Analytical Profile of Coleus Forskohlii | Forskolin .pptx
Human-centered AI: how can we support end-users to interact with AI?
1. Human-centered AI: how can we support end-users to
interact with AI?
TRAIL seminar – Paris – 7 April 2023
Katrien Verbert
Augment/HCI – Department of Computer Science – KU Leuven
@katrien_v
4. Human-Computer Interaction group
Explainable AI - recommender systems – visualization – intelligent user interfaces
Augment Katrien Verbert
ARIA Adalberto Simeone
Computer
Graphics
Phil Dutré
LIIR Sien Moens
NLP Miryam de Lhoneux
E-media
Vero Vanden Abeele
Luc Geurts
5. Human-centered AI
5
Human-Centered AI (HCAI) is an emerging discipline intent on creating AI
systems that amplify and augment rather than displace human abilities.
HCAI seeks to preserve human control in a way that ensures artificial
intelligence meets our needs while also operating transparently, delivering
equitable outcomes, and respecting privacy.
https://research.ibm.com/blog/what-is-human-centered-ai
6. Explaining model outcomes to increase user trust and acceptance
Enable users to interact with the explanation process to improve the model
New forms of human-AI interactions
Models
7. Explaining prediction models
7
Gutiérrez, F., Ochoa, X., Seipp, K., Broos, T., & Verbert, K. (2019). Benefits and trade-offs of different
model representations in decision support systems for non-expert users. In Human-Computer Interaction–
INTERACT 2019
9. Explanations
9
Millecamp, M., Htun, N. N., Conati, C., & Verbert, K. (2019, March). To explain or not to explain: the
effects of personal characteristics when explaining music recommendations. In Proceedings of the 2019
Conference on Intelligent User Interface (pp. 397-407). ACM.
media
10. Personal characteristics
Need for cognition
• Measurement of the tendency for an individual to engage in, and enjoy, effortful cognitive activities
• Measured by test of Cacioppo et al. [1984]
Visualisation literacy
• Measurement of the ability to interpret and make meaning from information presented in the form of
images and graphs
• Measured by test of Boy et al. [2014]
Locus of control (LOC)
• Measurement of the extent to which people believe they have power over events in their lives
• Measured by test of Rotter et al. [1966]
Visual working memory
• Measurement of the ability to recall visual patterns [Tintarev and Mastoff, 2016]
• Measured by Corsi block-tapping test
Musical experience
• Measurement of the ability to engage with music in a flexible, effective and nuanced way
[Müllensiefen et al., 2014]
• Measured using the Goldsmiths Musical Sophistication Index (Gold-MSI)
Tech savviness
• Measured by confidence in trying out new technology
10
11. Study design
Within-subjects design: 105 participants recruited with Amazon Mechanical Turk
Baseline version (without explanations) compared with explanation interface
Pre-study questionnaire for all personal characteristics
Task: Based on a chosen scenario for creating a play-list, explore songs and rate all
songs in the final playlist
Post-study questionnaire:
Recommender effectiveness
Trust
Good understanding
Use intentions
Novelty
Satisfaction
Confidence
13. Design implications
Explanations should be personalised for different groups of end-
users.
Users should be able to choose whether or not they want to see
explanations.
Explanation components should be flexible enough to present
varying levels of details depending on a user’s preference.
13
14. User control
Users tend to be more satisfied when they have control over
how recommender systems produce suggestions for them
Control recommendations
Douban FM
Control user profile
Spotify
Control algorithm parameters
TasteWeights
media
15. Controllability Cognitive load
Additional controls may increase cognitive load
(Andjelkovic et al. 2016)
Ivana Andjelkovic, Denis Parra, andJohn O’Donovan. 2016. Moodplay: Interactive mood-based music
discovery and recommendation. In Proc. of UMAP’16. ACM, 275–279.
16. Different levels of user control
16
Level
Recommender
components
Controls
low Recommendations (REC) Rating, removing, and sorting
medium User profile (PRO)
Select which user profile data
will be considered by the
recommender
high
Algorithm parameters
(PAR)
Modify the weight of different
parameters
Jin, Y., Tintarev, N., & Verbert, K. (2018, September). Effects of personal characteristics on music recommender
systems with different levels of controllability. In Proceedings of the 12th ACM Conference on Recommender
Systems (pp. 13-21). ACM.
