Robots are increasingly used in a variety of work environments, but surprisingly little attention has been paid to how robots change work. In this comparative case study, we explore how robotization changed the work design of order pickers and order packers in eight logistic warehouses. We found that all warehouses robotized tasks based on technological functionality to increase efficiency, which sometimes created jobs consisting of ‘left-over tasks’. Only two warehouses used a bottom-up approach, where employees were involved in the implementation and quality of work was considered important. Although the other warehouses did not, sometimes their work design still benefitted from robotization. The positive effects we identified are reduced physical and cognitive demands and opportunities for upskilling. Warehouses that lack attention to the quality of work may risk ending up with the negative effects for employees, such as simplification and intensification of work, and reduced autonomy. We propose that understanding the consequences of robots on work design supports HR professionals to help managing this transition by both giving relevant input on a strategic level about the importance of work design and advocating for employees and their involvement.
This article discusses the viability of a feminist constructivist approach of knowledge through the careful reading of the work of the feminist scholar and historian of science and technology, Donna Haraway. Haraway proposes an interpretation of objectivity in terms of "situated knowledges". Both the subject and the object of knowledge are endowed with the status of material-semiotic actors. By blurring the epistemological boundary between subject and object, Haraway's narratives about scientific discourse become populated with hybrid subjects/objects. The author argues that the ethics of these hybrid subjects consists of an uneasy mixture of a Nietzschean and a socialist-Christian ethic. The article concludes by setting out why Haraway's project constitutes an interesting effort to fuse postmodern insights and feminist commitments.
Sarah Banks (2012) describes ethics work mainly as the effort people put into developing themselves as good practitioners. She discerns six aspects of ethics work: identity work, framing work, reason work, emotion work, role work and performance work. Although ethics work focuses on the ethical development of individual practitioners within their profession, the concept and all its aspects can be transferred into an ethical guideline for the collective development of practitioners in interprofessional cooperation. As such the concept of collective interprofessional ethics work can also be used as a set of criteria for the ethical evaluation of interprofessional cooperation, as is shown on the basis of an experiment in Belgium
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
The IMPULS-2020 project DIGIREAL (BUas, 2021) aims to significantly strengthen BUAS’ Research and Development (R&D) on Digital Realities for the benefit of innovation in our sectoral industries. The project will furthermore help BUas to position itself in the emerging innovation ecosystems on Human Interaction, AI and Interactive Technologies. The pandemic has had a tremendous negative impact on BUas industrial sectors of research: Tourism, Leisure and Events, Hospitality and Facility, Built Environment and Logistics. Our partner industries are in great need of innovative responses to the crises. Data, AI combined with Interactive and Immersive Technologies (Games, VR/AR) can provide a partial solution, in line with the key-enabling technologies of the Smart Industry agenda. DIGIREAL builds upon our well-established expertise and capacity in entertainment and serious games and digital media (VR/AR). It furthermore strengthens our initial plans to venture into Data and Applied AI. Digital Realities offer great opportunities for sectoral industry research and innovation, such as experience measurement in Leisure and Hospitality, data-driven decision-making for (sustainable) tourism, geo-data simulations for Logistics and Digital Twins for Spatial Planning. Although BUas already has successful R&D projects in these areas, the synergy can and should significantly be improved. We propose a coherent one-year Impuls funded package to develop (in 2021): 1. A multi-year R&D program on Digital Realities, that leads to, 2. Strategic R&D proposals, in particular a SPRONG/sleuteltechnologie proposal; 3. Partnerships in the regional and national innovation ecosystem, in particular Mind Labs and Data Development Lab (DDL); 4. A shared Digital Realities Lab infrastructure, in particular hardware/software/peopleware for Augmented and Mixed Reality; 5. Leadership, support and operational capacity to achieve and support the above. The proposal presents a work program and management structure, with external partners in an advisory role.
The ELSA AI lab Northern Netherlands (ELSA-NN) is committed to the promotion of healthy living, working and ageing. By investigating cultural, ethical, legal, socio-political, and psychological aspects of the use of AI in different decision-makingcontexts and integrating this knowledge into an online ELSA tool, ELSA-NN aims to contribute to knowledge about trustworthy human-centric AI and development and implementation of health technology innovations, including AI, in theNorthern region.The research in ELSA-NN will focus on developing and mapping ELSA knowledge around three general concepts of importance for the development, monitoring and implementation of trustworthy and human-centric AI: availability, use,and performance. These concepts will be explored in two lines of research: 1) use case research investigating the use of different AI applications with different types of data in different decision-making contexts at different time periods duringthe life course, and 2) an exploration among stakeholders in the Northern region of needs, knowledge, (digital) health literacy, attitudes and values concerning the use of AI in decision-making for healthy living, working and ageing. Specificfocus will be on investigating low social economic status (SES) perspectives, since health disparities between high and low SES groups are growing world-wide, including in the Northern region and existing health inequalities may increase with theintroduction and use of innovative health technologies such as AI.ELSA-NN will be integrated within the AI hub Northern-Netherlands, the Health Technology Research & Innovation Cluster (HTRIC) and the Data Science Center in Health (DASH). They offer a solid base and infrastructure for the ELSA-NNconsortium, which will be extended with additional partners, especially patient/citizens, private, governmental and researchrepresentatives, to have a quadruple-helix consortium. ELSA-NN will be set-up as a learning health system in which much attention will be paid to dialogue, communication and education.
