The document discusses the complexities of trust in smart systems, emphasizing the importance of user interactions and feedback in shaping trust and mental models. It presents research findings on how explanations about system behaviors and suggestions can enhance user satisfaction and trustworthiness, particularly in contexts involving music recommendation systems. The document also raises questions on measuring trust, personalizing explanations, and understanding user perceptions in varying risk scenarios.

1. In interfaces we trust?
Dr Simone Stumpf
End-user interactions with smart systems.
@DrSimoneStumpf
#HCID2014

2. A slippery term called trust.
Trustor’s dependency on the reliability, truth or
ability of a Trustee.
Risk or uncertainty means that trust may be
misplaced.
We use a number of cues to assess
trustworthiness which shape trusting intentions and
actions.

3. [Oosterhof & Todorov]
Who do you trust more?
Approachability and dominance shown to matter in trust.

4. Which do you trust more?
Research of trust in tools and systems is in its infancy.

5. Some systems nowadays are smart.
Use implicit and explicit feedback to learn how to
behave using complex algorithms and statistical
machine learning approaches.
They might make decisions automatically without
user control; they are autonomous.
They might personalise themselves to a user as
they interact with a system instead of following
static pre-set rules.

6. Why do we trust in these smart systems?
Previous research has indicated that the following
aspects seem to matter:
Reliability of the suggestions, especially the
predictability and perceived accuracy.
Understanding the process of how the system
makes suggestions.
Expectations of the system and personal
attitudes towards trust.
[Dzindolet et al. IJCHS 2003]

7. Explaining makes system transparent.
End
User
Intelligent
Agent
Mental
Model
FeedbackExplanation
What are the effects of explanations on building (correct)
mental models?
[Stumpf et al. Pervasive Intelligibility 2012]

8. Building a research prototype.
Figure 1. Users could debug by saying why the
current song was a good or bad choice.
. . .
Figure 2. Participants could debug by adding guidelines on the type of
music the station should or should not play, via a wide range of criteria.
[Kulesza et al. CHI 2012]

9. Researching a music recommender.
[Kulesza et al. CHI 2013]
Between-group study design on depth of
explanations about how the system works, free use
over a week from home, then assessment.
Deeper explanations helped to build a more
correct mental model.
Explanations also helped with user satisfaction
and success in adapting playlists.
Usage of the system did not help; in fact, it can
cause persistent incorrect mental models.

10. How much do we need to explain?
End
User
Intelligent
Agent
Mental
Model
FeedbackExplanation
How sound and complete do explanations need to be?

11. Researching a music recommender.
[Kulesza et al. VL/HCC 2013]
Lab-based between-group study varying levels of
explanations’ soundness and completeness, then
assessment.
Could make explanations less sound but
reducing soundness led to users losing trust in
system.
High levels of both combined are best for
building correct mental models and for user
satisfaction; completeness has more influence.

12. What of the system needs explaining?
End
User
Intelligent
Agent
Mental
Model
FeedbackExplanation
How can we explain system behaviour in the best way?
Algorithm?
Features?
Process?

13. Lo-fi and hi-fi research prototypes.
[Stumpf et al. IJCHS 2009]
[Kulesza et al. Vl/HCC 2010]
[Kulesza et al. TiiS 2011]

14. Explaining smart components.
Process is best understood if explained through
rules, keyword-based explanations second.
People struggle with understanding machine
learning algorithms e.g. negative weights.
Features used better understood than process.
Similarity-based explanations are not well
understood.
Preference for explanation style individual to user.

15. Explaining smart suggestions.
Showing how confident system is of correctness of
suggestion gives user cue to trustworthiness.
Carefully balance amount of explanation against
usefulness and cost in assessing trust.
Indicating prevalence of system suggestions in
relation to what the user has provided also useful.

16. The way forward.
What is a good way to measure trust?
How can we personalise explanations?
How do explanations differ in high-risk versus low-
risk systems?
What further cues can we gives users to assess
trustworthiness of a smart system?
How can we prevent disuse or misuse?

17. Thank you. Questions?
http://www.city.ac.uk/people/academics/simone-stumpf
Simone.Stumpf.1@city.ac.uk
@DrSimoneStumpf
#HCID2014