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Explainable AI for non-expert users
- 1. Explainable AI for non-expert users APEx-UI 2022 - 21 March 2022 Katrien Verbert Augment/HCI - KU Leuven @katrien_v towards the next generation of interactive and adaptive explanation methods Towards the next generation of interactive and adaptive explanation methods
- 2. Human-Computer Interaction group Explainable AI - recommender systems – visualization – intelligent user interfaces Learning analytics & human resources Media consumption Precision agriculture Healthcare Augment Katrien Verbert ARIA Adalberto Simeone Computer Graphics Phil Dutré LIIR Sien Moens E-media Vero Vanden Abeele Luc Geurts Kathrin Gerling
- 3. Augment/HCI team Robin De Croon Postdoc researcher Katrien Verbert Associate Professor Francisco Gutiérrez Postdoc researcher Tom Broos PhD researcher Nyi Nyi Htun Postdoc researcher Houda Lamqaddam PhD researcher Oscar Alvarado Postdoc researcher http://augment.cs.kuleuven.be/ Diego Rojo Carcia PhD researcher Maxwell Szymanski PhD researcher Arno Vanneste PhD researcher Jeroen Ooge PhD researcher Aditya Bhattacharya PhD researcher Ivania Donoso Guzmán PhD researcher
- 4. Explaining model outcomes to increase user trust and acceptance Enable users to interact with the explanation process to improve the model Research objectives Models
- 5. 5
- 6. Collaborative filtering – Content-based filtering Knowledge-based filtering - Hybrid Recommendation techniques
- 11. Explanations 11 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.
- 12. 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 12
- 13. User study 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
- 14. Results 14 The interaction effect between NFC (divided into 4 quartiles Q1-Q4) and interfaces in terms of confidence
- 15. 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. 15
- 16. 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
- 17. 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.
- 18. Different levels of user control 18 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.
- 19. 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
- 20. Evaluation method 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]
- 21. 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 21 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.
- 23. Learning analytics Src: Steve Schoettler
- 25. Explaining exercise recommendations How to automatically adapt the exercise recommending on Wiski to the level of students? How do (placebo) explanations affect initial trust in Wiski for recommending exercises? Goals and research questions Automatic adaptation Explanations & trust Young target audience Middle and high school students Ooge, J., Kato, S., Verbert, K. (2022) Explaining Recommendations in E-Learning: Effects on Adolescents' Initial Trust. Proceedings of the 27th IUI conference on Intelligent User Interfaces
- 26. Results: Real explanations… … did increase multidimensional initial trust … did not increase one-dimensional initial trust … led to accepting more recommended exercises compared to both placebo and no explanations
- 27. Results: Placebo explanations… … did not increase initial trust compared to no explanations … may undermine perceived integrity … are a useful baseline: • how critical are students towards explanations? • how much transparency do students need?
- 28. Results: No explanations Can be acceptable in low-stakes situations (e.g., drilling exercises): indications of difficulty level might suffice Personal level indication: Easy, Medium and Hard tags
- 29. Learning analytics Src: Steve Schoettler
- 30. 30 uncertainty 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
- 31. Evaluation method 31 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)
- 32. Results LADA was perceived as a valuable tool for more accurate and efficient decision making. LADA enables expert advisers to evaluate significantly more scenarios. More transparency in the prediction model is required in order to increase trust. 32 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
- 35. 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.
- 36. AHMoSe Visual Encodings 36 Model Explanations (SHAP) Model + Knowledge Summary
- 37. Case Study – Grape Quality Prediction 37 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]
- 38. Simulation Study AHMoSe vs full AutoML approach to support model selection. 38 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%
- 39. Qualitative Evaluation 10 open ended questions 5 viticulture experts and 4 ML experts. Thematic Analysis: potential use cases, trust, usability, and understandability.
- 40. Qualitative Evaluation - Trust 40 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
- 42. Designing for interacting with predictions for finding jobs 42
- 43. Predicting duration to find a job 43 Key Issues: Missing data, prediction trust issues, job seeker motivation, lack of control.
- 44. Methods A Customer Journey approach. (5 mediators). Hands-on time with the original dashboard (22 mediators). Observations of mediation sessions. (3 mediators, 6 job seekers). Questionnaire regarding perception of the dashboard and prediction model (15 Mediators). 44 Charleer S., Gutiérrez Hernández F., Verbert K. (2018). 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, Los Angeles, USA.
