Assessing Collaborative Explanations of AI using Explanation Goodness Criteria

Explainable AI represents an increasingly important category of systems that attempt to support human understanding and trust in machine intelligence and automation. Typical systems rely on algorithms to help understand underlying information about decisions and establish justified trust and reliance. Researchers have proposed using goodness criteria to measure the quality of explanations as a formative evaluation of an XAI system, but these criteria have not been systematically investigated in the literature. To explore this, we present a novel collaborative explanation system (CXAI) and propose several goodness criteria to evaluate the quality of its explanations. Results suggest that the explanations provided by this system are typically correct, informative, written in understandable ways, and focus on explanation of larger scale data patterns than are typically generated by algorithmic XAI systems. Implications for how these criteria may be applied to other XAI systems are discussed.

APPLICATION OF EXPERT SYSTEMS OR DECISION-MAKING SYSTEMS IN THE FIELD OF EDUCATION

Expert system (ES) is a branch of artificial intelligence (AI) that is used to manage different problems by making use of interactive computer-based decision-making process. It uses both factual information and heuristics to resolve the complicated decision-making issues in a specific domain. The architecture of the expert system was analyzed and found that it includes several parts such as user interface, knowledge base, working memory, inference engine, explanation system, system engineer, and knowledge engineer, user, and expert system shell in which each part of the architecture of an expert system is based on different functionary that helps it to make an adequate decision by analyzing complex situations. The research aims to analyze the application of expert systems or decision-making systems in the field of education and found that it is used for different purposes such as assessing teacher performance, providing guidance to the students regarding their career, and providing quality learning to students with disabilities. It is also used to help the students to make rightful career decisions and become efficient professionals after completing their studies.

Local realism quantum mechanics can be established: a review of the book of quantum mechanics’ return to local realism

All the experiments that prove that Einstein is wrong and Bohr won have a serious flaw or loophole that is difficult to overcome — Have to subjectively judge the state of the system before the measurement. Undoubtedly, the establishment of quantum mechanics of local realism is a strong support for Einstein.<strong> </strong>Many people are dissatisfied with the quantum mechanics of non-local realism and want to establish the quantum mechanics of local realism. But they did not break through the bottleneck. The concept of wave function is widely used in existing quantum mechanics. However, the nature of the wave function is unanswerable. The model of "real wave curling inside the particle" determines that the wave function is the motion equation of localized real wave. The wave mechanics based on such wave functions is quantum mechanics of localized realism. The mathematical formal system of local realism quantum mechanics is the same as the existing one. Explanation of double slit diffraction can be experiment by directional quantization. The explanation system of local realism quantum mechanics is established, and it is guaranteed that this system can also be organically combined with the existing mathematical formal systems of quantum mechanics. Local realism quantum mechanics has all the elements of a valuable new theory, and its birth conforms to the law of theoretical development.