XXIX Salon: Principles of Language for Autonomous Systems
Salon: Principles of Designing for Autonomous Systems: Communication 11/11/19
1. Premise: language is crucial in newness
How the system communicates to users
Branding of AI
Communicating responsibility and legibility of the system — just because they’re hidden / hard to pinpoint doesn’t mean we can’t do interpretive work / rectify
2. Everyone wants to know the future: hunting for predictions
Subliminal desire for futurism
Next / new psychology
The culture of hope
Other terms for prediction:
Estimate
Projection
Guess
Probability
Forecast
Segmentation — individual vs. collective
What language can we use to identify that we are part of a segment?
Personas and archetypes — is this the bias that led here in the first place?
Human factors that led to cultural segmentation
Articulating the unknowns in your prediction
“people like you like”
3. Ethical Issues with False Definitions
“My system is going to predict x, and x affects people” — problematic
Human filter, bias
4. Avoid the hype(machines can’t be smart)
Given: machines can’t be smart
Through language, is there a way to help people be thoughtful in creating systems?
5. Gradients of machine autonomy: the use of language in setting expectations
Spectrum:
Machine work--------------- Human work
What’s explicit, what’s insinuated?
Agency: single-turn, multi-turn… how many turns before one can lose human agency?
How do you tell somebody that their car is going to drive itself? How do you communicate
A task is a decision point that progresses the system’s goal
Communication → perception = decision(Signal → receiving the signal = result)
Spectrum:
High stakes ↔ low stakes / human ↔ machine
Not a point, a vector
Reference:
A Model for Types and Levels of Human Interaction with Automation Raja Parasuraman, Thomas B. Sheridan, Fellow, IEEE, and Christopher D. Wickens(Sam Kellogg)(Claire Mitchell)
1. Premise: language is crucial in newness
2. Everyone wants to know the future: hunting for predictions
3. Ethical Issues with False Definitions
4. Avoid the hype (machines can’t be smart)
5. Gradients of machine autonomy: the use of language in setting expectations
6. Language in User Interfaces