- 45. 45
- 46. Take away messages Key difference between actionable and non-actionable parameters Need for customization and contextualization. The human expert plays a crucial role when interpreting and relaying in the predicted or recommended output. 46 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, Los Angeles, USA. (Core: A)
- 48. 48 Healthcare
- 49. 49 Ooge, J., Stiglic, G., & Verbert, K. (2021). Explaining artificial intelligence with visual analytics in healthcare. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1427. https://doi.org/10.1002/widm.1427
- 50. https://www.jmir.org/2021/6/e18035 Nutrition Nutrition advice (7) Diets (7) Recipes (7) Menus (2) Fruit (1) Restaurants (1) Doctors (4) Hospital (5) Thread / fora (3) Self-Diagnosis (3) Healthcare information (5) Similar users (2) Advise for children (2) General health information Routes (2) Physical activity (10) Leisure activity (2) Wellbeing motivation (2) Behaviour (7) Wearable devices (1) Tailored messages (2) Routes (2) Physical activity (10) Leisure activity (2) Behaviour (7) Lifestyle Specific health conditions Health Recommende r Systems
- 51. Recommender systems for food 51
- 53. Design and Evaluation 53 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).
- 54. Design 54 Gutiérrez Hernández F., Htun NN., Charleer S., De Croon R., Verbert K. (2018). Designing augmented reality applications for personal health decision-making. In: Proceedings of the 2019 52nd Hawaii International Conference on System Sciences Presented at the HICSS, Hawaii, 07 Jan 2019-11 Jan 2019.
- 55. Design 55
- 56. Evaluation method Within Subjects n = 28 (1F, 27M) Ages from 22 to 38 (M = 25.81, SD = 4.57) Post-Questionnaires TAM (Technology Acceptance) NASA-TLX (Task Load Index) 56 Gutiérrez Hernández F., Htun NN., Charleer S., De Croon R., Verbert K. (2018). Designing augmented reality applications for personal health decision-making. In: Proceedings of the 2019 52nd Hawaii International Conference on System Sciences Presented at the HICSS, Hawaii, 07 Jan 2019-11 Jan 2019.
- 57. Results PHARA allows users to make informed decisions, and resulted in selecting healthier food products. Stack layout performs better with HMD devices with a limited field of view, like the HoloLens, at the cost of some affordances. The grid and pie layouts performed better in handheld devices, allowing to explore with more confidence, enjoyability and less effort. 57 Gutiérrez Hernández F., Htun NN., Charleer S., De Croon R., Verbert K. (2018). Designing augmented reality applications for personal health decision-making. In: Proceedings of the 2019 52nd Hawaii International Conference on System Sciences Presented at the HICSS, Hawaii, 07 Jan 2019-11 Jan 2019.
- 59. RECOMMENDE R ALGORITHMS MACHINE LEARNING INTERACTIVE DASHBOARDS SMART ALERTS RICH CARE PLANS OPEN IoT ARCHITECTURE
- 60. User centered design approach 60
- 61. 61 Gutiérrez Hernández, F.S., Htun, N.N., Vanden Abeele, V., De Croon, R., Verbert, K. (2022). Explaining call recommendations in nursing homes: a user- centered design approach for interacting with knowledge-based health decision support systems. IUI 2022.
- 62. Evaluation 12 nurses used the app for three months Data collection Interaction logs Resque questions Semi-structured interviews 62
- 63. 12 nurses during 3 months 63
- 64. 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. 64
- 65. Ongoing work 65
- 66. 66
- 67. 67 Explaining health recommendations Word cloud Feature importance Feature importance+ % Maxwell Szymanski,Vero Vanden Abeele and Katrien Verbert Explaininghealthrecommendationstolayusers: Thedosand don’ts – Apex-IUI2022
- 68. Biofortification info Plants to cultivate PERNUG Increased access to more nutritious plants Improved iron and B12 intakes for vegan and vegetarian subgroups Consumer app with recipe recommendations Hydroponic system with biofortified plants https://www.eitfood.eu/projects/pernug
- 70. Petal-X Explaining cardiovascular disease (CVD) risk predictions to patients.
- 71. 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 71
- 72. Peter Brusliovsky NavaTintarev CristinaConati Denis Parra Collaborations Bart Knijnenburg Jurgen Ziegler
Notas del editor
- We focus specifically on visual analytics for non-expert users. Non-expert users are defined as users that have little knowledge of data processing and analysis. We research two algorithmic foundations: predictions models like regression and clustering and recommender systems that suggest items to users. The key objective is to communicate the uncertainty of these models to support decision-making and increase trust. We do it through the use of visualization techniques that explain the models.
