Theoretical, Measured and Subjective Responsibility in Aided Decision Making

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. A bias existed only when participants with poor classification capabilities relied less-than-optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not assure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations

Human-centric zero-defect manufacturing: State-of-the-art review, perspectives, and challenges

Zero defect manufacturing (ZDM) aims at eliminating defects throughout the value stream as well as the cost of rework and scrap. The ambitious goal of zero defects requires the extensive utilization of emerging technologies. Amidst the major drive for technological advancement, humans are often kept out of the loop because they are perceived as the root cause of error. The report from the European Commission on Industry 5.0 emphasizes that human-centric is a key pillar in building a more resilient industry and is vital to incorporate the human component into the manufacturing sector. However, we did not find any publications that explain what human-centric ZDM is, nor what the roles of humans are in advancing ZDM. As a contribution to bridging this gap, a systematic literature review is conducted using different databases. We collected 36 publications and categorised them into 3 different human roles which are managers, engineers, and operators. From our search, we found out that managers play a vital role in cultivating ZDM in the entire organization to prevent errors despite the fact they often do not have direct contact with the production line as operators. Operators can help advance ZDM through knowledge capturing with feedback functions to the engineer to better design a corrective action to prevent errors. Assistive technologies such as extended reality are efficient tools used by operators to eliminate human errors in production environments. Human-centric is now a goal in the future manufacturing sector, but it could face barriers such as high technological investments and resistance to changes in their work tasks. This paper can contribute to paving the roadmap of human-centric ZDM to bring defects to zero and reposition the manufacturing sector to become more resilient.publishedVersio