Collaboration in Context: AI and Human Interaction

Collaboration in Context: AI and Human Interaction
@carologic @ddegler #UXPA22
23 June 2022 © Carol Smith, Duane Degler
Collaboration in
Context: AI and
Human Interaction
UXPA CONFERENCE 2022
Carol Smith
duane@designforcontext.com
@ddegler
cjsmith@sei.cmu.edu
@carologic
Duane Degler
https://d4c.link/UXPA22

Reshaping our understanding of UX

Challenges
● It can feel daunting!
● AI is an emerging area – it’s not just “building software”
● We have a small peer community among UX practitioners
The research and design practice is different
● Activities emphasize data and “models”
Models are profiles that characterize the system and its contexts
● Products have no fixity (or at best limited time periods of fixity)
Human-AI means evolving, ongoing, dynamic relationships between entities
WHY ARE WE HERE?

…a non-linear process
RESEARCH & DESIGN IS A CONTINUOUS PROCESS
Startup, design,
initial training
Adoption,
maturing
Evolution
through use
Visioning
Research
Design
Training
Use
Feedback

Imagine the autumn of 2020…
2020: Carol and Duane talking about AI, and wondering:
“How similar or different are the UX, IA and design approaches across
different branches of the AI landscape? How do our needs align?”
2021: Started articulating a Framework – thinking about the problem space for UX
● An ongoing, non-linear design practice
● Models for Context Signals
● Time Cycles
2022: How do we take a role, and apply it to our Practice?
From the IA Conference workshop:
Smith, Degler, Evanhoe (2022). IA for AI: Shaping the Experience for Human-AI Systems

CONTEXTS
Translating what we know about
humans + machines
Understanding aspects of the
environment and the relationship
between human and machine

HOW DO WE TRANSLATE
HUMAN CONTEXTS FOR
MACHINE RECOGNITION?
SITUATION
Location & proximity
Co-occurring events
Date / time
Participants
Devices / connectivity
ACTORS
Usage patterns
Experience
Interests / profile
History
Community
INFORMATION
Link relationships
Text patterns
Categories / keywords
Metadata
Data structures
ENVIRONMENT
Other people/things
Weather
Available resources
Politics/economics
Culture
TASK/OUTCOME
Outcomes / goals
Rules / requirements
Criticality
Sequence / status
Frequency for user
LANGUAGE
Dialect
Accent
Prosody
Behavior
Culture
Degler (2013). Supporting Relevance for Users: A Design Challenge https://d4c.link/semtech13
Degler (2018). No Static: IA for Dynamic Information Environments https://d4c.link/IAC18

CONSIDERATIONS FOR CONTEXTS IN A CONVERSATION
Now: workday, between hectic day of meetings
Smart speaker connected to kitchen ordering system
Human and chat bot
Operating vehicle, ordering food, and payment
Fast food drive thru
SITUATION
Co-occurring events
Date / time
Participants
Rebecca Evanhoe, from the IA Conference workshop:

CONSIDERATIONS FOR A MEDICAL DIAGNOSIS
Find the right information, probes for alternatives
Communication to/from colleagues as part of process
Past systems have been problematic
Diagnostic questions attuned to user’s protocols
So far, successful diagnosis for multiple patients
Physician has used system repeatedly
ACTORS
Usage patterns
Experience
Interests / profile
History
Community
From the IA Conference workshop:

Dynamic IA: at the point of human interaction, the human experience
is with dynamic structures
IA/UX must allow many contextual forces to interact with each other
in a way that is transparent, yet able to be controlled by the user
● How?
● Design with an expectation of co-evolution
● Provide feedback loops when releasing semi-autonomous entities into the world
● Engage responsibly with our dynamic creations
EMERGENT IMPLICATIONS FOR UX
No Static: IA for Dynamic Information
Environments Degler (2018)
https://d4c.link/IAC18

Collaboration

Human-Machine Teaming: a dynamic exchange between 2 or more
(semi-)autonomous entities.
Human(s):
● Work in existing (and/or new) systems and processes
● Have situational context and awareness of external information
● Need appropriate evidence of system capabilities to gain calibrated trust
● Maintain agency and ultimate control
Machine(s) augment human(s):
● Built with data, algorithms, software, infrastructure, UI, etc.
● Only know what taught (narrow systems)
● Learn over time – evolve over time, as new data is integrated
● Managed (continuous) knowledge-gathering
● Have designated agency – in the interaction, and in the information space
COLLABORATION
Hollen Barmer et al., Human-Centered AI,
White Paper (CMU Software Engineering
Institute, June 2021)
https://resources.sei.cmu.edu/library/asset-
view.cfm?assetid=735362.

