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The purpose of this study is to offer a roadmap for work on the ethical and societal implications of algorithms and AI. Based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems in Korea, this work conducts socioecological evaluations of the governance of algorithmic transparency and accountability.

This paper analyzes algorithm design and development from critical socioecological angles: social, technological, cultural and industrial phenomena that represent the strategic interaction among people, technology and society, touching on sensitive issues of a legal, a cultural and an ethical nature.

Algorithm technologies are a part of a social ecosystem, and its development should be based on user interests and rights within a social and cultural milieu. An algorithm represents an interrelated, multilayered ecosystem of networks, protocols, applications, services, practices and users.

Value-sensitive algorithm design is proposed as a novel approach for designing algorithms. As algorithms have become a constitutive technology that shapes human life, it is essential to be aware of the value-ladenness of algorithm development. Human values and social issues can be reflected in an algorithm design.

The arguments in this study help ensure the legitimacy and effectiveness of algorithms. This study provides insight into the challenges and opportunities of algorithms through the lens of a socioecological analysis: political discourse, social dynamics and technological choices inherent in the development of algorithm-based ecology.

With the conceptualization of the blockchain as a socio-technical assemblage, this study aims to critically examine the blockchain initiatives in Korea in terms of the opportunities, risks and challenges embedded in their development.

This paper analyzes blockchain design and development from socioecological views: social, technological and cultural phenomena that represent the strategic interaction among people, technology and society. The qualitative data were collected from a variety of sources and diverse means.

The results imply that blockchain needs a close socio-technical examination to avoid simplistic assumptions of its promises and pitfalls. The development of blockchains in Korea will need to consider a range of socio-technical issues to facilitate the best outcomes for blockchain in society.

Despite proactive drives, new risks, such as security, privacy and transparency, emerge as critical concerns of the social implications of the blockchain and their impact on the new digital environment. Questions are raised as to how to govern blockchains, and how to address the unexpected outcomes that such a policy choice may have on society and industry.

The sociopolitical implications of Korean blockchains are examined to identify key concerns and issues as the country progresses rapidly toward a blockchain-driven society.

As algorithms permeate nearly every aspect of digital life, artificial intelligence (AI) systems exert a growing influence on human behavior in the digital milieu. Despite its popularity, little is known about the roles and effects of algorithmic literacy (AL) on user acceptance. The purpose of this study is to contextualize AL in the AI environment by empirically examining the role of AL in developing users' information processing in algorithms. The authors analyze how users engage with over-the-top (OTT) platforms, what awareness the user has of the algorithmic platform and how awareness of AL may impact their interaction with these systems.

This study employed multiple-group equivalence methods to compare two group invariance and the hypotheses concerning differences in the effects of AL. The method examined how AL helps users to envisage, understand and work with algorithms, depending on their understanding of the control of the information flow embedded within them.

Our findings clarify what functions AL plays in the adoption of OTT platforms and how users experience algorithms, particularly in contexts where AI is used in OTT algorithms to provide personalized recommendations. The results point to the heuristic functions of AL in connection with its ties in trust and ensuing attitude and behavior. Heuristic processes using AL strongly affect the credibility of recommendations and the way users understand the accuracy and personalization of results. The authors argue that critical assessment of AL must be understood not just about how it is used to evaluate the trust of service, but also regarding how it is performatively related in the modeling of algorithmic personalization.

The relation of AL and trust in an algorithm lends strategic direction in developing user-centered algorithms in OTT contexts. As the AI industry has faced decreasing credibility, the role of user trust will surely give insights on credibility and trust in algorithms. To better understand how to cultivate a sense of literacy regarding algorithm consumption, the AI industry could provide examples of what positive engagement with algorithm platforms looks like.

User cognitive processes of AL provide conceptual frameworks for algorithm services and a practical guideline for the design of OTT services. Framing the cognitive process of AL in reference to trust has made relevant contributions to the ongoing debate surrounding algorithms and literacy. While the topic of AL is widely recognized, empirical evidence on the effects of AL is relatively rare, particularly from the user's behavioral perspective. No formal theoretical model of algorithmic decision-making based on the dual processing model has been researched.