Fairness and Explanation in AI-Informed Decision Making

AI-assisted decision-making that impacts individuals raises critical questions about transparency and fairness in artificial intelligence (AI). Much research has highlighted the reciprocal relationships between the transparency/explanation and fairness in AI-assisted decision-making. Thus, considering their impact on user trust or perceived fairness simultaneously benefits responsible use of socio-technical AI systems, but currently receives little attention. In this paper, we investigate the effects of AI explanations and fairness on human-AI trust and perceived fairness, respectively, in specific AI-based decision-making scenarios. A user study simulating AI-assisted decision-making in two health insurance and medical treatment decision-making scenarios provided important insights. Due to the global pandemic and restrictions thereof, the user studies were conducted as online surveys. From the participant’s trust perspective, fairness was found to affect user trust only under the condition of a low fairness level, with the low fairness level reducing user trust. However, adding explanations helped users increase their trust in AI-assisted decision-making. From the perspective of perceived fairness, our work found that low levels of introduced fairness decreased users’ perceptions of fairness, while high levels of introduced fairness increased users’ perceptions of fairness. The addition of explanations definitely increased the perception of fairness. Furthermore, we found that application scenarios influenced trust and perceptions of fairness. The results show that the use of AI explanations and fairness statements in AI applications is complex: we need to consider not only the type of explanations and the degree of fairness introduced, but also the scenarios in which AI-assisted decision-making is used.</jats:p

Total Observed Organic Carbon (TOOC): A synthesis of North American observations

Measurements of organic carbon compounds in both the gas and particle phases measured upwind, over and downwind of North America are synthesized to examine the total observed organic carbon (TOOC) over this region. These include measurements made aboard the NOAA WP-3 and BAe-146 aircraft, the NOAA research vessel Ronald H. Brown, and at the Thompson Farm and Chebogue Point surface sites during the summer 2004 ICARTT campaign. Both winter and summer 2002 measurements during the Pittsburgh Air Quality Study are also included. Lastly, the spring 2002 observations at Trinidad Head, CA, surface measurements made in March 2006 in Mexico City and coincidentally aboard the C-130 aircraft during the MILAGRO campaign and later during the IMPEX campaign off the northwestern United States are incorporated. Concentrations of TOOC in these datasets span more than two orders of magnitude. The daytime mean TOOC ranges from 4.0 to 456 μgC m^−3 from the cleanest site (Trinidad Head) to the most polluted (Mexico City). Organic aerosol makes up 3–17% of this mean TOOC, with highest fractions reported over the northeastern United States, where organic aerosol can comprise up to 50% of TOOC. Carbon monoxide concentrations explain 46 to 86% of the variability in TOOC, with highest TOOC/CO slopes in regions with fresh anthropogenic influence, where we also expect the highest degree of mass closure for TOOC. Correlation with isoprene, formaldehyde, methyl vinyl ketene and methacrolein also indicates that biogenic activity contributes substantially to the variability of TOOC, yet these tracers of biogenic oxidation sources do not explain the variability in organic aerosol observed over North America. We highlight the critical need to develop measurement techniques to routinely detect total gas phase VOCs, and to deploy comprehensive suites of TOOC instruments in diverse environments to quantify the ambient evolution of organic carbon from source to sink

Second Austrian Assessment Report on Climate Change (AAR2)

The Second Austrian Assessment Report (AAR2) will provide a comprehensive and inter-disciplinary synthesis of the scientific evidence on climate change in Austria. It will inform policymakers and society at large about synergies and trade-offs of alternative mitigation options and adaptation strategies. The report will be written by more than 150 authors representing the entire Austrian scientific community from all relevant fields and disciplines

Towards Mapping Competencies through Learning Analytics: Real-time Competency Assessment for Career Direction through Interactive Simulation

Assessment and Evaluation in Higher Education, 201, pp. 1-13

Instructional multimedia: An investigation of student and instructor attitudes and student study behavior

<p>Abstract</p> <p>Background</p> <p>Educators in allied health and medical education programs utilize instructional multimedia to facilitate psychomotor skill acquisition in students. This study examines the effects of instructional multimedia on student and instructor attitudes and student study behavior.</p> <p>Methods</p> <p>Subjects consisted of 45 student physical therapists from two universities. Two skill sets were taught during the course of the study. Skill set one consisted of knee examination techniques and skill set two consisted of ankle/foot examination techniques. For each skill set, subjects were randomly assigned to either a control group or an experimental group. The control group was taught with live demonstration of the examination skills, while the experimental group was taught using multimedia. A cross-over design was utilized so that subjects in the control group for skill set one served as the experimental group for skill set two, and vice versa. During the last week of the study, students and instructors completed written questionnaires to assess attitude toward teaching methods, and students answered questions regarding study behavior.</p> <p>Results</p> <p>There were no differences between the two instructional groups in attitudes, but students in the experimental group for skill set two reported greater study time alone compared to other groups.</p> <p>Conclusions</p> <p>Multimedia provides an efficient method to teach psychomotor skills to students entering the health professions. Both students and instructors identified advantages and disadvantages for both instructional techniques. Reponses relative to instructional multimedia emphasized efficiency, processing level, autonomy, and detail of instruction compared to live presentation. Students and instructors identified conflicting views of instructional detail and control of the content.</p

Scenario-based requirements elicitation for user-centric explainable AI

Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder’s requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenario-based design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection

PainDroid: An android-based virtual reality application for pain assessment

Earlier studies in the field of pain research suggest that little efficient intervention currently exists in response to the exponential increase in the prevalence of pain. In this paper, we present an Android application (PainDroid) with multimodal functionality that could be enhanced with Virtual Reality (VR) technology, which has been designed for the purpose of improving the assessment of this notoriously difficult medical concern. Pain- Droid has been evaluated for its usability and acceptability with a pilot group of potential users and clinicians, with initial results suggesting that it can be an effective and usable tool for improving the assessment of pain. Participant experiences indicated that the application was easy to use and the potential of the application was similarly appreciated by the clinicians involved in the evaluation. Our findings may be of considerable interest to healthcare providers, policy makers, and other parties that might be actively involved in the area of pain and VR research

Persistent topology for natural data analysis - A survey

Natural data offer a hard challenge to data analysis. One set of tools is being developed by several teams to face this difficult task: Persistent topology. After a brief introduction to this theory, some applications to the analysis and classification of cells, lesions, music pieces, gait, oil and gas reservoirs, cyclones, galaxies, bones, brain connections, languages, handwritten and gestured letters are shown