What is required for a healthy collaboration with an AI system?
Teaming factors:
● Roles
● Tone
● Calibrated Trust
● Awareness
● Value (sustained)
Interaction factors:
● Communication
● Negotiation
● Coordination
WHAT IS A HEALTHY TEAMING EXPERIENCE?

● How are complementary roles and contributions fostered?
● How are responsibilities assigned?
● Roles may include:
● Direct users
● Larger societal groups
● Affected populations
● Other Stakeholders (subject matter experts, acquisition, management, etc.)
● Development and monitoring teams
COLLABORATION: ROLES

● How does the machine communicate humility? How does it engender humility?
● What does it mean to “respond appropriately”?
● How does the machine reflect culturally-appropriate values and communication
styles?
● How is respect shown?
● Can the machine communicate compassion? Should it?
COLLABORATION: TONE

● Humans find balance between over trust and under trust.
● Ability to judge risk of teaming with machine based on evidence of system
capabilities and integrity.
● What is “enough” evidence?
● How is evidence provided appropriately to different Roles?
What levels of transparency?
● Data provenance?
● System capabilities and availability?
● Bias identification and mitigation?
● Acting for privacy and transparency?
● How does evidence change based on the human’s experience?
COLLABORATION: CALIBRATED TRUST
Hollen Barmer et al., Human-Centered AI, White
Paper (CMU Software Engineering Institute, June
2021) https://resources.sei.cmu.edu/library/asset-
view.cfm?assetid=735362.

● Co-evolution
● Humans and machines gaining knowledge – continuously learning
● Synchronous and asynchronous
● Complement human and machine strengths
● Design AI to…
● Show evidence: share information and show awareness
● Be understandable
● Engender calibrated trust
● System is not deterministic – it will ideally find new patterns, even create new ideas!
● Flexibly handle exchanges that are not “obvious” or scripted (ideally) – interactions
should be designed to support variation within a scope
● Always keep humans in ultimate control
COLLABORATION: CALIBRATED TRUST (evidence)

● Timing
● Speed of interaction?
● Urgency?
● Frequency?
● Anomalous situation?
● How are turn-taking and information exchanges communicated?
COLLABORATION: AWARENESS
C
O
N
T
E
X
T
Brian Neese, Intercultural Communication:
High- and Low-Context Cultures. Aug 17,
2016. https://online.seu.edu/high-and-low-context-cultures
See also:
Elizabeth Würtz, Intercultural
Communication on Web sites: A Cross-
Cultural Analysis of Web sites from High-
Context Cultures and Low-Context Cultures.
Journal of Computer-Mediated
Communication 11 (2006) 274–299.
https://doi.org/10.1111/j.1083-6101.2006.tb00313.x
● What is the situational context?
● High-context culture –
implicit communication and nonverbal cues
● Low-context culture –
more explicit communication and explanation

● Will the collaboration build and sustain over time?
● How do we maintain a mutual exchange of value?
● How do we monitor changes in Calibrated Trust (reduced value)?
● How do we monitor and measure changes in outcomes - system effectiveness?
● More than personality – interactional (meaningful) exchanges - not only
transactional.
COLLABORATION: VALUE

COLLABORATION / ACTION
FRAMEWORK
Expressing the nature of the interactions between Human-AI
Remember where we started:
“How similar or different are the UX, IA and design
approaches across different branches of the AI landscape?
How do our needs align?”

