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A Model for transparency by design
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In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learne...
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... the TbD-framework rests on those three phases, we derive the content of each principle within these phases from the academic literature on transparency as well as policy and industry reports on transparency in AI systems. We propose nine principles, aligned with the three phases outlined in Fig. ...
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AI can deliver numerous business benefits. But automation with AI comes with a new set of challenges and risks. Organisations will only make a success of AI if they can effectively govern what should be automated (not just what could be automated).
This article considers automation with AI and concludes that automation with AI must be:
• Ethical –...
Citations
... As seen, while major ethical theories offer valuable normative foundations yet they might not be always well-suited for the practical challenges of designing and governing XAI systems. This is where the field of applied ethics comes in, developing mid-level principles and context-sensitive guidance to address the moral, political and social implications of technologies in real-world settings (Felzmann et al., 2020;Beauchamp & Childress, 2001). A wealth of XAI surveys and reviews mention such applied ethics principles while identifying and reporting various explanation techniques and domain, shedding light on diverse domain applications (Samek et al., 2017;Adadi & Berrada, 2018;Arrieta et al., 2020;Cambria et al., 2023;Stepin et al., 2021;Saeed & Omlin, 2023a;Martins et al., 2024). ...
... As outlined in Sect. 2, the major ethical theories (deontology, consequentialism, and virtue ethics) and the field of applied ethics offer valuable frameworks for navigating the complex ethical challenges surrounding XAI systems (Shafer-Landau, 2012;Copp, 2006;Felzmann et al., 2020;Beauchamp & Childress, 2001). These theories provide the necessary grounding for substantive ethical engagement, enabling researchers to consider the specific implications of their XAI systems in light of established moral principles and context-specific guidelines (Floridi, 2019;Bietti, 2020). ...
With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research frequently asserts its relevance to ethical considerations. Yet, the substantiation of these claims with rigorous ethical analysis and reflection remains largely unexamined. This contribution endeavors to scrutinize the relationship between XAI and ethical considerations. By systematically reviewing research papers mentioning ethical terms in XAI frameworks and tools, we investigate the extent and depth of ethical discussions in scholarly research. We observe a limited and often superficial engagement with ethical theories, with a tendency to acknowledge the importance of ethics, yet treating it as a monolithic and not contextualized concept. Our findings suggest a pressing need for a more nuanced and comprehensive integration of ethics in XAI research and practice. To support this, we propose to critically reconsider transparency and explainability in regards to ethical considerations during XAI systems design while accounting for ethical complexity in practice. As future research directions, we point to the promotion of interdisciplinary collaborations and education, also for underrepresented ethical perspectives. Such ethical grounding can guide the design of ethically robust XAI systems, aligning technical advancements with ethical considerations.
... A comprehensive understanding of these factors is essential to design AI systems that are both technologically advanced and human-centered, leading to increased acceptance and effective use of AI in decision making. The integration of AI in decision making raises questions around accountability, transparency, and explainability, necessitating innovative solutions to address these challenges (Felzmann et al., 2020). AI may perpetuate existing biases and inequalities, emphasizing the need for diverse AI development teams and ethical guidelines prioritizing fairness and social justice. ...
The aim of this study was to establish the influence of integration of artificial intelligence on
augmenting decision-making protocols in the street-level bureaucracy. This qualitative study reviews the
existing literature on the integration of Artificial Intelligence (AI) in street-level bureaucracy, focusing on its
impact on decision-making protocols. The study explores the challenges, opportunities, and future prospects of
AI integration in street-level bureaucracy, highlighting its potential to improve efficiency, decision-making, and
service delivery. The study emphasizes the need for responsible AI development, ensuring accountability,
transparency, and ethical considerations. Recommendations include prioritizing user-centered AI designs,
investments in natural language processing, computer vision, and human-AI collaboration, and emphasizing
ethical concerns in AI development. The study concludes that AI has the potential to enhance public services,
promote social justice, and lead to a more equitable and sustainable future for citizens.
... One approach to facilitating teachers' effective use of orchestration tools and their meaningful participation in offering constructive feedback is through enhancing the tools' transparency. Such transparency should include explanations of the capabilities and limitations of the tool [34,11]. For example, it can detail the specific criteria the tool utilizes to generate recommendations and the criteria it cannot consider. ...
