scholarly journals Drivers’ Age and Automated Vehicle Explanations

2021 ◽  
Vol 13 (4) ◽  
pp. 1948
Author(s):  
Qiaoning Zhang ◽  
Xi Jessie Yang ◽  
Lionel P. Robert
Keyword(s):  
Decision Making ◽  
Middle Age ◽  
Age Groups ◽  
Design Experiment ◽  
Mixed Design ◽  
Automated Vehicles ◽  
Automated Vehicle ◽  
Automated Decision Making

Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results highlight both commonalities and differences between age groups. These results have important implications in designing AV explanations and promoting trust.

AUTO-ODGOVORNOST U SLUČAJU AUTOMATSKOG IZBORA

2021 ◽  
Author(s):  
Sara Landini ◽  
Keyword(s):  
Decision Making ◽  
Civil Liability ◽  
Legal Liability ◽  
Point Of View ◽  
Decision Making Process ◽  
Human Errors ◽  
Automated Vehicle ◽  
Causal Nexus ◽  
Automated Decision Making

Automation has an important role in the reduction of human errors, but what about in case of losses caused by an automated vehicle? Who is liable? Th is paper addresses the issue of automation coverage for costs in the event of damage caused by an automated decision-making process. It will consider civil liability and insurance from the point of view of problems related to the proof of a causal nexus between wrongdoing and losses. Th e thesis that the paper proposes is that legal liability is not a suffi cient instrument to permit eff ective prevention and compensation in the case of damage caused by full algorithmic automation.

scholarly journals Legal and Technical Feasibility of the GDPR’s Quest for Explanation of Algorithmic Decisions: of Black Boxes, White Boxes and Fata Morganas

2020 ◽  
Vol 11 (1) ◽  
pp. 18-50 ◽  
Author(s):  
Maja BRKAN ◽  
Grégory BONNET
Keyword(s):  
Decision Making ◽  
Interdisciplinary Approach ◽  
Practical Implementation ◽  
General Data Protection Regulation ◽  
Legal Limits ◽  
Black Boxes ◽  
General Data ◽  
The Subject ◽  
The Right ◽  
Automated Decision Making

Understanding of the causes and correlations for algorithmic decisions is currently one of the major challenges of computer science, addressed under an umbrella term “explainable AI (XAI)”. Being able to explain an AI-based system may help to make algorithmic decisions more satisfying and acceptable, to better control and update AI-based systems in case of failure, to build more accurate models, and to discover new knowledge directly or indirectly. On the legal side, the question whether the General Data Protection Regulation (GDPR) provides data subjects with the right to explanation in case of automated decision-making has equally been the subject of a heated doctrinal debate. While arguing that the right to explanation in the GDPR should be a result of interpretative analysis of several GDPR provisions jointly, the authors move this debate forward by discussing the technical and legal feasibility of the explanation of algorithmic decisions. Legal limits, in particular the secrecy of algorithms, as well as technical obstacles could potentially obstruct the practical implementation of this right. By adopting an interdisciplinary approach, the authors explore not only whether it is possible to translate the EU legal requirements for an explanation into the actual machine learning decision-making, but also whether those limitations can shape the way the legal right is used in practice.

scholarly journals Validation of a French Version of the Career Decision-Making Difficulties Questionnaire: Relationships With Self-Esteem and Self-Efficacy

2021 ◽  
pp. 089484532110099
Author(s):  
Jérôme Rossier ◽  
Shékina Rochat ◽  
Laurent Sovet ◽  
Jean-Luc Bernaud
Keyword(s):  
Decision Making ◽  
Career Counseling ◽  
Self Efficacy ◽  
Age Groups ◽  
Career Decision ◽  
Self Esteem ◽  
Career Decision Making ◽  
Student Sample ◽  
French Version ◽  
The Relationship

The aim of this study was to validate the French version of the Career Decision-Making Difficulties Questionnaire (CDDQ) and to assess its measurement invariance across gender, age groups, countries, and student versus career counseling samples. We also examined the sensitivity of this instrument to discriminate a career counseling population from a general student sample. Third, we studied the relationship between career decision-making difficulties, career decision-making self-efficacy, and self-esteem in a sample of 1,748 French and French-speaking Swiss participants. A confirmatory factor analysis confirmed the overall hierarchical structure of the CDDQ. Multigroup analysis indicated that the level of invariance across groups almost always reached configural, metric, and scalar invariance. Differences between countries were very small, whereas differences between the general population and career counseling subsamples were much larger. Both self-esteem and self-efficacy significantly predicted career decision-making difficulties. Moreover, as expected, self-efficacy partially mediated the relationship between self-esteem and career decision-making difficulties.

scholarly journals National Surveillance of Injury in the Republic of Korea: Increased Injury Vulnerability in the Late Middle Age

2021 ◽  
Vol 18 (3) ◽  
pp. 1210
Author(s):  
Hansol Chang ◽  
Ji Young Min ◽  
Dajeong Yoo ◽  
Se Uk Lee ◽  
Sung Yeon Hwang ◽  
...  
Keyword(s):  
Middle Age ◽  
Age Groups ◽  
Admission Rate ◽  
Age Group ◽  
National Surveillance ◽  
Early Prevention ◽  
Group 4 ◽  
Group 3 ◽  
Prevention Methods ◽  
Injury Outcomes

Surveillance of injury patterns and comparisons among different age groups help develop a better understanding of recent injury trends and early prevention. This study conducted a national surveillance of injury by age group. Data were collected retrospectively from Emergency Department-Based Injury In-Depth Surveillance (EDIIS) in South Korea, between January 2011 and December 2017. Patients were divided into the following four groups by age: Group 1–18 to 34 years, Group 2–35 to 49 years, Group 3–50 to 64 years, and Group 4—≥65 years. A total of 1,221,746 patients were included in the study. Findings revealed that, each year, the injury rate increased in the population aged ≥65 years. The place and mechanism of injury in Group 3 were similar to those in younger age groups, while injury outcomes and injured body parts were similar to those in Group 4. Further, hospital admission rate, ICU admission rate, hospital death, traumatic brain injury, and injury severity increased with an increase in age. In our study, each age group showed diverse characteristics pertaining to the mechanism, place, time, and outcomes of injuries. Interestingly, Group 3, which represented the late middle age, exhibited increased vulnerability to injury, and emerged as a gray zone between the young and old age groups. Therefore, different injury prevention methods are needed for each age group. Specifically, early prevention methods need to be implemented from the late middle age to improve the old age group’s injury outcomes.

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