Digital innovation offers significant societal, economic and environmental benefits but is also a source of profound harms. Prior information systems (IS) research has often overlooked the ethical tensions involved, framing harms as 'unintended consequences' rather than symptoms of deeper systemic problems. In response, this paper presents a problematization review that critiques and revises three widely held assumptions about digital innovations: (1) that they generate net benefits that outweigh the associated harm; (2) that their ethicality can be calculated through utility and (3) that their harms can be mitigated through technological, corporate or regulatory intervention. We argue that compassion provides a pluralistic ethical foundation that integrates the strengths of consequentialism, deontology, and virtue ethics. This framework prioritises serving all stakeholders, especially the most vulnerable, while avoiding harm. It sets a research agenda focused on addressing structural dysfunctions, amplifying marginalised voices, and fostering sustainable systems. By reimagining digital innovation as a force for the common good, this paper contributes to a more just and equitable digital future for all.

This study explores whether privacy literacy, self-efficacy, and concerns mediate the age-related effects on privacy decision behavior. To study privacy decision behavior, we designed an experiment that integrates both heuristic and cognitive manipulations in the decision scenario. 625 old and younger adults participated in the experiment and used our web-based application, “RecipeDigger.” The application recorded users’ privacy decision behavior in the form of accepting or rejecting cookies, which offered a personalized service. Our findings indicate that some of the differences in privacy decision-making between older and younger adults can be traced to having different levels of privacy literacy. Older and younger adults with higher privacy literacy can better align their privacy preferences with their disclosure behavior. By bridging the gap between psychological theories and privacy research, this study provides a comprehensive understanding of the factors influencing privacy decisions among older and younger adults, offering methodological, theoretical, and policy implications.

Multimodal hate speech detection targets offensive content expressed through combinations of modalities such as text and images, which often evade detection when analyzed separately. We introduce MAXplain, an interactive framework that addresses both issues via a configurable LLM-based multi-agent architecture. Specialized agents handle distinct subtasks and exchange information through structured dialogues, enabling intrinsic explainability and improved accuracy. The web interface supports human-in-the-loop interaction, including real-time adjustment of agent behaviors and evaluation rules. A browser plugin enables direct inspection of online content. While demonstrated for hate speech detection, MAX-plain also supports rapid prototyping for other multimodal tasks.

Echo Chambers and Information Cocoons have become the subject of a multifaceted academic debate – ranging from the proper conceptualization and delineation of related concepts, to questions about their prevalence and uniqueness in the online environment, to arguments about their societal impact and the role of digital technologies. This study presents a systematic literature review that analyzes the existing research to synthesize relevant findings and build the missing foundations of these phenomena. This study follows a hermeneutic analytical approach to the literature to clarify and model the distinction between information cocoons and echo chambers. Furthermore, we summarize the selected literature and identify existing knowledge gaps to outline future research opportunities.

Small-scale farmers often suffer from poverty due to their dependence on unjust practices in global supply chains. On-farm training could help them address this, but it often lacks scalability and reach. Technology-enhanced learning (TEL) can tackle this issue but risks creating secondary injustices, such as epistemic injustice, by neglecting marginalized users' unique needs, knowledge, and local context. This case study analyzes the process of co-designing a digital peer training model for marginalized users, in this case, cacao farmers in Nicaragua. The authors draw on comprehensive data from participatory action research, as well as facilitator conversations, surveys, informal discussions, and field observations. Based on the findings, the authors suggest a framework to mitigate the risks of introducing secondary injustices in TEL design through a critical digital pedagogy (CDP) lens.

