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Ethical Design of a Robot Platform for Disabled Employees: Some Practical Methodological Considerations.

ABSTRACT: This paper explains the process of developing a scenario involving the use of a robotic platform to enhance the work experience of disabled employees. We outline the challenges involved in revealing the potential unintended consequences of introducing elements of Artificial Intelligence, automation, and robotics into a socially and ethically complex and potentially fragile scenario, and the practical challenges involved in giving a voice to vulnerable users throughout the design process. While an ideal case scenario would involve the disabled employees as much as possible directly in the design process, this can, realistically, be a challenge. In this paper, we detail a methodological and analytic approach that is centered around ethnography and design fictions. It is designed to provide a deeper understanding of all the stakeholders involved in the scenario while encouraging ethical reflection. Based on our findings, we argue that, while it is relatively easy to adopt an a priori ethical stance through notions such as inclusivity and accessibility, there are risks involved in making such a priori prescriptions with respect to the perspectives of different stakeholders in an applied research project. More specifically, we highlight the importance of understanding the broad organizational and bureaucratic characteristics of a business or workplace when devising HRI scenarios and tasks, and of considering elements such as business models, operating philosophy, and organizational hierarchies in the design process.

SUBMITTER: Colombino T

PROVIDER: S-EPMC8417308 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMuch research is being carried out using publicly available Twitter data in the field of public health, but the types of research questions that these data are being used to answer and the extent to which these projects require ethical oversight are not clear.ObjectiveThis review describes the current state of public health research using Twitter data in terms of methods and research questions, geographic focus, and ethical considerations including obtaining informed consent from Twitter handlers.MethodsWe implemented a systematic review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, of articles published between January 2006 and October 31, 2019, using Twitter data in secondary analyses for public health research, which were found using standardized search criteria on SocINDEX, PsycINFO, and PubMed. Studies were excluded when using Twitter for primary data collection, such as for study recruitment or as part of a dissemination intervention.ResultsWe identified 367 articles that met eligibility criteria. Infectious disease (n=80, 22%) and substance use (n=66, 18%) were the most common topics for these studies, and sentiment mining (n=227, 62%), surveillance (n=224, 61%), and thematic exploration (n=217, 59%) were the most common methodologies employed. Approximately one-third of articles had a global or worldwide geographic focus; another one-third focused on the United States. The majority (n=222, 60%) of articles used a native Twitter application programming interface, and a significant amount of the remainder (n=102, 28%) used a third-party application programming interface. Only one-third (n=119, 32%) of studies sought ethical approval from an institutional review board, while 17% of them (n=62) included identifying information on Twitter users or tweets and 36% of them (n=131) attempted to anonymize identifiers. Most studies (n=272, 79%) included a discussion on the validity of the measures and reliability of coding (70% for interreliability of human coding and 70% for computer algorithm checks), but less attention was paid to the sampling frame, and what underlying population the sample represented.ConclusionsTwitter data may be useful in public health research, given its access to publicly available information. However, studies should exercise greater caution in considering the data sources, accession method, and external validity of the sampling frame. Further, an ethical framework is necessary to help guide future research in this area, especially when individual, identifiable Twitter users and tweets are shared and discussed.Trial registrationPROSPERO CRD42020148170; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=148170.

Project description:ObjectivePredicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units.ApproachGiven the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 h of scanning from ten healthy young adults.Main resultsFollowing a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state-of-the-art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints.SignificanceOur hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.

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Ethical Design of a Robot Platform for Disabled Employees: Some Practical Methodological Considerations.

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