- Amazon.com gebruikt een collaborative filtering techniek: zoekt gelijkenissen tussen gebruikers en gaat dan op basis van wat gelijkaardige gebruikers kopen aanbevelingen doen.
- The procedure contains the following steps: \begin{enumerate} \item \textit{Tutorial of study} - Participants were invited to read the description of the user study and to choose a scenario for generating a play-list. Then, they were asked to watch a task tutorial. Only the features of the particular setting were shown in this video. The ``Start'' button of the study was only activated after finishing the tutorial. Users logged in with their Spotify accounts to our experimental system, so that our recommenders could leverage the Spotify API and user listening history to generate ``real'' recommendations. \item \textit{Pre-study questionnaire} - This questionnaire collects user demographics and measures user's personal characteristics such as musical sophistication and visual memory capacity. %and their trust in recommender systems. The visual memory capacity is measured by ``Corsi block-tapping test''. In the test, a number of tiles are highlighted one at a time, and participants are asked to select the tiles in the correct order afterward. The number of highlighted tiles increases until the user makes too many errors. In Experiments 1 and 3, we used a test with a more sophisticated implementation of the Corsi test~\footnote{\url{https://www.humanbenchmark.com/tests/memory}, accessed June 2018}, which allows us to better distinguish participants by the level of visual memory capacity. In Experiment 2, to control the workload of participants in the within-subjects design, we chose a simple version of the Corsi test~\footnote{\url{http://www.psytoolkit.org/experiment-library/corsi.html}, accessed June 2018} for measuring visual short-term memory. \end{itemize} \item \textit{Manipulating Recommender and rating songs} - To ensure that participants spent enough time to explore recommendations, the questionnaire link was only activated after 10 minutes. After tweaking the recommender, participants were asked to rate the top-20 recommended songs that resulted from their interactions. \item \textit{Post-study questionnaire} - Participants were asked to evaluate the perceived quality, perceived accuracy, perceived diversity, satisfaction, effectiveness, and choice difficulty of the recommender system. After answering all the questions, participants were given opportunities to provide free-text comments of their opinions and suggestions about our recommender. \end{enumerate}
- Figure 2 shows that participants with low NFC are reporting a higher confident in their playlist with the explanations in- terface than in the baseline. Participants with a high NFC reported the opposite. Hence, the participants with low NFC have more confidence in the explanation interface than in the baseline, in contrast to user with low NFC. An explanation might be that low NFC participants benefited from the expla- nations because they did not spontaneously engage in much extra reasoning to justify the recommendations they received, and when they received the rational from the explanation this increased their confidence in their songs selection. Figure 2 also indicates that as the NFC increased, the con- fidence of participants in the playlist created in the baseline also increased. This result indicated that participants with a high NFC were more willing to understand their own musical preference in relation to the attributes of the recommended songs. This may have resulted in a higher confidence in their playlist. We did not see the same increase in trust as NFC increases in the explanation interface. As Figure 2 shows, the NFC scores in the third quartile were almost the same for both interfaces. At the highest NFC level, participants had a higher confidence in the baseline than in the explanation interface. The reduced confidence within the explanation interface could be an indication that users with a high NFC have less need for explanations.
- We employed a between-subjects study to investigate the effects of interactions among different user control on acceptance, perceived diversity, and cognitive load. We consider each of three user control components as a variable. By following the 2x2x2 factorial design we created eight experimental settings (Table~\ref{tab:table1}), which allows us to analyze three main effects, three two-way interactions, and one three-way interaction. We also investigate which specific \textit{personal characteristics} (musical sophistication, visual memory capacity) influence acceptance and perceived diversity. Each experimental setting is evaluated by a group of participants (N=30). Of note, to minimize the effects of UI layout, all settings have the same UI and disable the unsupported UI controls, e.g., graying out sliders. As shown in section ~\ref{evaluation questions}, we employed Knijnenburg et al.'s framework~\citep{knijnenburg2012explaining} to measure the six subjective factors, perceived quality, perceived diversity, perceived accuracy, effectiveness, satisfaction, and choice difficulty~\citep{knijnenburg2012explaining}. In addition, we measured cognitive load by using a classic cognitive load testing questionnaire, the NASA-TLX~\footnote{https://humansystems.arc.nasa.gov/groups/tlx}. It assesses the cognitive load on six aspects: mental demand, physical demand, temporal demand, performance, effort, and frustration. The procedure follows the design outlined in the general methodology (c.f., Section \ref{sec:general-procedure}). The \textit{experimental task} is to compose a play-list for the chosen scenario by interacting with the recommender system. Participants were presented with play-list style recommendations (Figure~\ref{fig:vis1}c). Conditions were altered on a between-subjects basis. Each participant was presented with only one setting of user control. For each setting, initial recommendations are generated based on the selected top three artists, top two tracks, and top one genre. According to the controls provided in a particular setting, participants were able to manipulate the recommendation process.