We evaluate the use of the existing Microsoft Vale framework to guarantee correctness of low level Ethereum Virtual Machine (EVM) bytecode, while affording smart contract developers higher-level language and reasoning features. We encode EVM-R (a subset of EVM semantics and instruction set) into F*, and raise the EVM-R into Vale design-by-contract components in an intermediate language supporting conditional logic. The specifications of Vale procedures constructed from these verified EVM bytecodes carry integrity to the bytecode level, unlike current EVM compilers. Furthermore, raising the instruction set to Vale allows opportunity for refinement of the instructions, which we did ensuring safety properties of overflow protection, invalid memory access protection, and functional correctness. We demonstrate our contributions through two case study smart contracts, a simple casino, and a subcurrency coin.

Blockchain is an emerging technology that may fundamentally alter the way in which customers and companies interact. However, there is still uncertainty toward use cases in the distinct aspects of customer service. In this research, we draw on the Customer Service Life Cycle framework to conceptually understand how blockchain technology can reshape the various stages of the customer service relationship. We perform a multiple-case-study analysis on blockchain functionalities and challenges to abstract how blockchain technologies can enhance the customer service relationship. The findings highlight benefits and challenges of blockchain technologies during the requirements, acquisition, ownership, and retirement stages across industries.

Online extremism remains a persistent problem despite the best efforts of governments, tech companies and civil society. Digital technologies can induce group polarization to promote extremism and cause substantial changes to extremism (e.g., create new forms of extremism, types of threats or radicalization approaches). Current methods to counter extremism induce undesirable side-effects (e.g., ostracize minorities, inadvertently promote extremism) or do not leverage the full potential of digital technologies. Extremism experts recognize the need for researchers from other disciplines, like information systems, to contribute their technical expertise for understanding and countering online extremism. This article aims to introduce the field of information systems to the issue of online extremism. Information systems scholars address technology-related societal issues from a sociotechnical perspective. The sociotechnical perspective describes systems through a series of interactions between social (structure, people) and technical components (physical system, task). We apply the sociotechnical perspective to (1) summarize the current state-of-the-art knowledge of 222 articles in a systematic multi-disciplinary literature review and (2) propose specific research questions that address two questions (How do digital technologies augment extremism? How can we successfully counter online extremism?).

To counter the side effect brought by the proliferation of social media platforms, hate speech detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early stage. However, given the ubiquitous topical communities on social media, a trained HSD classifier can easily become biased towards specific targeted groups (e.g.,female andblack people), where a high rate of either false positive or false negative results can significantly impair public trust in the fairness of content moderation mechanisms, and eventually harm the diversity of online society. Although existing fairness-aware HSD methods can smooth out some discrepancies across targeted groups, they are mostly specific to a narrow selection of targets that are assumed to be known and fixed. This inevitably prevents those methods from generalizing to real-world use cases where new targeted groups constantly emerge (e.g., new forums created on Reddit) over time. To tackle the defects of existing HSD practices, we propose Generalizable target-aware Fairness (GetFair), a new method for fairly classifying each post that contains diverse and even unseen targets during inference. To remove the HSD classifier's spurious dependence on target-related features, GetFair trains a series of filter functions in an adversarial pipeline, so as to deceive the discriminator that recovers the targeted group from filtered post embeddings. To maintain scalability and generalizability, we innovatively parameterize all filter functions via a hypernetwork. Taking a target's pretrained word embedding as input, the hypernetwork generates the weights used by each target-specific filter on-the-fly without storing dedicated filter parameters. In addition, a novel semantic gap alignment scheme is imposed on the generation process, such that the produced filter function for an unseen target is rectified by its semantic affinity with existing targets used for training. Finally, experiments are conducted on two benchmark HSD datasets, showing advantageous performance of GetFair on out-of-sample targets among baselines.

Scholars studying online extremism and terrorism face major challenges, including finding safe access to hostile environments where members evade law enforcement. Protective measures, such as research ethics, often overlook the safety of investigators. Investigators, including Open-Source Intelligence (OSINT) analysts, encounter emotional harm, abuse from ideologues, consent issues, and legal challenges in data collection. Despite rising awareness of these challenges, scholars lack guidance on starting and navigating research in these areas. This paper identifies challenges and offers strategies for safely, ethically, and legally researching in this environment.