In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024. However, most diagnoses are non-lethal and deemed clinically insignificant which means that the patient will likely not be impacted by the cancer over their lifetime. As a result, numerous research studies have explored the accuracy of predicting clinical significance of prostate cancer based on magnetic resonance imaging (MRI) modalities and deep neural networks. Despite their high performance, these models are not trusted by most clinical scientists as they are trained solely on a single modality whereas clinical scientists often use multiple magnetic resonance imaging modalities during their diagnosis. In this paper, we investigate combining multiple MRI modalities to train a deep learning model to enhance trust in the models for clinically significant prostate cancer prediction. The promising performance and proposed training pipeline showcase the benefits of incorporating multiple MRI modalities for enhanced trust and accuracy.

The deployment of autonomous agents in environments involving human interaction has increasingly raised security concerns. Consequently, understanding the circumstances behind an event becomes critical, requiring the development of capabilities to justify their behaviors to non-expert users. Such explanations are essential in enhancing trustworthiness and safety, acting as a preventive measure against failures, errors, and misunderstandings. Additionally, they contribute to improving communication, bridging the gap between the agent and the user, thereby improving the effectiveness of their interactions. This work presents an accountability and explainability architecture implemented for ROS-based mobile robots. The proposed solution consists of two main components. Firstly, a black box-like element to provide accountability, featuring anti-tampering properties achieved through blockchain technology. Secondly, a component in charge of generating natural language explanations by harnessing the capabilities of Large Language Models (LLMs) over the data contained within the previously mentioned black box. The study evaluates the performance of our solution in three different scenarios, each involving autonomous agent navigation functionalities. This evaluation includes a thorough examination of accountability and explainability metrics, demonstrating the effectiveness of our approach in using accountable data from robot actions to obtain coherent, accurate and understandable explanations, even when facing challenges inherent in the use of autonomous agents in real-world scenarios.

Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.

Today, publicity around highly touted but underperforming AI solutions has placed the health sector at risk for another AI winter. To respond to this challenge, we propose that industry organizations consider implementing self-governance standards to better mitigate risks and encourage greater trust in AI capabilities. Building on the National Academy of Medicine's AI implementation lifecycle, we created a detailed organizational framework that identifies 10 groups of AI risks and 14 groups of mitigation practices across the four lifecycle phases. AI developers, implementers, and other stakeholders can use this analysis to guide collective, voluntary actions to select, establish, and track adherence to trust-enhancing AI standards. Without industry self-governance, government agencies may act to institute their own compliance requirements. However, industries that have proactively defined, adopted, and implemented standards complementary to government regulation have reduced the urgency of public-sector action while allowing for the appropriate use of available resources.

Two of the biggest names in biometrics have issued a joint White Paper detailing the benefits of AI-driven face authentication. The paper, entitled "Enhancing Trust with AI-Driven Biometrics", is jointly presented by FaceTec and Jumio, ID verification and authentication specialists leading the current digital onboarding trend with remote enrollment, face authentication and biometric liveness technology. Artificial Intelligence plays an enormously important role in their solutions, and accordingly, it's a major focus of the paper. Available as an e-book, the paper builds on the idea of the "trust anchor" explored in the previously published White Paper "Trusted Identity From Start to Finish": essentially, the idea is that strong authentication starts with the foundation of an anchor document that provides reliable identity assurance. Establishing a link between the end user's face and this anchor – a passport or a driver's license, for example – allows for the creation of a "trust chain" in which authentication can reliably be performed at any point going forward.

There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments.

There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments. Auditability, explainability, transparency and replicability (reproducibility) are often suggested as means of avoiding bias.