... For example, it can detail the specific criteria the tool utilizes to generate recommendations and the criteria it cannot consider. Maintaining transparency allows teachers to evaluate the alignment of the tool's features with their educational values, make necessary adjustments, and maintain meaningful control over the use of these tools [11]. ...
Effective design of classroom orchestration tools depends on purposeful collection, analysis, and presentation of learning analytics (LA) to teachers. However, researchers have raised concerns regarding their acceptance and adoption in real classrooms, emphasizing the critical role that teachers can play in their design. To engage teachers in identifying their analytics needs during class time, we conducted classroom observations, interviews, and a focus group with ten grades 3 to 8 math teachers. Our thematic analysis of the data led to findings about teachers' in-class information needs and usage, which we integrated with existing models of teachers' sensemaking and decision-making in LA systems to generate a conceptual model of math teachers' in-class information needs and usage. Our model captures interconnections across categories of (a) information needs for in-class sensemaking (events, event causes, student profiles) and (b) information usage for in-class decision-making (interventions). We illustrate the connections between different instances of these categories through five case scenarios. Based on our model, we propose strategies for supporting the design of orchestration tools aiming to integrate cross-domain analytics for overall student growth, balance automated assistance and teacher control, and manage information overload for teachers. Our findings offer valuable insights for LA communities in grounding the design of classroom orchestration tools on teachers' actual needs.
... These dashboards can be customized to align with specific reporting frameworks and stakeholder requirements, ensuring that reports are relevant and compliant with international standards. By automating reporting processes, AI helps companies maintain transparency and accountability, fostering stakeholder trust and confidence (Felzmann et al., 2020). AI can address challenges related to data quality and standardization in environmental reporting. ...
The integration of Artificial Intelligence (AI) in sustainable accounting represents a transformative approach to enhancing the accuracy, efficiency, and comprehensiveness of environmental impact assessment and reporting. This paper explores the development of AI-driven models aimed at advancing sustainable accounting practices, focusing on environmental impact assessment and transparent reporting. AI technologies, particularly machine learning (ML) and natural language processing (NLP), play a pivotal role in automating and refining data collection, analysis, and reporting processes. These technologies enable the processing of vast amounts of heterogeneous data from multiple sources, including IoT sensors, satellite imagery, and corporate disclosures. By leveraging ML algorithms, organizations can identify patterns, predict trends, and assess the environmental impact of their operations with unprecedented precision. One of the key advantages of AI in sustainable accounting is its ability to enhance data accuracy and reliability. Traditional methods often suffer from manual errors and inconsistencies. AI models, however, can continuously learn and adapt, improving their accuracy over time. For instance, predictive analytics can forecast future environmental impacts based on historical data, allowing companies to implement proactive measures to mitigate adverse effects. Furthermore, AI facilitates real-time monitoring and reporting. IoT devices equipped with environmental sensors can stream data to AI systems, which process and analyze the information instantaneously. This capability is crucial for timely reporting and compliance with environmental regulations. Real-time data analytics also empower organizations to make informed decisions swiftly, optimizing their sustainability strategies and reducing their ecological footprint. Another significant contribution of AI is in enhancing transparency and accountability in environmental reporting. NLP algorithms can analyze and interpret regulatory texts, corporate reports, and public records, ensuring that organizations adhere to sustainability standards and guidelines. Additionally, AI can automate the generation of comprehensive and comprehensible sustainability reports, making them accessible to a broader audience, including stakeholders and regulators. Developing robust AI models for sustainable accounting involves several critical steps. Initially, data preprocessing is essential to clean and harmonize diverse datasets, ensuring quality input for AI algorithms. Next, model training and validation are conducted using historical and real-time data to refine predictive capabilities. Continuous model evaluation and adjustment are necessary to maintain accuracy and relevance in dynamic environmental contexts. Collaboration between AI experts, environmental scientists, and accounting professionals is paramount in this development process. Interdisciplinary teams can ensure that AI models are not only technically sound but also aligned with environmental science principles and accounting standards. This collaboration also fosters innovation, leading to the development of more sophisticated tools for environmental impact assessment and reporting. The adoption of AI-driven sustainable accounting models offers numerous benefits, including enhanced efficiency, accuracy, and compliance. However, challenges such as data privacy, algorithmic transparency, and the need for substantial initial investments must be addressed. Future research should focus on overcoming these obstacles and exploring the potential of emerging AI technologies, such as deep learning and blockchain, to further revolutionize sustainable accounting practices. AI holds significant promise for transforming sustainable accounting by improving environmental impact assessment and reporting. Through advanced data analytics, real-time monitoring, and enhanced transparency, AI can help organizations achieve their sustainability goals, ensuring a more sustainable future. The continuous development and refinement of AI models, supported by interdisciplinary collaboration, are essential for realizing these benefits and addressing the complex challenges of environmental sustainability. Keywords: Sustainable Accounting, Environmental Impact Assessment, AI, Developing Models, Reporting.
