Background: The continuous development of artificial intelligence (AI) and increasing rate of adoption by software startups calls for governance measures to be implemented at the design and development stages to help mitigate AI governance concerns. Most AI ethical design and development tools mainly rely on AI ethics principles as the primary governance and regulatory instrument for developing ethical AI that inform AI governance. However, AI ethics principles have been identified as insufficient for AI governance due to lack of information robustness, requiring the need for additional governance measures. Adaptive governance has been proposed to combine established governance practices with AI ethics principles for improved information and subsequent AI governance. Our study explores adaptive governance as a means to improve information robustness of AI ethical design and development tools. We combine information governance practices with AI ethics principles using ECCOLA, a tool for ethical AI software development at the early developmental stages.
Aim: How can ECCOLA improve its robustness by adapting it with GARP® IG practices?
Methods: We use ECCOLA as a case study and critically analyze its AI ethics principles with information governance practices of the Generally Accepted Recordkeeping principles (GARP®).
Results: We found that ECCOLA’s robustness can be improved by adapting it with Information governance practices of retention and disposal.
Conclusions: We propose an extension of ECCOLA by a new governance theme and card, # 21.
Advances in machine learning (ML) technologies have greatly improved Artificial Intelligence (AI) systems. As a result, AI systems have become ubiquitous, with their application prevalent in virtually all sectors. However, AI systems have prompted ethical concerns, especially as their usage crosses boundaries in sensitive areas such as healthcare, transportation, and security. As a result, users are calling for better AI governance practices in ethical AI systems. Therefore, AI development methods are encouraged to foster these practices. This research analyzes the ECCOLA method for developing ethical and trustworthy AI systems to determine if it enables AI governance in development processes through ethical practices. The results demonstrate that while ECCOLA fully facilitates AI governance in corporate governance practices in all its processes, some of its practices do not fully foster data governance and information governance practices. This indicates that the method can be further improved.
Technology designers and developers can be understood as social experience (SE) mediators. In user experience (UX), notions of SE have served to identify and define the factors contributing to human-technology interaction (HTI). Three dominant perspectives have been promoted in UX discourse: 1) SE of brand, brand value and consumer culture; 2) technology design as mediator of human-to-human interactions; and 3) meaning generation through action and interaction between actors. Symbolic interactionalism understands meaning as occurring through dialogue, in the construction of the social self, promoting selfreflection as a social construction. This theorisation of social experience is valuable in the context of HTI as it allows for greater insight into the immaterial dimensions of technology integration in human societies. The purpose of this paper is to break down the factors contributing to social emotional experience of technology through illustrating how it operates according to fashion -temporality and spatiality in culture. This is a theoretical paper that presents a review of social experience, social emotional and collective emotion based literature in light of fashion and design. The result is a presentation of a proposed fashion framework of social emotions in technology interaction design (FASHEM). Based on symbolic interactionism, FASHEM helps break down emotional technology experience into a matrix of self, other, design semiotic interactions.
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