Suchergebnisse

In this study, an investigation was conducted into the extent to which ergonomic requirements for work on computers are met in Greek office workstations. The ergonomic requirements considered were those included in Council Directive 90/270/EEC (1990) of the European Union and the ISO 9241 (1997) standard. 593 office workstations were assessed using an assessment tool consisting of 70 assessment points. The assessment results show that the ergonomic requirements that are independent of the specific characteristics of individual work spaces and environments (e.g., design standards for seats, monitors, and input devices) are adequately met. Ergonomic requirements that should take into consideration the specific characteristics and constraints of individual work content, work spaces, and environments (e.g., requirements dealing with workplace layout, environmental conditions, software, and work organization) are inadequately met. Based on these results, issues are indicated on the effort ergonomists should focus to improve working conditions in computerized offices.

Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues.

AstraZeneca ; European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.) ; Wellcome Trust (102696 and 206194)