AI artifacts are technological ones with AI as crucial components. Those actually used in practice are denoted as AI artifacts-in-practice. Intelligent behaviors of AI appear to be results of recognition and assessment of meaning of input data. Judgements or decisions by AI convey intended/perceived information to its user. This research first studies connection between the interpretive approach and intended/perceived information, and defines purposeful domain-specific AI (PDsAI) as well as AI-related terms. Based on it, we maintain that PDsAIs have been essential components of ICT systems since 1950s. Three essential factors which make AI ethics critical concern are clarified. We also propose ideas of supply-ethics and use-ethics, and insist on usefulness of the interpretive approach and the intended/perceived information aspect.

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Today, many people have been paying academic and journalistic attention to the so-called machine learning or deep learning. But in many cases, there is a kind of serious lack of awareness of the ontological meanings behind these methods. In deep learning in which a kind of approximate calculation using discrete values is used, a set of concepts such as learning, trial-and-error, minimalizing of errors would disappear. In this article, we will critically and ethically examine how various meanings would disappear or appear in mathematics, machine learning or in our activity with our own imagination and views on life.

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AI has been introduced in various operations, but the current situation is that the introduction in audit operations has not progressed. Audit work can be divided into external audit and internal audit, but in this paper, we will consider the possibility of introducing AI from the two aspects of external audit and internal audit. In addition, the audit work is characterized in that it checks and evaluates the accuracy of deliverables created by others and evaluates the appropriateness of the work execution status of others, as compared with other works. Based on these characteristics, we will consider the possibility of introducing AI in audit work and the issues that hinder the introduction of AI.

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An unjustified large-scale military offensive by Russia began abruptly on February 24. The Ukraine-Russia war is said to show a striking contrast between Russia's old tactics and Ukraine's advanced use of information. AI is being used in pure information warfare, so-called propaganda and social networking, and facial recognition AI is being used to identify soldiers on both sides and to notify their mothers. Satellite networks are used to locate enemy officers, and AI-equipped unmanned drones are used to get there. In a situation where life and death are literally at stake for both sides, the ethical standards of peacetime AI will not apply. It is natural human psychology to hope that our own robots (AI soldiers) and AI weapons will eventually fight on behalf of our children and defeat the assassins (enemy soldiers). Parents, too, have a harsh reality in which the logic of wanting to know factual information prevails over emotional sensation. In wartime, in addition to production testing of new weapons, research and development of AI warfare applications and empirical evaluations are proceeding at breakneck speed. This paper follows the first examples of AI applications and their implications in a full-scale war that has been going on for more than a few months, as well as the ones that caught our attention.

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This study reconsiders responsible artificial intelligence (RAI) from a perspective of business. Machine learning-based artificial intelligence systems have widely been utilised for various purposes due to extremely useful functions such autonomous systems can perform. On the other hand, however, the autonomy tends to make system behaviour unpredictable and uncontrollable, and thus to make it difficult to determine exact cause as well as to define the locus of responsibility when the systems bring any harm. In this respect, RAI becomes an ethical and social matter. Although a variety of principles and recommendations for RAI have been offered, they lack a perspective of business which is critical to ensuring the effectiveness of them. Efforts to compensate for the lack are urgently necessary to develop sound economy and society which have increasingly been dependant on AI technologies.

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AI systems that are developed for deployment in organizational task environments can be considered very similar in their functions to OR systems that are developed for solving specific problems. Noticing that experiences concerning ethical risks have been accumulated in OR systems development, we investigate each development stage of OR systems for related ethical principles. Then, applying similarity of development stages of AI systems to those of OR systems, each development stages of AI system are assigned ethical principles that should be dealt with there.

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