Addressing Ethical Issues in AI

Stahl, Bernd Carsten

doi:10.1007/978-3-030-69978-9_5

Cited by 14 publications

(10 citation statements)

References 45 publications

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“…The MCQ items were organized in two modules: AI Basics (14 items and one point for an item) and machine learning (18 items and one point for an item). Ethical awareness is concerned with students’ value and attitude towards AI ethical issues, whether they could identify, evaluate, and respond to these issues approximately and responsibly (Stahl, 2021). The students’ ethical awareness was measured with scenario-based assessments (SBA), where real-life situations or ethical dilemmas were provided for them to analyze, evaluate, and take action (Romine et al, 2017; Daniel & Mazzurco, 2019).…”

Section: Methodsmentioning

confidence: 99%

Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools

Dai,

Lin,

Liu

et al. 2023

Journal of Educational Computing Research

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Artificial intelligence (AI) has emerged as a prominent topic in K-12 education recently. However, pedagogical design has remained a major challenge, especially among young learners. Guided by the Zone of Proximal Development theory and AI education research literature, this design-based study proposes an analogy-based pedagogical approach to support AI teaching and learning in upper primary education. This pedagogical approach is centered on human–AI comparison, where humans are gradually shifted from an analogue to a contrast to make visible the attributes, mechanisms, and processes of AI. To evaluate its effectiveness, a quasi-experimental study with mixed methods was conducted. The quantitative comparison shows that the participants in the experimental group learning with the analogy-based pedagogical approach significantly outperformed their peers with the conventional direct instructional approach in all three dimensions of AI knowledge, skills, and ethical awareness. Qualitative analyses further reveal its pedagogical benefits, including demystifying AI through relatable and engaging learning, supporting student comprehension and skill mastery, and nurturing critical thinking and attitudes. The analogy-based approach contributes to the field of K-12 AI education with an age-appropriate, child-friendly pedagogical approach. Notably, AI education should prioritize teaching for student understanding, and AI should be recognized as an independent subject with interdisciplinary applications.

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Section: Methodsmentioning

confidence: 99%

Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools

Dai,

Lin,

Liu

et al. 2023

Journal of Educational Computing Research

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show abstract

“…Addressing challenges of implementing AI and ML in RBM, such as ethical concerns and promoting fairness, transparency, and accountability, requires a comprehensive approach, entailing robust data governance, transparency (Stahl, 2021). This way, organizations will achieve transparency, fairness, and transparency.…”

Section: Fairness and Accountabilitymentioning

confidence: 99%

The The Application of Artificial Intelligence and Machine Learning to Enhance Results-Based Management

Lainjo

2023

J. Inf. Syst. Informatics

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Artificial Intelligence (AI) and Machine Learning (ML) technologies have revolutionized numerous industries and sectors, offering transformative potential for Results-Based Management (RBM). RBM is a management paradigm wherein organizations and government entities plan and assess the effectiveness of their projects, policies, or programs in achieving outcomes. Integrating AI and ML into RBM can significantly enhance outcomes, fostering data-driven and informed decision-making. AI and ML integration into RBM practices facilitates improved decision-making, resource optimization, accountability, and transparency. These technologies enhance RBM by enabling predictive analytics, real-time monitoring, task automation, customization, and scalability. The dynamic synergy of AI and ML extends beyond RBM into sectors like agriculture, public health, academia, and public administration. Despite their immense potential, AI and ML tools face challenges such as perpetuating inaccuracies and biases due to inherent biases or low data quality. Nevertheless, their application in RBM empowers organizations to plan better, monitor, evaluate, and refine projects and programs, optimizing resource allocation and performance. Ongoing research, ethical considerations, data quality, and accountability are essential priorities for harnessing the full benefits of AI and ML in RBM. Therefore, this research paper investigates the potential of AI and ML tools and technologies in improving results-based management. It comprehensively reviews existing literature, practical applications, and case studies to elucidate how AI and ML can enhance results-based management practices and contribute to better decision-making.

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“…General AI is different from weak or limited AI, which can only perform specific tasks and cannot adapt to new situations. General AI is so called because it focuses on developing computing systems that can perform a wide variety of tasks and come as close as possible to human intelligence (Stahl, 2021a).…”

Section: General or Strong Artificial Intelligence (Agi)mentioning

confidence: 99%

What is Artificial Intelligence?

Morandín-Ahuerma¹

2022

Int.J.Res.Publ.Rev.

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Artificial intelligence (AI) is the ability of a machine or computer system to simulate and perform tasks that would normally require human intelligence, such as logical reasoning, learning, and problem solving. Artificial intelligence is based on the use of machine learning algorithms and technologies to give machines the ability to apply certain cognitive abilities and perform tasks on their own autonomously or semi-autonomously. Artificial intelligence is distinguished by its degree of cognitive capacity or by its degree of autonomy. By capacity it can be weak or limited, general or superlative. Due to its autonomy, it can be reactive, deliberative, cognitive, or totally autonomous. As artificial intelligence improves, many processes are becoming more efficient and tasks that seem complicated today will be performed more quickly and accurately.

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Addressing Ethical Issues in AI

Cited by 14 publications

References 45 publications

Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools

Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools

The The Application of Artificial Intelligence and Machine Learning to Enhance Results-Based Management

What is Artificial Intelligence?

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