Breakable Machine: A K–12 Classroom Game for Transformative AI Literacy Through Spoofing and eXplainable AI (XAI)

The paper brings together two major research areas: the effects of technology in workplaces and trust in technology, in the context of Artificial Intelligence’s (AI) proliferation. By doing so, the paper answers the call to go beyond the automation augmentation debate to understand the nuances caused by the continuance of augmentation on knowledge workers within AI systems. We introduce a theory of trust in AI systems, proposing a dynamic form of trust that emerges when the usual epistemic relationship between trust and knowing (that technology delivers an expected result) is broken. The creation of doubts in the system, due to the occurrences of different types of errors, questions the veracity of the AI model’s results. The implications of this theory are shown in the context of different AI models used in a Department of Radiology, through an extensive ethnographic fieldwork. Unlike previous workplace technologies, where trust is developed gradually, the trust-building process for each AI model follows a unique path, offering a combination of two forms of trust: fleeting and stable. In order to further understanding this phenomenon, the data suggests integrating trusting building with social actions, system functions and the daily work context within the department. The study points to the transient nature of trust in AI systems, which depends on the context and features of the trusting object such as data, algorithm, task, and user or user community. The paper finds that trust in metahuman systems (a sociotechnical system where both humans and machines learn) requires human to trust the machine components, as machines continuously learn and evolve with humans. And this dynamic, context-specific form of trust building must be cultivated and maintained for effective interaction and integration.

Practical Aspects of Designing a Human-centred AI System in Manufacturing

PurposeDrawing upon a circular model proposition, Kantian ethics is employed to explore how ethical considerations within AI translate into concrete actions that prioritize transparency, privacy, inclusivity and equality. Additionally, agency theory is applied to understand the relevance of fairness in the interactions between agents, principals and algorithmic systems, particularly in the creation of value through digital platforms.Design/methodology/approachA review of literature on ethical concerns within the AI ecosystem is conducted, proposing a unifying ethical principle and standards. The circular model for ethics in action is then developed, emphasising the responsible use of AI and its role in capturing and creating social value, ultimately contributing to sustainable organizational outcomes. The model also highlights key drivers that shape the ethical framing of AI, as well as the influence of the institutional context on its adoption and effectiveness.FindingsResponsible use of AI positively affects organizational performance and the digital ecosystem via the psychological mechanism of ethical identity. Ethical standards and regulations are the global requirements for the AI ecosystem that are required for achieving a sustainable digital society.Research limitations/implicationsThis study contributes to a comprehensive understanding of the responsible use of AI’s and its practical and theoretical implications for organizations in the current digital ecosystem. Lack of global understanding, awareness and implementation of ethical practice in the AI ecosystem is not yet developed and practice. Future researchers can design a cross-border ethical framework to overcome these limitations. Organizations targeting to increase responsible digital interactions can benefit from maintaining ethical principles through responsible labourers, leaders and all stakeholders involved in the ecosystem.Practical implicationsThis study offers practical guidance for businesses, policymakers and AI practitioners on the ethical use of AI. It emphasizes the need for robust data governance, a “human-first” approach focusing on privacy and accountability and alignment with ethical standards. Given AI’s global reach, international cooperation and standard-setting are essential to navigate diverse regulatory and cultural contexts. The paper also highlights the importance of ethics education for AI developers and practitioners. Investing in training that integrates technical skills with ethical awareness will help build a responsible AI workforce capable of addressing societal impacts and maintaining public trust.Social implicationsThis study underscores the urgent need for responsible AI adoption, highlighting risks such as bias, lack of transparency and privacy concerns. As AI reshapes work, decision-making and governance, its social impact grows – potentially deepening inequalities if left unchecked. The study calls for explainable, fair and inclusive AI systems guided by ethical frameworks that respect human dignity. A “human-first” approach ensures AI supports-not replaces-human agency. By fostering transparency, accountability and cultural sensitivity, organizations can build public trust, empower diverse communities and contribute to a more equitable digital future. Ethical leadership and inclusive design are essential to avoid reinforcing systemic harms.Originality/valueThis study presents an original approach to integrating ethical considerations into the development and deployment of artificial intelligence, by conceptualizing how transparency, accountability and inclusivity can be embedded throughout the AI ecosystem, fostering trust and responsible innovation. Through a comprehensive examination of ethical principles and requirements, we recommend a set of tools and strategies needed to promote ethical AI practices, mitigate risks and maximize societal benefit. Furthermore, this study serves as a roadmap for building AI systems that prioritize human collaboration and uphold fundamental values in the digital age.