17. User profile (PRO) Algorithm parameters (PAR) Recommendations (REC)
8 control settings
No control
REC
PAR
PRO
REC*PRO
REC*PAR
PRO*PAR
REC*PRO*PAR
18. Study design
Between-subjects – 240 participants recruited with AMT
Independent variable: settings of user control
2x2x2 factorial design
Dependent variables:
Acceptance (ratings)
Cognitive load (NASA-TLX), Musical Sophistication, Visual Memory
Framework Knijnenburg et al. [2012]
19. Results
Main effects: from REC to PRO to PAR → higher cognitive load
Two-way interaction: does not necessarily result in higher
cognitive load. Adding an additional control component to
PAR increases the acceptance. PRO*PAR has less cognitive
load than PRO and PAR
High musical sophistication leads to higher quality, and thereby
result in higher acceptance
19
Jin, Y., Tintarev, N., & Verbert, K. (2018, September). Effects of personal characteristics on music
recommender systems with different levels of controllability. In Proceedings of the 12th ACM Conference on
Recommender Systems (pp. 13-21). ACM.
20. What if the stakes are higher?
20
Learning
analytics &
human
resources
Media
consumption
health
Precision
agriculture
FinTech &
Insurtech
22. 22
Gutiérrez Hernández F., Seipp K., Ochoa X., Chiluiza K., De Laet T., Verbert K. (2018). LADA: A learning
analytics dashboard for academic advising. Computers in Human Behavior, pp 1-13. doi:
10.1016/j.chb.2018.12.004
LADA: a learning analytics dashboard for
study advisors
24. Results
What worked
✚ valuable tool for more
accurate and efficient
decision making.
✚ Users evaluated significantly
more scenarios.
What didn’t work
− More transparency needed
increase trust.
− Model didn’t behave as
expected
− LADA didn’t meet our users
needs
24
Gutiérrez Hernández F., Seipp K., Ochoa X., Chiluiza K., De Laet T., Verbert K. (2018). LADA: A learning
analytics dashboard for academic advising. Computers in Human Behavior, pp 1-13. doi:
10.1016/j.chb.2018.12.004
26. Design science research
26
Fraefel, U. (2014, November). Professionalization of pre-service teachers through university-school partnerships. In
Conference Proceedings of WERA Focal Meeting, Edinburgh.
27. Data-centric explanations
Charleer, S., Moere, A. V., Klerkx, J., Verbert, K., & De Laet, T. (2018). Learning analytics
dashboards to support adviser-student dialogue. IEEE Transactions on Learning
Technologies, 11(3), 389-399.
28. Do not oversimplify: show uncertainty
reality is complex
measurement is limited
individual circumstances
need for nuance
trigger reflection
29. 29
Charleer S., Gutiérrez Hernández F., Verbert K. (2019). Supporting job mediator and job seeker through an actionable dashboard. In:
Proceedings of the 24th IUI conference on Intelligent User Interfaces Presented at the ACM IUI 2019
actionalable
explanations
35. Take away messages
Explanations contribute to user empowerment
Key difference between actionable and non-actionable
parameters
Need for customization and contextualization
Need for simplification
35
38. AHMoSe
Rojo, D., Htun, N. N., Parra, D., De Croon, R., & Verbert, K. (2021). AHMoSe: A knowledge-based visual
support system for selecting regression machine learning models. Computers and Electronics in Agriculture,
187, 106183.
40. Case Study – Grape Quality Prediction
40
Grape Quality Prediction Scenario [Tag14]
Data
Years 2010, 2011 (train) 2012 (test)
48 cells (Central Greece)
Knowledge-based rules
[Tag14] Tagarakis, A., et al. "A fuzzy inference system to model
grape quality in vineyards." Precision Agriculture 15.5 (2014):
555-578.
Source: [Tag14]
41. Simulation Study
AHMoSe vs full AutoML approach to support model selection.
