AI management an exploratory survey of the influence of GDPR and FAT principles

As organisations increasingly adopt AI technologies, a number of ethical issues arise. Much research focuses on algorithmic bias, but there are other important concerns arising from the new uses of data and the introduction of technologies which may impact individuals. This paper examines the interplay between AI, Data Protection and FAT (Fairness, Accountability and Transparency) principles. We review the potential impact of the GDPR and consider the importance of the management of AI adoption. A survey of data protection experts is presented, the initial analysis of which provides some early insights into the praxis of AI in operational contexts. The findings indicate that organisations are not fully compliant with the GDPR, and that there is limited understanding of the relevance of FAT principles as AI is introduced. Those organisations which demonstrate greater GDPR compliance are likely to take a more cautious, risk-based approach to the introduction of AI

A Review of Best Practices for Monitoring and Improving Inpatient Pediatric Patient Experiences.

ContextAchieving high-quality patient-centered care requires assessing patient and family experiences to identify opportunities for improvement. With the Child Hospital Consumer Assessment of Healthcare Providers and Systems Survey, hospitals can assess performance and make national comparisons of inpatient pediatric experiences. However, using patient and family experience data to improve care remains a challenge.ObjectiveWe reviewed the literature on best practices for monitoring performance and undertaking activities aimed at improving pediatric patient and family experiences of inpatient care.Data sourcesWe searched PubMed, Cumulative Index to Nursing and Allied Health Literature, and PsychINFO.Study selectionWe included (1) English-language peer-reviewed articles published from January 2000 to April 2019; (2) articles based in the United States, United Kingdom, or Canada; (3) articles focused on pediatric inpatient care; (4) articles describing pediatric patient and family experiences; and (5) articles including content on activities aimed at improving patient and family experiences. Our review included 25 articles.Data extractionTwo researchers reviewed the full article and abstracted specific information: country, study aims, setting, design, methods, results, Quality Improvement (QI) initiatives performed, internal reporting description, best practices, lessons learned, barriers, facilitators and study implications for clinical practice, patient-experience data collection, and QI activities. We noted themes across samples and care settings.ResultsWe identified 10 themes of best practice. The 4 most common were (1) use evidence-based approaches, (2) maintain an internal system that communicates information and performance on patient and family experiences to staff and hospital leadership, (3) use experience survey data to initiate and/or evaluate QI interventions, and (4) identify optimal times (eg, discharge) and modes (eg, print) for obtaining patient and family feedback. These correspond to adult inpatient best practices.ConclusionsBoth pediatric and adult inpatient best practices rely on common principles of culture change (such as evidence-based clinical practice), collaborative learning, multidisciplinary teamwork, and building and/or supporting a QI infrastructure that requires time, money, collaboration, data tracking, and monitoring. QI best practices in both pediatric and adult inpatient settings commonly rely on identifying drivers of overall ratings of care, rewarding staff for successful implementation, and creating easy-to-use and easy-to-access planning and QI tools for staff

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups