Trustworthy Explanations for Knowledge Discovered from E-Health Records

In the current era of big data, very large amounts of data are generating at a rapid rate from a wide variety of rich data sources. Electronic health (e-health) records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. Embedded in the big data are valuable information and knowledge that can be discovered by data science, data mining and machine learning techniques. Many of these techniques apply "opaque box"approaches to make accurate predictions. However, these techniques may not be crystal clear to the users. As the users not necessarily be able to clearly view the entire knowledge discovery (e.g., prediction) process, they may not easily trust the discovered knowledge (e.g., predictions). Hence, in this paper, we present a system for providing trustworthy explanations for knowledge discovered from e-health records. Specifically, our system provides users with global explanations for the important features among the records. It also provides users with local explanations for a particular record. Evaluation results on real-life e-health records show the practicality of our system in providing trustworthy explanations to knowledge discovered (e.g., accurate predictions made). © 2022 IEEE.

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients.

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does "trustworthy AI" mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic.

A privacy-preserving dialogue system based on argumentation

Dialogue systems are a class of increasingly popular AI-based solutions to support timely and interactive communication with users in many domains. Due to the apparent possibility of users disclosing their sensitive data when interacting with such systems, ensuring that the systems follow the relevant laws, regulations, and ethical principles should be of primary concern. In this context, we discuss the main open points regarding these aspects and propose an approach grounded on a computational argumentation framework. Our approach ensures that user data are managed according to data minimization, purpose limitation, and integrity. Moreover, it is endowed with the capability of providing motivations for the system responses to offer transparency and explainability. We illustrate the architecture using as a case study a COVID-19 vaccine information system, discuss its theoretical properties, and evaluate it empirically. © 2022 The Author(s)

Modeling Trust in COVID-19 Contact-Tracing Apps Using the Human-Computer Trust Scale: Online Survey Study.

BACKGROUND: The COVID-19 pandemic has caused changes in technology use worldwide, both socially and economically. This pandemic crisis has brought additional measures such as contact-tracing apps (CTAs) to help fight against spread of the virus. Unfortunately, the low adoption rate of these apps affected their success. There could be many reasons for the low adoption, including concerns of security and privacy, along with reported issues of trust in CTAs. Some concerns are related with how CTAs could be used as surveillance tools or their potential threats to privacy as they involve health data. For example, in Estonia, the CTA named HOIA had approximately 250,000 downloads in the middle of January 2021. However, in 2021, only 4.7% of the population used HOIA as a COVID-19 CTA. The reasons for the low adoption include lack of competency, and privacy and security concerns. This lower adoption and the lack of trustworthiness persist despite efforts of the European Union in building ethics and trustworthy artificial intelligence (AI)-based apps. OBJECTIVE: The aim of this study was to understand how to measure trust in health technologies. Specifically, we assessed the usefulness of the Human-Computer Trust Scale (HCTS) to measure Estonians' trust in the HOIA app and the causes for this lack of trust. METHODS: The main research question was: Can the HCTS be used to assess citizens' perception of trust in health technologies? We established four hypotheses that were tested with a survey. We used a convenience sample for data collection, including sharing the questionnaire on social network sites and using the snowball method to reach all potential HOIA users in the Estonian population. RESULTS: Among the 78 respondents, 61 had downloaded the HOIA app with data on usage patterns. However, 20 of those who downloaded the app admitted that it was never opened despite most claiming to regularly use mobile apps. The main reasons included not understanding how it works, and privacy and security concerns. Significant correlations were found between participants' trust in CTAs in general and their perceived trust in the HOIA app regarding three attributes: competency (P<.001), risk perception (P<.001), and reciprocity (P=.01). CONCLUSIONS: This study shows that trust in the HOIA app among Estonian residents did affect their predisposition to use the app. Participants did not generally believe that HOIA could help to control the spread of the virus. The result of this work is limited to HOIA and health apps that use similar contact-tracing methods. However, the findings can contribute to gaining a broader understanding and awareness of the need for designing trustworthy technologies. Moreover, this work can help to provide design recommendations that ensure trustworthiness in CTAs, and the ability of AI to use highly sensitive data and serve society.