41
RMSE (AutoML) RMSE (AHMoSe) Difference %
Scenario A
Complete
Knowledge
0.430 0.403 ▼ 6.3%
Scenario B
Incomplete
Knowledge
0.458 0.385 ▼ 16.0%
42. Qualitative Evaluation
10 open ended questions
5 viticulture experts and 4 ML experts.
Thematic Analysis: potential use cases, trust, usability, and
understandability.
43. Qualitative Evaluation - Trust
43
Showing the dis/agreement of model outputs with expert’s
knowledge can promote trust.
“The thing that makes us trust the models is the fact that most of
the time, there is a good agreement between the values
predicted by the model and the ones obtained for the knowledge
of the experts.”
– Viticulture Expert
46. Design and Evaluation
46
Gutiérrez F., Cardoso B., Verbert K. (2017). PHARA: a personal health augmented reality assistant to support
decision-making at grocery stores. In: Proceedings of the International Workshop on Health Recommender
Systems co-located with ACM RecSys 2017 (Paper No. 4) (10-13).
47. What if the stakes are really high?
Learning
analytics &
human
resources
Media
consumption
health
Precision
agriculture
FinTech &
Insurtech
50. 50
Gutiérrez Hernández, F. S., Htun, N. N., Vanden Abeele, V., De Croon, R., & Verbert, K. (2021). Explaining call
recommendations in nursing homes: a user-centered design approach for interacting with knowledge-based
health decision support systems. In Proceedings of the 27th Annual Conference on Intelligent User Interfaces.
ACM.
Explaining predictions health
51. Evaluation
12 nurses used the app for three months
Data collection
Interaction logs
Resque questions
Semi-structured interviews
51
53. Results
Iterative design process identified several important features, such as the pending
list, overview and the feedback shortcut to encourage feedback.
Explanations seem to contribute well to better support the healthcare professionals.
Results indicate a better understanding of the call notifications by being able to see the
reasons of the calls.
More trust in the recommendations and increased perceptions of transparency and control
Interaction patterns indicate that users engaged well with the interface, although some
users did not use all features to interact with the system.
Need for further simplification and personalization.
53
55. 55
Explaining recommendations
Word cloud Feature importance Feature importance+ %
Maxwell Szymanski, Vero Vanden Abeele and Katrien Verbert Explaining health
recommendations to lay users: The dos and don’ts – Apex-IUI 2022
health
59. Results
Hybrid explanations more useful compared to both the textual and
visual explanations.
Users with a higher NFC tend to score the hybrid explanations
lower in terms of trust, transparency and usefulness compared to
the unimodal explanation.
59
60. Results
Participants with low NFC have a better perception of hybrid
explanations
Participants with high NFC have a better perception of
unimodal explanations
60
62. Combining XAI methods to address different
dimensions of explainability
Increasing actionability through interactive what-if analysis
Explanations through actionable features instead of non-
actionable features
Color-coded visual indicators for easy identification of patients
with high risk
Data-centric directive explanations
62
Bhattacharya, A., Ooge, J., Stiglic, G., & Verbert, K. (2023, March). Directive Explanations for Monitoring the Risk of Diabetes
Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations. In Proceedings of the
28th International Conference on Intelligent User Interfaces (pp. 204-219).
67. Data-centric explanation methods for fraud detection
Explanations in high-stake domains will become mandatory by EU
regulations
Transparent and interactive data matching
Insurance premium simulations
Link with external data sources
E.g. occupational accidents, absenteeism data
67
https://human-centered.ai/project/explainable-ai-fwf-32554/
68. Take-away messages
Involvement of end-users has been key to come up with
interfaces tailored to the needs of non-expert users
Actionable vs non-actionable parameters
Domain expertise of users and need for cognition important
personal characteristics
Need for personalisation and simplification
Data-centric explanations provide powerful solution
68
The prediction model shows the impact of the food product on the weight of the participant. Opacity is used to represent the uncertainty of this prediction. (POINT to third card)
“Insight vs. information overload”
Most users prefer more information (holistic overview of inputs)
However, some users experienced information overload
→ Future work - Do personal characteristics such as NFC influence this?