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Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.

The proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.

The authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.

This research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

Climate change is becoming one of the biggest and most pressing environmental challenges of the last century. As such, the board of directors and its sub-committees need to recognise and address climate change as a potential strategic risk for their companies, using a sustainable approach that prioritises this risk over other business-related risks. In this framework, this paper aims to examine the influence of audit committee characteristics and its effectiveness on the commitment to voluntary climate change disclosure.

Our sample covers 403 non-financial companies from 48 countries. These companies belong to the largest greenhouse gas (GHG) emitting sectors, namely oil and gas, chemicals and coal. We have used data from company responses to the Carbon Disclosure Project (CDP) survey for the years 2015–2021. The total number of observations for the seven-year periods is 2,821 firm years. Audit committee characteristics examined are size, independence, gender diversity and number of meetings. Regarding the effectiveness of the audit committee, it is measured using a composite index developed from its individual characteristics.

Our findings show that the number of meetings held by the audit committee affects positively the commitment to the CDP initiative. However, the effects of audit committee size, independence and the presence of women are statistically insignificant. Regarding the effectiveness of the audit committee, our results show that it has a positive and significant effect on the commitment to the CDP initiative. In addition, the results demonstrate that the audit committee diversity, measured using the Herfindahl index, had a positive and significant effect on climate change disclosure commitment.

This study offers new insights into the role of the audit committee in improving climate change disclosure. The findings indicate that companies can improve their sustainability and social responsibility by establishing an effective audit committee within their board of directors. Furthermore, investors and regulators must give great importance to the characteristics of the audit committee and its roles and duties in the fight against climate change.

In contrast to previous research, this study examines, simultaneously, the impact of individual characteristics and the effectiveness of the audit committee on the commitment to climate change disclosure.