The implementation of this community service aims to increase moral and ethical awareness in the digital era among teenagers from the Al-Lathifah Mosque Youth Association, West Cikarang, through a structured and interactive counseling program. In its implementation, community service involves four main steps: preparation, planning, implementation, and evaluation. In the preparation stage, surveys and discussions were carried out to determine teenagers' needs regarding digital ethics. Next, at the planning stage, outreach materials are prepared thoroughly and delivery methods are chosen carefully. The outreach includes lectures, group discussions, and interactive activities designed to strengthen understanding and application of digital ethics in everyday life. Evaluation was carried out using questionnaires and in-depth interviews to measure changes in participants' understanding and attitudes. The results of this research show that there is a significant increase in digital moral and ethical awareness among teenagers after attending the counseling. This program has also succeeded in creating an environment that supports the development of better character. Thus, this outreach program has made a positive contribution to increasing digital moral and ethical awareness and could be an example for similar programs in the future.

Darkness, Datafication, and Provenance as an Illuminating Methodology

PurposeInscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.Design/methodology/approachThe authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.FindingsThe authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.Research limitations/implicationsBased on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.Originality/valueThis literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.

This study concerns the sociotechnical bases of human autonomy. Drawing on recent literature on AI ethics, philosophical literature on dimensions of autonomy, and on independent philosophical scrutiny, we first propose a multi-dimensional model of human autonomy and then discuss how AI systems can support or hinder human autonomy. What emerges is a philosophically motivated picture of autonomy and of the normative requirements personal autonomy poses in the context of algorithmic systems. Ranging from consent to data collection and processing, to computational tasks and interface design, to institutional and societal considerations, various aspects related to sociotechnical systems must be accounted for in order to get the full picture of potential effects of AI systems on human autonomy. It is clear how human agents can, for example, via coercion or manipulation, hinder each other’s autonomy, or how they can respect each other’s autonomy. AI systems can promote or hinder human autonomy, but can they literally respect or disrespect a person’s autonomy? We argue for a philosophical view according to which AI systems—while not moral agents or bearers of duties, and unable to literally respect or disrespect—are governed by so-called “ought-to-be norms.” This explains the normativity at stake with AI systems. The responsible people (designers, users, etc.) have duties and ought-to-do norms, which correspond to these ought-to-be norms.

Explainable Artificial Intelligence (XAI) aims to bring transparency to AI systems by translating, simplifying, and visualizing its decisions. While society remains skeptical about AI systems, studies show that transparent and explainable AI systems result in improved confidence between humans and AI. We present preliminary results from a study designed to assess two presentation-order methods and three AI decision visualization attribution models to determine each visualization’s impact upon a user’s cognitive load and confidence in the system by asking participants to complete a visual decision-making task. The results show that both the presentation order and the morphological clarity impact cognitive load. Furthermore, a negative correlation was revealed between cognitive load and confidence in the AI system. Our findings have implications for future AI systems design, which may facilitate better collaboration between humans and AI.

Human-Centered AI prioritizes end-users’ needs like transparency and usability. This is vital for applications that affect people’s everyday lives, such as social assessment tasks in the public sector. This paper discusses our pioneering effort to involve public sector AI users in XAI application design through a co-creative workshop with unemployment consultants from Estonia. The workshop’s objectives were identifying user needs and creating novel XAI interfaces for the used AI system. As a result of our user-centered design approach, consultants were able to develop AI interface prototypes that would support them in creating success stories for their clients by getting detailed feedback and suggestions. We present a discussion on the value of co-creative design methods with end-users working in the public sector to improve AI application design and provide a summary of recommendations for practitioners and researchers working on AI systems in the public sector.

