Table 1. Summary of key findings and... | Emerald Publishing

Table 1.

Summary of key findings and proposed service sector-specific guidelines

Key findings related to what hinders AI startup’s role in the SSD of the service sector	Proposed service sector-specific guidelines for the ethical development of beneficial AI	Key factors influencing AI startups’ ability to contribute to the SSD of the service sector
No apparent need and system for AI ethics • AI startup members do not have enough resources to conduct impact assessments • Their entrepreneurial methodologies do not encourage formal planning, which prevents the identification of ethical pitfalls • Having more robust AI and driving profit is often more important than acting • When AI solutions are not compromising the well-being of human beings, e.g. customer service AI chatbots, no ethical guidelines are needed	Do not underestimate your AI solutions, implement basic impact assessments • Despite how unharmful the AI system may seem, assessments must be done from the early stages of the project and periodically during the implementation to identify all the stakeholders and the implications of the AI system on decent and meaningful jobs • This basic assessment would not need funds; it can look as simple as a feedback survey or quarterly meetings to assess the implementation. This entails audit processes to assess not only the impact but the algorithms and data • The aim is to apply such assessments systematically to secure constant monitoring as well as to register and justify all decisions made	Awareness of socioeconomic issues
Issues with technology adoption and change management in service settings • Despite AI startups developing a product with specific features, the way it is implemented might not necessarily promote decent work • Change management is relevant to fostering decent work in the service sector. Service employees might not want their routine and monotonous tasks to be automated • AI startups’ role in fostering decent work can be hampered by how technology is implemented in practice	Proactively engage with service providers to augment service employees’ capabilities • AI startups should be involved in the implementation of AI technology, not just the design and development of it • Collaborate with the service providers to improve the task portfolio of service jobs, i.e. promote less physically and mentally demanding tasks • Build AI solutions that do not automate the tasks that are identified as enhancers of meaningful jobs for service employees; for this, it is necessary to empathize with them. Consider reducing labor intensity and inadequate employment practices or poor working conditions • For all this, it is imperative to have a committed collaboration with the AI startups’ clients, which are the service providers. Collaboration is key	Fostering decent work
Assuming which service tasks are best to automate without engaging with all stakeholders • AI startups showed a strong opinion on which tasks should be automated: the routinized and mundane. They emphasized that if it is repetitive and takes a lot of time, people will not consider the task meaningful • AI systems designers might not be empowered to make decisions related to ethics, because it is believed that only the middle and senior management should be thinking about social sustainability • AI startups need to take responsibility internally, but they also need support externally from different actors to ensure that everybody is playing their role accordingly	Work as a multistakeholder team and use inclusive design methodologies • AI startup members must design the AI systems and decide which tasks to substitute by using methodologies such as codesign, speculative design or design thinking • Using such methodologies will ensure the consideration of all stakeholders throughout the startup’s life cycle, from the advent of the first value proposition to scale-up and potential exit • With a cross-disciplinary collaborative approach, AI startups can engage with end-users and indirect users of their solutions and, as a result, design more meaningful service jobs • AI startups can collaborate with research partners to complement their expertise, e.g. with researchers related to service management and human resources	Systematically applying ethics
	Involve the team in the real service setting • Promote the immersion of the AI startup team into the real environment of the service provider to have a firsthand experience of how the service is carried out. This could create more empathy and understanding of what the real struggles are, leading to creating more meaningful jobs and more successful solutions to real problems • Support this immersion with design methodologies	Systematically applying ethics
AI startups’ focus is on how to get more investment • AI startup members consider sustainability issues challenging because they often lack tangible results • Being socially responsible cannot be the main value proposition. Instead, the main advantage of AI is to have more effective and cheap processes	Reframe sustainability in the business model to make it attractive for investors • Find the best way to tackle social impact issues in the business model innovation process by being aware of all the benefits that it brings for the startup’s development; e.g. proposing sustainable solutions is a competitive advantage because both investors and clients could see the ethical standards, policies and compliance metrics, which would in turn help each stakeholder earn trust in a way that competitors cannot • Consider social impact from the ideation and conceptualization of the key business model elements to be able to demonstrate how social impact can create revenue	Business model innovation

