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Human-centered AI: how can we support end-users to interact with AI?

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Katrien Verbert
Katrien Verbert

Human-centered AI: how can we support end-users to interact with AI? This document discusses how to design human-centered AI systems that support end-users. It explores explaining model outcomes to increase trust and acceptance, and enabling users to interact with explanation processes. Personal characteristics like need for cognition impact how users respond to explanations. Explanations should be personalized and allow different levels of detail. Evaluations show explanations improve understanding but also increase cognitive load, so simplification is important. The goal is to preserve human control and ensure AI meets user needs.

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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
2 for the invitation!
Human-centered AI: how can we support end-users to interact with AI?
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
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
 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
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
Learning analytics & human resources Media consumption Wellbeing, food & health Precision agriculture FinTech & Insurtech What do end-users really need?
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
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
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
Results 12 The interaction effect between NFC (divided into 4 quartiles Q1-Q4) and interfaces in terms of confidence
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
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
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.
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.
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
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]
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.
What if the stakes are higher? 20 Learning analytics & human resources Media consumption health Precision agriculture FinTech & Insurtech
Learning analytics Src: Steve Schoettler
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
Study design 23 Evaluation @KU Leuven Monitoraat N = 12 6 Experts (4F, 2M) 6 Laymen (1F, 5M) Evaluation @ESPOL (Ecuador) N = 14 8 Experts (3F, 5M) 6 Laymen (6M)
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
Strategies 25
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.
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.
Do not oversimplify: show uncertainty  reality is complex  measurement is limited  individual circumstances  need for nuance  trigger reflection
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
30
User-centred design 31
Final evaluation 32 66 job seekers (age 33.9 ± 9.5, 18F) 8 Training Programs, 4 Groups, 1 Hour. 1 2 3 4 5 6 7 8 ResQue Questionnaire + two open questions. Users explored the tool freely. All interactions were logged.
33 Results
34 Results
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
Strategies 36 Explaining model behaviour In-situ decision support
Explaining model behavior 37 Precision agriculture
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.
AHMoSe Visual Encodings 39 Model Explanations (SHAP) Model + Knowledge Summary
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]
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%
Qualitative Evaluation  10 open ended questions  5 viticulture experts and 4 ML experts.  Thematic Analysis: potential use cases, trust, usability, and understandability.
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
In-situ decision support 44
45 https://augment.cs.kuleuven.be/demos
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).
What if the stakes are really high? Learning analytics & human resources Media consumption health Precision agriculture FinTech & Insurtech
Explaining predictions 48 https://www.imec-int.com/en/what-we-offer/research-portfolio/discrete health
RECOMMENDE R ALGORITHMS MACHINE LEARNING INTERACTIVE DASHBOARDS SMART ALERTS RICH CARE PLANS OPEN IoT ARCHITECTURE Explaining predictions health
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
Evaluation  12 nurses used the app for three months  Data collection  Interaction logs  Resque questions  Semi-structured interviews 51
 12 nurses during 3 months 52
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
54
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
Why do users (dis)like the explanation? 56
Textual or visual? 57 health
Study design 58
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
Results  Participants with low NFC have a better perception of hybrid explanations  Participants with high NFC have a better perception of unimodal explanations 60
61 Data-centric explanations Health
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).
Next steps: model steering 63
Steering Approaches Steering approaches 64 Feature Selection Feature filtering
Steering Approaches 65 Awareness through: • Scores and measures • Visualizations • Issue description Steering approaches
66 Next steps: Fair AI FinTech - InsurTech
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/
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
Peter BrusliovskyNava Tintarev Cristina Conati Denis Parra Collaborations Bart KnijnenburgJurgen Ziegler
Questions? katrien.verbert@cs.kuleuven.be @katrien_v Thank you! https://augment.cs.kuleuven.be/

More Related Content

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
  • 12. Results 12 The interaction effect between NFC (divided into 4 quartiles Q1-Q4) and interfaces in terms of 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
  • 23. Study design 23 Evaluation @KU Leuven Monitoraat N = 12 6 Experts (4F, 2M) 6 Laymen (1F, 5M) Evaluation @ESPOL (Ecuador) N = 14 8 Experts (3F, 5M) 6 Laymen (6M)
  • 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
  • 30. 30
  • 32. Final evaluation 32 66 job seekers (age 33.9 ± 9.5, 18F) 8 Training Programs, 4 Groups, 1 Hour. 1 2 3 4 5 6 7 8 ResQue Questionnaire + two open questions. Users explored the tool freely. All interactions were logged.
  • 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.
  • 39. AHMoSe Visual Encodings 39 Model Explanations (SHAP) Model + Knowledge Summary
  • 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
  • 49. RECOMMENDE R ALGORITHMS MACHINE LEARNING INTERACTIVE DASHBOARDS SMART ALERTS RICH CARE PLANS OPEN IoT ARCHITECTURE Explaining predictions health
  • 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
  • 52.  12 nurses during 3 months 52
  • 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
  • 54. 54
  • 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
  • 56. Why do users (dis)like the explanation? 56
  • 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).
  • 63. Next steps: model steering 63
  • 65. Steering Approaches 65 Awareness through: • Scores and measures • Visualizations • Issue description Steering approaches
  • 66. 66 Next steps: Fair AI FinTech - InsurTech
  • 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
  • 69. Peter BrusliovskyNava Tintarev Cristina Conati Denis Parra Collaborations Bart KnijnenburgJurgen Ziegler

Editor's Notes

  1. 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)
  2. “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?