... Robust data protection regulations, such as comprehensive data encryption standards and stringent access controls, must be enforced to safeguard individuals' sensitive information from unauthorized access or misuse by AI systems (Thapa & Camtepe, 2021). Additionally, transparency in AI algorithms and decision-making processes is essential to uphold ethical standards and enable accountability (Felzmann et al., 2020). ...
The emergence of Industry 5.0 has ushered in a new era of industrial transformation characterized by the integration of physical and digital systems. In this dynamic environment, the role of artificial intelligence (AI) in supply chain management has become paramount. This research paper explores the impact of AI-driven imperatives on supply chain resilience and sustainability in Industry 5.0. Using a combination of probability analysis (PA), Bayesian-BWM (B-BWM), and Pareto analysis, we identify key AI-driven strategies for enhancing supply chain operations in the ready-made garment (RMG) and footwear sectors in China. The study highlights the importance of continuous monitoring of supply chain operations via the Internet of Things (IoT), adopting a circular production and packaging (CPP) model, and establishing a digital supply chain replica. These strategies improve resilience and contribute to environmental and societal outcomes. Automation and robust cybersecurity for data handling are crucial in the context of Industry 5.0, as they enhance production adaptability and data security. Also, streamlining inventory via RFID technology and harnessing AI to improve workforce safety and operational flow are essential measures. Comprehensive data analysis and forward-looking predictions through AI-driven big data analytics provide insights into consumer demand and energy consumption, ensuring efficient supply chain management. Lastly, while promising, blockchain integration poses challenges in terms of investment and regulatory compliance. However, it is important to consider the ethical implications and regulatory frameworks associated with AI deployment, as well as the need for education and training to bridge the digital gap. Collaboration between governments, industries, and educational institutions is essential to establish a comprehensive Industry 15.0 that benefits all segments of society. This research sheds light on the transformative potential of AI-driven supply chain management in Industry 5.0 and underscores the importance of addressing challenges to maximize its benefits.
... Implementing transparent systems and checks can mitigate agency problems, reducing the likelihood of tax evasion and corruption. Understanding the dynamics through this theoretical framework allows policymakers to develop strategies that foster accountability and discourage opportunistic behavior within the customs system, thereby promoting a fair and efficient taxation process [25]. ...
This study aims to investigate the dynamics of tax evasion and revenue leakage in the Somali customs framework, providing insights into the systemic opportunity structures, tax governance deficiencies, and personal incentive structures that facilitate these practices. By applying agency theory and rent-seeking theory, this research seeks to deepen the understanding of the complex relationship between individual motivations and systemic vulnerabilities in exacerbating corruption and tax evasion in a post-conflict governance context. By employing structural equation modeling (SEM) within the ADANCO-SEM analysis framework, this study analyzes primary survey data. This approach allows for a comprehensive examination of the relationships between systemic, governance, and personal factors contributing to corruption and tax evasion. The findings reveal a significant positive relationship between systemic opportunity structures, tax governance deficiencies, and personal incentive structures and the prevalence of tax evasion and corruption. Specifically, systemic opportunity structures were found to significantly influence both tax governance deficiencies and personal incentive structures, highlighting the intertwined nature of these factors in facilitating corrupt practices and tax evasion in Somali customs. This study underscores the urgent need for comprehensive reforms targeting systemic vulnerabilities, enhancing tax governance frameworks, and aligning personal incentives with the public interest. Practical applications include the adoption of advanced technological solutions for improved monitoring and transparency, as well as the development of targeted training programs for customs officials to foster ethical standards and compliance. This research contributes to the existing body of knowledge by providing a unique empirical examination of corruption and tax evasion in the context of Somali customs, a largely underexplored area in the literature. By integrating agency theory and rent-seeking theory, this study offers novel insights into the mechanisms of corruption and tax evasion, highlighting the importance of addressing both systemic and individual factors in combating these issues.