Key findings related to what hinders AI startup’s role in the SSD of the service sector	Proposed service sector-specific guidelines for the ethical development of beneficial AI	Key factors influencing AI startups’ ability to contribute to the SSD of the service sector
No apparent need and system for AI ethics• AI startup members do not have enough resources to conduct impact assessments• Their entrepreneurial methodologies do not encourage formal planning, which prevents the identification of ethical pitfalls• Having more robust AI and driving profit is often more important than acting• When AI solutions are not compromising the well-being of human beings, e.g. customer service AI chatbots, no ethical guidelines are needed	Do not underestimate your AI solutions, implement basic impact assessments• Despite how unharmful the AI system may seem, assessments must be done from the early stages of the project and periodically during the implementation to identify all the stakeholders and the implications of the AI system on decent and meaningful jobs• This basic assessment would not need funds; it can look as simple as a feedback survey or quarterly meetings to assess the implementation. This entails audit processes to assess not only the impact but the algorithms and data• The aim is to apply such assessments systematically to secure constant monitoring as well as to register and justify all decisions made	Awareness of socioeconomic issues
Issues with technology adoption and change management in service settings• Despite AI startups developing a product with specific features, the way it is implemented might not necessarily promote decent work• Change management is relevant to fostering decent work in the service sector. Service employees might not want their routine and monotonous tasks to be automated• AI startups’ role in fostering decent work can be hampered by how technology is implemented in practice	Proactively engage with service providers to augment service employees’ capabilities• AI startups should be involved in the implementation of AI technology, not just the design and development of it• Collaborate with the service providers to improve the task portfolio of service jobs, i.e. promote less physically and mentally demanding tasks• Build AI solutions that do not automate the tasks that are identified as enhancers of meaningful jobs for service employees; for this, it is necessary to empathize with them. Consider reducing labor intensity and inadequate employment practices or poor working conditions• For all this, it is imperative to have a committed collaboration with the AI startups’ clients, which are the service providers. Collaboration is key	Fostering decent work
Assuming which service tasks are best to automate without engaging with all stakeholders• AI startups showed a strong opinion on which tasks should be automated: the routinized and mundane. They emphasized that if it is repetitive and takes a lot of time, people will not consider the task meaningful• AI systems designers might not be empowered to make decisions related to ethics, because it is believed that only the middle and senior management should be thinking about social sustainability• AI startups need to take responsibility internally, but they also need support externally from different actors to ensure that everybody is playing their role accordingly	Work as a multistakeholder team and use inclusive design methodologies• AI startup members must design the AI systems and decide which tasks to substitute by using methodologies such as codesign, speculative design or design thinking• Using such methodologies will ensure the consideration of all stakeholders throughout the startup’s life cycle, from the advent of the first value proposition to scale-up and potential exit• With a cross-disciplinary collaborative approach, AI startups can engage with end-users and indirect users of their solutions and, as a result, design more meaningful service jobs• AI startups can collaborate with research partners to complement their expertise, e.g. with researchers related to service management and human resources	Systematically applying ethics
	Involve the team in the real service setting• Promote the immersion of the AI startup team into the real environment of the service provider to have a firsthand experience of how the service is carried out. This could create more empathy and understanding of what the real struggles are, leading to creating more meaningful jobs and more successful solutions to real problems• Support this immersion with design methodologies	Systematically applying ethics
AI startups’ focus is on how to get more investment• AI startup members consider sustainability issues challenging because they often lack tangible results• Being socially responsible cannot be the main value proposition. Instead, the main advantage of AI is to have more effective and cheap processes	Reframe sustainability in the business model to make it attractive for investors• Find the best way to tackle social impact issues in the business model innovation process by being aware of all the benefits that it brings for the startup’s development; e.g. proposing sustainable solutions is a competitive advantage because both investors and clients could see the ethical standards, policies and compliance metrics, which would in turn help each stakeholder earn trust in a way that competitors cannot• Consider social impact from the ideation and conceptualization of the key business model elements to be able to demonstrate how social impact can create revenue	Business model innovation