The Role of Interactive Visualization in Fostering Trust in AI

Beauxis-Aussalet, Emma; Behrisch, Michael; Borgo, Rita; Chau, Duen Horng; Collins, Christopher; Ebert, David S.; El‐Assady, Mennatallah; Endert, Alex; Keim, Daniel A.; Kohlhammer, Jörn; Oelke, Daniela; Peltonen, Jaakko; Riveiro, Maria; Schreck, Tobias; Strobelt, Hendrik; Wijk, Jarke J. van; Rhyne, Theresa-Marie

doi:10.1109/mcg.2021.3107875

Cited by 23 publications

(6 citation statements)

References 9 publications

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“…Medical data analysis trustworthiness issues prevent data summaries of specific patients from different healthcare provider databases. Medical data analysis makes sharing medical data between healthcare providers easy [20,21]. Since medical databases are linked directly to a healthcare provider or regional boundaries, it is difficult to locate patient records from other medical data.…”

Section: Discussionmentioning

confidence: 99%

“…Successful solutions to selection-based problems are achieved through fuzzy MCDM [19,20]. Since it can manage the information that is examined using a multi-resource linguistic and computational decision-making challenge and suggestions that are varied, the approach has been adopted by a number of researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study looks at how trustworthiness is measured in visualization tools for medical data using the classifications described in the next sections. This research uses the unified fuzzy Analytic Hierarchy Process-Technique for Ordering Performance by Similarity to an Ideal Solution (AHP-TOPSIS), which is a well-known method for making decisions based on more than one factor [15][16][17][18][19][20][21]. Many authors and practitioners have used the AHP method to judge how well hybrid decision support systems work based on the value of one parameter or the value of another high-precision parameter.…”

Section: Introductionmentioning

confidence: 99%

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Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Albarrak

2023

Diagnostics

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Medical data, such as electronic health records, are a repository for a patient’s medical records for use in the diagnosis of different diseases. Using medical data for individual patient care raises a number of concerns, including trustworthiness in data management, privacy, and patient data security. The introduction of visual analytics, a computing system that integrates analytics approaches with interactive visualizations, can potentially deal with information overload concerns in medical data. The practice of assessing the trustworthiness of visual analytics tools or applications using factors that affect medical data analysis is known as trustworthiness evaluation for medical data. It has a variety of major issues, such as a lack of important evaluation of medical data, the need to process much of medical data for diagnosis, the need to make trustworthy relationships clear, and the expectation that it will be automated. Decision-making strategies have been utilized in this evaluation process to avoid these concerns and intelligently and automatically analyze the trustworthiness of the visual analytics tool. The literature study found no hybrid decision support system for visual analytics tool trustworthiness in medical data diagnosis. Thus, this research develops a hybrid decision support system to assess and improve the trustworthiness of medical data for visual analytics tools using fuzzy decision systems. This study examined the trustworthiness of decision systems using visual analytics tools for medical data for the diagnosis of diseases. The hybrid multi-criteria decision-making-based decision support model, based on the analytic hierarchy process and sorting preferences by similarity to ideal solutions in a fuzzy environment, was employed in this study. The results were compared to highly correlated accuracy tests. In conclusion, we highlight the benefits of our proposed study, which includes performing a comparison analysis on the recommended models and some existing models in order to demonstrate the applicability of an optimal decision in real-world environments. In addition, we present a graphical interpretation of the proposed endeavor in order to demonstrate the coherence and effectiveness of our methodology. This research will also help medical experts select, evaluate, and rank the best visual analytics tools for medical data.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Albarrak

2023

Diagnostics

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“…Tools for Supporting Error Discovery. Interactive visualization is a powerful method for helping ML developers explore and interpret their models [8,42]. While many visualizations have been built to help practitioners evaluate models over time, one area of recent work has focused on designing and developing analytic tools for ML error discovery [e.g.…”

Section: Visualizationmentioning

confidence: 99%

Angler: Helping Machine Translation Practitioners Prioritize Model Improvements

Robertson

Wang

Moritz

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Angler enables ML developers to easily explore and curate challenge sets for machine translation. (A) The Table View lists all challenge sets, allowing users to compare them by metrics such as sample count, model performance, and familiarity score. After selecting a set, (B) the Detail View allows users to further explore samples in this set across various dimensions. (B1) The Timeline enables users to query data samples by time. (B2) The Spotlight presents visualizations with linking and brushing to help users characterize the set from different angles. (B3) The Sentence List shows all selected data samples and allows users to further fine-tune before exporting this challenge set for downstream tasks.

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“…The results show that revitalizing village industry, optimizing village spatial organization, strengthening village cultural identity, building green ecological village, promoting village multifunctional collaborative transformation, and promoting village sustainable development are all effective strategies in rural poverty gov-ernance. Beauxis-Aussalet et al [10] used data visualization technology to perform interactive analysis of artificial intelligence system, and finally found that data visualization technology is very important to the development of artificial intelligence. Qiao et al [11] proposed to integrate the resource advantages of small watersheds into rural industrial development, and transform the economic and social benefits contained in the ecological environment into multiscale spatial benefits among farmers, villages, and regional rural areas of sustainable development.…”

Section: Research Status Of Data Visualization Governance Inmentioning

confidence: 99%

Collaborative Governance of Rural Relative Poverty under Blockchain and Back Propagation Neural Network

Tian

Zhijun

2022

Advances in Multimedia

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It aims at exploring the effect of coordinated relative poverty governance in rural areas based on blockchain and back propagation (BP) neural network, so that the living standard of rural areas can be significantly improved. In view of the shortcomings and defects existing in the rural relative poverty governance, artificial intelligence technologies such as blockchain and neural network are innovatively introduced to conduct intelligent storage and data visualization analysis on the rural relative poverty governance process. A visualized governance and mechanism innovation model of rural relative poverty data based on blockchain and BP neural network is constructed. Finally, its performance is analyzed through experiments. The results show that the accuracy of data visualization prediction of the proposed algorithm model reaches 94.31%. When the amount of data is 5.5 Mb, the calculation consumption of the proposed algorithm is 0.08 s, and the average leakage rate finally stabilizes below 10%. Therefore, the constructed poverty data visualization governance model can achieve good data transmission security performance while ensuring high classification accuracy. It can provide experimental basis and contribution for the intelligent development and mechanism innovation of rural relative poverty governance.

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The Role of Interactive Visualization in Fostering Trust in AI

Cited by 23 publications

References 9 publications

Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Determining a Trustworthy Application for Medical Data Visualizations through a Knowledge-Based Fuzzy Expert System

Angler: Helping Machine Translation Practitioners Prioritize Model Improvements

Collaborative Governance of Rural Relative Poverty under Blockchain and Back Propagation Neural Network

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