of “augmented teams” of bots and humans working together. • Design bots that are read as predictable, reliable by their human peers. • Create bots that do a few things and do that well, let them enter positive feedback loops to learn and build up trust. • Acknowledge cultural biases, both coming from the culture and from your company. • Enable contexts that can be inhabited by humans and bots, embrace the dual nature of bot as agent and affordance of your digital product.
19. Where to from here? • Integrate a holistic thinking that considers both human and non human actors in your systems. • Seek support on social theory and philosophy: ANT? Object-oriented ontology? • Expand the definition the design to include aesthetics, systems and moral thinking.
20. Thanks