- Main effects: REC has lowest cgload and highest acceptance Two-way: All the settings that combine two control components do not lead to significantly higher cognitive load than using only one control component. combing multiple control components potentially increases acceptance without increasing cognitive load significantly. visual memory is not a significant factor that affects the cognitive load of controlling recommender systems. In other words, controlling the more advanced recommendation components in this study does not seem to demand a high visual memory. In addition, we did not find an effect of visual memory on acceptance (or perceived accuracy and quality). One possible explanation is that users with higher musical so- phistication are able to leverage different control components to explore songs, and this influences their perception of recommenda- tion quality, thereby accepting more songs. Our results show that the settings of user control significantly influence cognitive load and recommendation acceptance. We discuss the results by the main effects and interaction effects in a 2x2x2 factorial design. Moreover, we discuss how visual memory and musical sophistication affect cognitive load, perceived diversity, and recommendation acceptance. \subsubsection{Main effects} We discuss the main effects of three control components. Increased control level; from control of recommendations (REC) to user profile (PRO) to algorithm parameters (PAR); leads to higher cognitive load (see Figure \ref{fig:margin}c). The increased cognitive load, in turn, leads to lower interaction times. Compared to the control of algorithm parameters (PAR) or user profile (PRO), the control of recommendations (REC) introduces the least cognitive load and supports users in finding songs they like. We observe that most existing music recommender systems only allow users to manipulate the recommendation results, e.g., users provide feedback to a recommender through acceptance. However, the control of recommendations is a limited operation that does not allow users to understand or control the deep mechanism of recommendations. \subsubsection{Two-way interaction effects} Adding multiple controls allows us to improve on existing systems w.r.t. control, and do not necessarily result in higher cognitive load. Adding an additional control component to algorithm parameters increases the acceptance of recommended songs significantly. Interestingly, all the settings that combine two control components do \textit{not} lead to significantly higher cognitive load than using only one control component. We even find that users' cognitive load is significantly \textit{lower} for (PRO*PAR) than (PRO, PAR), which shows a benefit of combining user profile and algorithm parameters in user control. Moreover, combing multiple control components potentially increases acceptance without increasing cognitive load significantly. Arguably, it is beneficial to combine multiple control components in terms of acceptance and cognitive load. \subsubsection{Three-way interaction effects} The interaction of PRO*PAR*REC tends to increase acceptance (see Figure \ref{fig:margin}a), and it does not lead to higher cognitive load (see Figure \ref{fig:margin}c). Moreover, it also tends to increase interaction times and accuracy. Therefore, we may consider having three control components in a system. Consequently, we answer the research question. \textbf{RQ1}: \textit{The UI setting (user control, visualization, or both) has a significant effect on recommendation acceptance?} It seems that combining PAR with a second control component or combing three control components increases acceptance significantly. %KV: this paragraph refers to different RQs: either rephrase or omit? -SOLVED \subsubsection{Effects of personal characteristics} Having observed the trends across all users, we survey the difference in cognitive load and item acceptance due to personal characteristics. We study two kinds of characteristics: visual working memory and musical sophistication. \paragraph{Visual working memory} The SEM model suggests that visual memory is not a significant factor that affects the cognitive load of controlling recommender systems. The cognitive load for the type of controls used may not be strongly affected by individual differences in visual working memory. In other words, controlling the more advanced recommendation components in this study does not seem to demand a high visual memory. In addition, we did not find an effect of visual memory on acceptance (or perceived accuracy and quality). Finally, the question items for diversity did not converge in our model, so we are not able to make a conclusion about the influence of visual working memory on diversity. \paragraph{Musical sophistication} Our results imply that high musical sophistication allows users to perceive higher recommendation quality, and may thereby be more likely to accept recommended items. However, higher musical sophistication also increases choice difficulty, which may negatively influence acceptance. One possible explanation is that users with higher musical sophistication are able to leverage different control components to explore songs, and this influences their perception of recommendation quality, thereby accepting more songs. Finally, the question items for diversity did not converge in our model, so we are not able to make a conclusion about the influence of musical sophistication on diversity.
