Explainable artificial intelligence modeling for corporate social responsibility and financial performance

Lachuer, Julien; Jabeur, Sami Ben

doi:10.1057/s41260-022-00291-z

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…For example, an XAI system could thoroughly explain why a specific credit decision was made or a specific stock was recommended for investment. Credit-based score decisions are being studied to make them more understandable [19][20][21][22][23][59][60][61][62][63][64][65][66][67].…”

Section: 3mentioning

confidence: 99%

A Systematic Literature Review of Explainable Artificial Intelligence (XAI) in Software Engineering (SE)

khan,

Ali,

Khan

et al. 2023

Preprint

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Artificial intelligence (AI) is the most advanced developing area for enhancing Machine Intelligence and replicating the intelligence of humans. In this regard, Machine Learning (ML) is used to develop algorithms and models that help machines learn from data and predict problems. Although ML models provide accurate predictions, they are frequently considered black boxes due to their lack of interpretability. This can undermine trust and acceptance of AI systems, particularly in critical domains requiring transparency and accountability, such as Healthcare. Explainable Artificial Intelligence (XAI) techniques, which have emerged to make ML models more transparent and interpretable, can address the lack of interpretability challenge. They shed light on how ML models make decisions and explain and justify the results. This builds trust and makes AI systems more accessible to implement in various industries. The proposed research study investigates how much XAI is used in Software Engineering (SE). It intends to present a comprehensive view of the most recent advancements and address challenges and future directions for further investigation. This Systematic Literature Review (SLR) investigates the application of XAI techniques in SE. It is based on empirical studies published between January 2020 and September 2022 to analyze the XAI’s overall illustration. We developed a search string and six research questions, each answered briefly. According to our SLR findings, 14 of the 131 research studies extracted from various databases addressed XAI techniques. Additionally, 14 research studies using XAI techniques in the Healthcare and Finance domains were chosen to compare with the findings of this literature review. These studies were chosen because the researchers frequently cited them. Following our findings, XAI approaches were mainly employed in the Software Fault Predictions (SFP) subdomain of SE, and all studies used local explanations. Python programming libraries were used for implementation, with “sci-kit” being the most widely used, followed by “caret” of the R programming language. In addition, the “LIME” tool is the most commonly used in the SFP domain for local explanations, followed by the “SHAP” tool. The findings also show that local and global explanations were used in the Healthcare and Finance domains. The most widely used Python programming library is the “sci-kit learn” library, with the “SHAP” tool being the most commonly used explanation tool in the Finance and Healthcare domains. Finally, whereas XAI in SE is new, XAI methods have been used in conjunction with traditional machine learning models. However, there is a lack of benchmark evaluation metrics in the existing literature, leading to researcher confusion and unreliable comparison standards.

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Section: 3mentioning

confidence: 99%

A Systematic Literature Review of Explainable Artificial Intelligence (XAI) in Software Engineering (SE)

khan,

Ali,

Khan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhao (2018) explores the possibilities of improving the social responsibility of artificial intelligence and the achievement of sustainable development of artificial intelligence, through the adoption of international social responsibility (ISO 26000 standard). Lachuer and Jabeur (2022) examine the relationship between corporate social responsibility and corporate financial performance in a rising market. For their purposes, they use an innovative approach based on artificial intelligence (XAI).…”

Section: The Place Of Artificial Intelligence In Csrmentioning

confidence: 99%

Increasing CSR, through a Methodology for Intelligent Personal Efficiency of Employees

Biolcheva,

Valchev

2023

str

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. Corporate social responsibility affects the interests of a wide range of stakeholders, but among the most important for it are the employees themselves in the organizations. This material is aimed at increasing the efficiency of employees by reducing their personal stress levels in the work environment. For this purpose, a methodology for personal effectiveness of employees (PEE) is proposed here. PEE is a conceptual view on the collaboration of different intelligent tools to monitor the stress levels in employees and offer different individual mechanisms to deal with it, according to the specific tasks, capabilities, and psycho-physical state. The proposed methodology is considered an alternative to an accurate and impartial mechanism through which CSR can be increased, it is applicable in a wide range of businesses and has a beneficial effect on employees.

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The hard problem of the androcentric context of AI: challenges for EU policy agendas

González-Martín

2024

Ethics in Online AI-based Systems

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Explainable artificial intelligence modeling for corporate social responsibility and financial performance

Cited by 7 publications

References 48 publications

A Systematic Literature Review of Explainable Artificial Intelligence (XAI) in Software Engineering (SE)

A Systematic Literature Review of Explainable Artificial Intelligence (XAI) in Software Engineering (SE)

Increasing CSR, through a Methodology for Intelligent Personal Efficiency of Employees

The hard problem of the androcentric context of AI: challenges for EU policy agendas

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