…research & design is a continuous, non-linear process
REFLECTING ON UX PROCESS
Startup, design,
initial training
Adoption,
maturing
Evolution
through use
Visioning
Research
Design
Training
Use
Feedback

REFLECTING ON
HUMAN AND MACHINE
CONTEXTS
SITUATION
Co-occurring events
Date / time
Participants
ACTORS
Usage patterns
Experience
Interests / profile
History
Community
INFORMATION
Link relationships
Text patterns
Categories / keywords
Metadata
Data structures
ENVIRONMENT
Other people/things
Weather
Available resources
Politics/economics
Culture
TASK/OUTCOME
Outcomes / goals
Rules / requirements
Criticality
Sequence / status
Frequency for user
LANGUAGE
Dialect
Accent
Prosody
Behavior
Culture
Degler (2013). Supporting Relevance for Users: A Design Challenge https://d4c.link/semtech13
Degler (2018). No Static: IA for Dynamic Information Environments https://d4c.link/IAC18

CATALYST CLOSURE
Event
Act
Monitor
Request
Continue
Complete
INTERACTIONS
COMMUNICATION NEGOTIATION COORDINATION
TIME CYCLES OF INTERACTION

TIME CYCLE: ORDERING BY VOICE
“Popsicle
please.”
Respond
Elicit
Detect
[Machine
lights turn on] “What
would you
like to
order?”
Elicit
“What
flavor?”
Confirm
“Which
one’s
best?”
Clarify
“Cherry is
the most
popular.”
Recommend
“Okay,
I’ll try
that.”
Request
“Can I
have
one,
too?”
Act
Act
[popsicles
drop
down]
[pay
for
popsicles]
Affirm
“Thank
you.”
“Do you
want
Cherry,
too?”
Elicit
Request
“No, I
want
Lemon.”
“One
Cherry,
one
Lemon.”
Confirm
Rebecca Evanhoe, from the IA Conference workshop:

(recover?)
Identify
Identify Instruct
Act Explain Identify
Hand Off
Assess Act
Learn
TIME CYCLE: VEHICLE AVOIDING ROAD OBSTRUCTION

“Continuous Learning” is mutual, and inherent in future AI systems. How do we think
about that? Consider how to design for state changes between sessions.
MUTUAL CONTINUOUS LEARNING
Human(s) evolve/learn,
and their needs change
Machine(s) evolve/learn, and
Information space evolves
EXPRESS
RESPECT
Human shares information
with system
Machine acknowledges
human evolution/learning
Machine demonstrates
evolution/learning
Human acknowledges explanation
of evolution/learning

TIME CYCLE: DECISION SUPPORT (MEDICAL ASSESSMENT)
CATALYST CLOSURE
Question
Refine
Identify
Describe
Reflect
back
Elaborate
Record
Prescribe
Suggest
Explain
Expand
Suggest Explain
Clarify
Question

TIME CYCLE: DECISION SUPPORT (MEDICAL FOLLOW-UP)
CATALYST CLOSURE
Question
Refine Record
Discuss
Elaborate
Prescribe
Suggest
Explain
Expand
Suggest Explain
Review
Notify
Reflect
back
Elaborate
Question

Assess
Deliver
Explain
Instruct
Refine
Collect
Remove
Learn
Adjust
Question
Explain Seek
Assess Select
Deliver
Explain
Update
Update
Gather
Deliver
Reflect
back
Clarify
Seek
Adjust
CATALYST CLOSURE
TIME CYCLE: SEARCH FOR RESEARCH (RESUMING)

VARIATIONS IN TIME CYCLE PATTERNS (an exploration)
Advisory
assessment personal improvement pathway
emerge, evolve
extend
act
extend
act
Longitudinal
research
research process
explore explore
Layered
exchanges
journey
Direct
interactions
transaction
?

SUMMARY
● Research & design is a continuous, iterative process
● Contexts: translate human contexts for machine recognition
● Collaboration: fostering a healthy teaming experience
● Time Cycles: a Collaboration/Action Framework

DISCUSSION
When asked to join a team / project
● How does the team anticipate the involvement of UX?
● What engagement with users is planned and possible?
● What challenges does the intended system create for your research/design?
When planning UX activities
● What kinds of activities will produce insights and value?
● How can you incorporate elements of the Framework in your process?
● How do you make your insights actionable and integrated with team materials?

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