... While these questions relate also to the broader interface between humans and AI, they hold special relevance for psychotherapy and the larger mental health domain [7,17,22]. These three questions are meticulously designed to advance the principles of transparency and autonomy, which are essential for fostering critical thinking, especially in the context of interacting with AI systems [44,45]. Critical thinking is one of the most important capacities for promoting human freedom and agency [46,47]. ...
This paper explores a significant shift in the field of mental health in general and psychotherapy in particular following generative artificial intelligence's new capabilities in processing and generating humanlike language. Following Freud, this lingo-technological development is conceptualized as the "fourth narcissistic blow" that science inflicts on humanity. We argue that this narcissistic blow has a potentially dramatic influence on perceptions of human society, interrelationships, and the self. We should, accordingly, expect dramatic changes in perceptions of the therapeutic act following the emergence of what we term the artificial third in the field of psychotherapy. The introduction of an artificial third marks a critical juncture, prompting us to ask the following important core questions that address two basic elements of critical thinking, namely, transparency and autonomy: (1) What is this new artificial presence in therapy relationships? (2) How does it reshape our perception of ourselves and our interpersonal dynamics? and (3) What remains of the irreplaceable human elements at the core of therapy? Given the ethical implications that arise from these questions, this paper proposes that the artificial third can be a valuable asset when applied with insight and ethical consideration, enhancing but not replacing the human touch in therapy.
... This leads to regulatory delays that prevent the development of timely and effective oversight (Utilities One, 2023). Additionally, the ambiguous aspect of AI development pose challenges in assessing decision-making processes (Felzmann et al., 2020). This lack of transparency impedes the enforcement of essential standards for accountability (e.g. for those involved in safeguarding national security), which is necessary for the ethical and responsible deployment of AI across different sectors. ...
The increasing use of AI in national security emphasizes the need to regulate its ethical and responsible use through international rules and regulations. This article examines the importance of AI to national security, with a particular focus on how it impacts cybersecurity, intelligence gathering, and defense strategies. Even
though AI has been contributing in the reinforcement of national security, it still raises moral issues and potential security risks, while the need to create international laws and standards is becoming more and more evident. Moreover, this article assesses the use of AI in national security, highlighting the benefits, drawbacks, and need for oversight. In addition to examining governance issues, the importance of integrating safety standards with moral and ethical concerns is emphasized, with a particular focus on international
cooperation and the evaluation of current institutions.
Keywords: Artificial Intelligence (AI), National Security, International Norms, Partnerships, International Collaboration, International Standards
... As AI systems become increasingly autonomous and opaque, questions arise regarding the accountability of AI developers, operators, and users for the decisions and actions of AI systems. The lack of transparency in AI algorithms and decision-making mechanisms complicates efforts to understand, interpret, and challenge algorithmic outcomes, particularly in highstakes domains such as criminal justice, healthcare, and financial services (Felzmann et al., 2020;Kumar & Suthar, 2024;Rodgers et al., 2023). ...
The rapid expansion of artificial intelligence (AI) has engendered significant societal and ethical concerns, necessitating a comprehensive examination of scholarly work in AI ethics. This study aims to present a bibliometric analysis delineating the growth trends, author collaborations, thematic evolution, and international contributions within the realm of AI ethics research from 2014 to 2023. Employing established bibliometric methodologies, this research utilizes data from the Scopus database, encompassing 1490 articles. The study investigates growth patterns, authorship trends, thematic, and global contributions in AI ethics scholarship. The result reveals an exponential surge in annual AI ethics publications, with critical thematic shifts and prolific author collaborations. Author productivity, citation metrics, institutional affiliations, and country-wise contributions elucidate the global landscape of AI ethics research.
... Regarding illegal violations, retributive strategies on commercially moderated platforms deliver higher justice and fairness than user-moderated platforms. Many scholarships have highlighted the importance of moderation transparency to improve justice and fairness [12,41]. Most of them focus on one platform's moderation policies. ...
Social media users may perceive moderation decisions by the platform differently, which can lead to frustration and dropout. This study investigates users' perceived justice and fairness of online moderation decisions when they are exposed to various illegal versus legal scenarios, retributive versus restorative moderation strategies, and user-moderated versus commercially moderated platforms. We conduct an online experiment on 200 American social media users of Reddit and Twitter. Results show that retributive moderation delivers higher justice and fairness for commercially moderated than for user-moderated platforms in illegal violations; restorative moderation delivers higher fairness for legal violations than illegal ones. We discuss the opportunities for platform policymaking to improve moderation system design.
