[학술논문 2024] 저자: Jan Maarten Schraagen | 인용수: 15 | 초록: Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it. Responsible Use of AI in Military Systems lay
[학술논문 2024] 저자: Ahmed Shalaby | 인용수: 28 | 초록: Abstract This study presents a novel classification framework for digital and cognitive AI hazards (Shalaby’s Classification for Digital Hazards), aiming to comprehensively categorize risks across pathophysiological impacts, technical sources, content-related risks, algorithmic influences, modification factors, and mitigation measures. It utilizes rigorous literature review methodologies to synthesize existing research and proposes practical implemen
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic
[학술논문 2020] 저자: Thilo Hagendorff | 인용수: 1780 | 초록: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compar
[학술논문 2020] 저자: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White | 인용수: 1328 | 초록: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, e
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[학술논문 2024] 저자: Jan Maarten Schraagen | 인용수: 15 | 초록: Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it. Responsible Use of AI in Military Systems lay
[학술논문 2024] 저자: Ahmed Shalaby | 인용수: 28 | 초록: Abstract This study presents a novel classification framework for digital and cognitive AI hazards (Shalaby’s Classification for Digital Hazards), aiming to comprehensively categorize risks across pathophysiological impacts, technical sources, content-related risks, algorithmic influences, modification factors, and mitigation measures. It utilizes rigorous literature review methodologies to synthesize existing research and proposes practical implemen
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic
[학술논문 2020] 저자: Thilo Hagendorff | 인용수: 1780 | 초록: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compar
[학술논문 2020] 저자: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White | 인용수: 1328 | 초록: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, e
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[학술논문 2024] 저자: Jan Maarten Schraagen | 인용수: 15 | 초록: Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it. Responsible Use of AI in Military Systems lay
[학술논문 2024] 저자: Ahmed Shalaby | 인용수: 28 | 초록: Abstract This study presents a novel classification framework for digital and cognitive AI hazards (Shalaby’s Classification for Digital Hazards), aiming to comprehensively categorize risks across pathophysiological impacts, technical sources, content-related risks, algorithmic influences, modification factors, and mitigation measures. It utilizes rigorous literature review methodologies to synthesize existing research and proposes practical implemen
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic
[학술논문 2020] 저자: Thilo Hagendorff | 인용수: 1780 | 초록: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compar
[학술논문 2020] 저자: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White | 인용수: 1328 | 초록: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, e
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[학술논문 2024] 저자: Jan Maarten Schraagen | 인용수: 15 | 초록: Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it. Responsible Use of AI in Military Systems lay
[학술논문 2024] 저자: Ahmed Shalaby | 인용수: 28 | 초록: Abstract This study presents a novel classification framework for digital and cognitive AI hazards (Shalaby’s Classification for Digital Hazards), aiming to comprehensively categorize risks across pathophysiological impacts, technical sources, content-related risks, algorithmic influences, modification factors, and mitigation measures. It utilizes rigorous literature review methodologies to synthesize existing research and proposes practical implemen
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic
[학술논문 2020] 저자: Thilo Hagendorff | 인용수: 1780 | 초록: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compar
[학술논문 2020] 저자: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White | 인용수: 1328 | 초록: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, e
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[학술논문 2024] 저자: Jan Maarten Schraagen | 인용수: 15 | 초록: Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it. Responsible Use of AI in Military Systems lay
[학술논문 2024] 저자: Ahmed Shalaby | 인용수: 28 | 초록: Abstract This study presents a novel classification framework for digital and cognitive AI hazards (Shalaby’s Classification for Digital Hazards), aiming to comprehensively categorize risks across pathophysiological impacts, technical sources, content-related risks, algorithmic influences, modification factors, and mitigation measures. It utilizes rigorous literature review methodologies to synthesize existing research and proposes practical implemen
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic
[학술논문 2020] 저자: Thilo Hagendorff | 인용수: 1780 | 초록: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compar
[학술논문 2020] 저자: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White | 인용수: 1328 | 초록: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, e
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[학술논문 2024] 저자: Jan Maarten Schraagen | 인용수: 15 | 초록: Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it. Responsible Use of AI in Military Systems lay
[학술논문 2024] 저자: Ahmed Shalaby | 인용수: 28 | 초록: Abstract This study presents a novel classification framework for digital and cognitive AI hazards (Shalaby’s Classification for Digital Hazards), aiming to comprehensively categorize risks across pathophysiological impacts, technical sources, content-related risks, algorithmic influences, modification factors, and mitigation measures. It utilizes rigorous literature review methodologies to synthesize existing research and proposes practical implemen
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic
[학술논문 2020] 저자: Thilo Hagendorff | 인용수: 1780 | 초록: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compar
[학술논문 2020] 저자: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White | 인용수: 1328 | 초록: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, e
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[학술논문 2024] 저자: Jan Maarten Schraagen | 인용수: 15 | 초록: Artificial Intelligence (AI) is widely used in society today. The (mis)use of biased data sets in machine learning applications is well‑known, resulting in discrimination and exclusion of citizens. Another example is the use of non‑transparent algorithms that can’t explain themselves to users, resulting in the AI not being trusted and therefore not being used when it might be beneficial to use it. Responsible Use of AI in Military Systems lay
[학술논문 2024] 저자: Ahmed Shalaby | 인용수: 28 | 초록: Abstract This study presents a novel classification framework for digital and cognitive AI hazards (Shalaby’s Classification for Digital Hazards), aiming to comprehensively categorize risks across pathophysiological impacts, technical sources, content-related risks, algorithmic influences, modification factors, and mitigation measures. It utilizes rigorous literature review methodologies to synthesize existing research and proposes practical implemen
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic
[학술논문 2020] 저자: Thilo Hagendorff | 인용수: 1780 | 초록: Abstract Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compar
[학술논문 2020] 저자: Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White | 인용수: 1328 | 초록: Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms. However, it remains challenging for practitioners to identify the harmful repercussions of their own systems prior to deployment, and, once deployed, e
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[학술논문 2023] 저자: Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh | 인용수: 748 | 초록: Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors th
[arXiv 2026-01-23] 저자: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo | 초록: Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communic