The design and testing of a tool for developing responsible innovation in start-up enterprises

Long, Thomas B.; Blok, Vincent; Dorrestijn, Steven; Macnaghten, Phil

doi:10.1080/23299460.2019.1608785

Cited by 57 publications

(32 citation statements)

References 36 publications

(56 reference statements)

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“…Bringing frameworks and practices for responsible innovation into settings for which they were not originally designed continues to be a theme in the third research article, as Long et al (2019) tackle the question of 'how to introduce responsible innovation to real-world, competitive, industry settings.' In response to this challenge, and in hopes of developing 'usable' practices that can help install responsible innovation, the authors develop and test a means by which innovation managers can systematically identify social and ethical issues.…”

Section: Research Articlesmentioning

confidence: 99%

Reinventing responsible innovation

Fisher

2020

Journal of Responsible Innovation

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Section: Research Articlesmentioning

confidence: 99%

Reinventing responsible innovation

Fisher

2020

Journal of Responsible Innovation

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“…Function creep: is the use of techniques for unforeseen and different purposes than the developers aimed for. [7] Privacy: "The right of research subjects to safeguard their integrity must always be respected. Every precaution should be taken to respect the privacy of the subject, the confidentiality of the patient's information and to minimize the impact of the study on the subject's physical and mental integrity and on the personality of the subject."…”

Section: Autonomymentioning

confidence: 99%

“…impact that cannot be quantified) of technology before it is fully functional [4,5]. It helps to anticipate possible adverse effects with ethical implications and therefore allows for more socially responsible innovation [6,7]. As ML-DSTs are being developed for use in health care settings, it seems appropriate to refer to the four pre-dominating biomedical ethical principles presented by Beauchamp and Childress [8]: respect for autonomy, justice, beneficence and non-maleficence.…”

Section: Introductionmentioning

confidence: 99%

Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving Periodic Workers’ Health Assessments

et al. 2020

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Purpose Computer algorithms and Machine Learning (ML) will be integrated into clinical decision support within occupational health care. This will change the interaction between health care professionals and their clients, with unknown consequences. The aim of this study was to explore ethical considerations and potential consequences of using ML based decision support tools (DSTs) in the context of occupational health. Methods We conducted an ethical deliberation. This was supported by a narrative literature review of publications about ML and DSTs in occupational health and by an assessment of the potential impact of ML-DSTs according to frameworks from medical ethics and philosophy of technology. We introduce a hypothetical clinical scenario from a workers' health assessment to reflect on biomedical ethical principles: respect for autonomy, beneficence, non-maleficence and justice. Results Respect for autonomy is affected by uncertainty about what future consequences the worker is consenting to as a result of the fluctuating nature of ML-DSTs and validity evidence used to inform the worker. A beneficent advisory process is influenced because the three elements of evidence based practice are affected through use of a ML-DST. The principle of non-maleficence is challenged by the balance between group-level benefits and individual harm, the vulnerability of the worker in the occupational context, and the possibility of function creep. Justice might be empowered when the ML-DST is valid, but profiling and discrimination are potential risks. Conclusions Implications of ethical considerations have been described for the socially responsible design of ML-DSTs. Three recommendations were provided to minimize undesirable adverse effects of the development and implementation of ML-DSTs.

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“…These elements demand specific tools and mechanisms of interaction for the management of multidisciplinary research agendas [43] with multi-sectoral impacts (e.g., [20]). In this perspective, an effective RRI process should "help scientists and innovators to identify four dimensions in their activities: anticipation, reflexivity, inclusion, and responsiveness" [44].…”

Section: Background: Responsible Research and Innovationmentioning

confidence: 99%

Experimenting with RRI tools to Drive Sustainable Agri-Food Research: The SASS Case Study from Sub-Saharan Africa

et al. 2020

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The need to develop experimental tools for a responsible research and innovation (RRI) framework is relevant for managing research agendas and policy making that seriously take into account the complex conditions of innovation development (linked to multidisciplinarity and interaction processes) between the researchers and their fieldwork activities. The adoption of an RRI framework is even more important for multidisciplinary and complex issues, such as the agri-food system. In this context, the SASS (Sustainable Agri-food Systems for Sustainable Development (SASS) project represents a good example for verifying the application of the RRI strategy in a varied research group committed to the development of sustainable agri-food systems in Sub-Saharan Africa. The project, which involves more than 50 researchers from different fields of knowledge and theoretical backgrounds, showed the importance of the processes of reflection, re-driving, and convergence in the definition of research objectives and strategies. This process started by experimenting with new dedicated RRI tools in order to allow interactions between the researchers, including exchanging their experience in data collection and theoretical reflection development. With respect to this analysis, it was interesting to analyze how the RRI tools and strategies have been activated between researchers and different stakeholders, generating reflections capable of re-adapting the results towards shared and accessible innovation for the extended society. Following the discussion based on the description of the SASS-RRI agenda tools and following an internal verification given from an RRI-based web survey, this contribution provides new insights, in terms of tools and strategies, to promote and refine RRI approaches. This work underlines how RRI methods have promoted internal and external interactions to connect the research objectives towards a model of open innovation.

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The design and testing of a tool for developing responsible innovation in start-up enterprises

Abstract: The design and testing of a tool for developing responsible innovation in start-up enterprises,

Cited by 57 publications

References 36 publications

Reinventing responsible innovation

Reinventing responsible innovation

Ethical Considerations of Using Machine Learning for Decision Support in Occupational Health: An Example Involving Periodic Workers’ Health Assessments

Experimenting with RRI tools to Drive Sustainable Agri-Food Research: The SASS Case Study from Sub-Saharan Africa

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