- As I have already explained, this is the LADA Dashboard that predicts the chance of success and presents a set of components that are intended to help the student adviser to give feedback to the student.
- We evaluated this dashboard with both laymen and experts. They used LADA based on real data of students to plan a semester for a student in two Conditions: Using the dashboard. Using the traditional system.
- Results indicate that the prediction models enables users to explore more possible scenarios, but more transparency is required. The quality indicator is insuffient to increase user trust
- The first application domain is agriculture Precision agriculture is an interesting domain to research the representation of data and uncertainty associated with both data and prediction models for non-expert users, such as farmers. This domain faces some typical challenges of Visual Analytics, missing data and uncertainty of predictions. In this work, we conducted a systematic review of visualisation techniques and the representation of uncertainty.
- The transcribed data were coded and analysed following the thematic analysis approach (Braun and Clarke, 2006), which resulted in four main themes: potential use cases, trust, usability, and understandability. Marimekko charts difficult
- Showing the dis/agreement of model outputs with expert’s knowledge can promote understandability and trust. ability to see dis/agreements between models' predictions and an expert's knowledge can help them inspect further and thus promote trust. u
- Job recommender systems have become a well researched area. In this dissertation, we designed and evaluated two interactive dashboards that can help explain the reasoning behind job recommendations and predictions. A first dashboard has been elaborated that explain predictions of the chance to find a job in a particular job area. The second dashboard explains job recommendations by showing competences and competence gaps instead of the typical matching score used by recommender systems.
- This is the first dashboard: We designed this dashboard on top of a prediction model to explain the inner workings to job mediators. We make a distinction between actionable and non-actionable parameters. Age is an example of a non-actionable parameter.
- We used a user-centered design methodology consisting of the steps listed on this slide.
- This is the first dashboard: We designed this dashboard on top of a prediction model to explain the inner workings to job mediators. We make a distinction between actionable and non-actionable parameters. Age is an example of a non-actionable parameter.
- Job mediators highlighted the importance of customising the dashboard to be able to control the message. Five mediators used negative parameters to support their message Two mediators removed negative parameters to avoid demotivation. “age can be demotivating” “too much information might be difficult to process” “would like to see an overview of everything” “depends on the job seeker” Our explanatory tool helps mediators to control the message they wish to convey depending on the situation context.
- Health is another interesting domain: here we try to provide relevant information to end-users that is trustworthy and has a positive impact on decision-making. We researched the representation of uncertainty of a prediction model that predicts the impact of a food product on weight as well as different layouts to present this data together with recommendations in an AR setting.
- We found that the stack visualisation performs better with HMD devices with a limited field of view, like the HoloLens, at the cost of some usability affordances (RQ4). For handheld devices, the grid and the pie tended to score higher in terms of confidence in decision making, compared to the list and stack layouts (RQ1, RQ4).
- 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)
- 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)
- We compared four different layouts to represent this information: a stack layout, a list layout, a grid layout and a pie layout. We compared their use in two implementations: one using the Microsoft HoloLens, and a second one using an Smartphone
- We evaluated these layouts in a user study with 28 participants in a lab setting. and measured both subjective and objective data collected from the use of our application,
- We found that the stack visualisation performs better with HMD devices with a limited field of view, like the HoloLens, at the cost of some usability affordances (RQ4). For handheld devices, the grid and the pie tended to score higher in terms of confidence in decision making, compared to the list and stack layouts (RQ1, RQ4).
- Q11: not mych effort Q2, Q5 During the interviews, some participants also appreciated the explanations of the system, indicating in particular the detailed view of the call, where they can see the most frequent reasons for a call: \say{\textit{\textbf{P8}: I like that you see the most frequent reasons. Why they call most often. That is the most important to me. That you can see it again afterward. Maybe, when a person calls a lot, that you can reflect. they go a lot to the toilet, maybe they have a urinary infection. When many nurses visit a resident, maybe you don’t see this if you don’t look at the overview.}} Overview of resuldents