Living with the Algorithm - Toward a New Social Contract in the Age of AI

The data and Artificial Intelligence revolution has had a massive impact on enterprises, governments, and society alike. It is fueled by two key factors. First, data have become increasingly abundant and are often available openly. Enterprises have more data than they can process. Governments are spearheading open data initiatives by setting up data portals such as data.gov and releasing large amounts of data to the public. Second, AI engineering development is becoming increasingly democratized. Open source frameworks have enabled even an individual developer to engineer sophisticated AI systems. But with such ease of use comes the potential for irresponsible use of data. Ensuring that AI systems adhere to a set of ethical principles is one of the major problems of our age. We believe that data and model transparency has a key role to play in mitigating the deleterious effects of AI systems. In this article, we describe a framework to synthesize ideas from various domains such as data transparency, data quality, data governance among others to tackle this problem. Specifically, we advocate an approach based on automated annotations (of both data and the AI model), which has a number of appealing properties. The annotations could be used by enterprises to get visibility of potential issues, prepare data transparency reports, create and ensure policy compliance, and evaluate the readiness of data for diverse downstream AI applications. We propose a model architecture and enumerate its key components that could achieve these requirements. Finally, we describe a number of interesting challenges and opportunities.

We propose, Trustworthy Explainability Acceptance metric to evaluate explainable AI systems using expert-in-the-loop. Our metric calculates acceptance by quantifying the distance between the explanations generated by the AI system and the reasoning provided by the experts based on their expertise and experience. Our metric also evaluates the trust of the experts to include different groups of experts using our trust mechanism. Our metric can be easily adapted to any Interpretable AI system and be used in the standardization process of trustworthy AI systems. We illustrate the proposed metric using the high-stake medical AI application of Predicting Ductal Carcinoma in Situ (DCIS) Recurrence. Our metric successfully captures the explainability of AI systems in DCIS recurrence by experts.

Artificial intelligence systems face important challenges in adversarial machine learning because smooth yet carefully constructed disturbances to data inputs make models display wrong behavior, resulting in prediction mistakes or system malfunctions. The author of this research paper investigates how adversarial attacks affect AI systems within three primary sectors: autonomous driving, security systems, and healthcare. The paper discusses white-box and black-box adversarial attacks while analyzing machine learning model vulnerabilities. The paper evaluates existing defense methods, including adversarial training and robust optimization, and discusses the difficulties of achieving security without affecting model performance. The existing defense approaches perform poorly against state-of-the-art adversarial techniques, so researchers must develop stronger protection methods. The paper ends by providing security solutions for AI systems through explainable AI integration alongside advanced adversarial training methods so AI models can identify and guard against advancing adversarial threats.

The journal aims to reduce energy usage that emits greenhouse gases by making products more productive and eco-friendlier. The writing deals with the issue of how the current trends in Industry 4.0 and AI systems can be used to protect the environment from the harms of pollution. It incorporates 1) conserving energy, implementing eco-friendlier workplaces, and reducing carbon footprints and 2) digital transformation with environmentally positive implications. Also, like in real manufacturing, it addresses the possibility of AI-driven manufacturing. It refers to Industry 5.0 as an evolutional road of communication and people and machines collaboration that transforms both society and individual; personalization as a result and higher productivity are just a few foundations to transform our perception of production and everyday life. Other Firstly, our guide will give some examples of preventive measures. This part stresses investing in R&D, collaborating, complying, and ensuring decision-making is informed by data running through the supply chain.

Artificial intelligence is rapidly penetrating the most diverse areas of our everyday lives and the world of work. Its real impact is hard to assess, not only because many AI systems work in the background but also because the term is unclear and mainly used for marketing purposes. In addition, AI systems are often seen as a ‘black box’, not fully understood even by their developers. This chapter aims to provide a better understanding of where and how artificial intelligence is used in the world of work and how trade unions and other employee representatives can negotiate its use. In its first part, the chapter gives an overview of three different forms of AI at the workplace: algorithmic management, automated decision-making, and generative AI. The second part discusses the possibilities of negotiating artificial intelligence in the context of the recently updated German Works Constitution Act, and it introduces the concept of the Machine Learning Pipeline as a means to tackle the ‘black box’ issue.