An Inquiry Into the Obstacles Hindering the Widespread Use of Artificial Intelligence in Environmental, Social, and Governance Practices

Pramanik, Sabyasachi

doi:10.4018/979-8-3693-2964-1.ch004

Advances in Computational Intelligence and Robotics

2024

DOI: 10.4018/979-8-3693-2964-1.ch004

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An Inquiry Into the Obstacles Hindering the Widespread Use of Artificial Intelligence in Environmental, Social, and Governance Practices

Sabyasachi Pramanik

Abstract: This chapter examines the correlation between artificial intelligence (AI) and the integration of environmental, social, and governance (ESG) factors in contemporary business and technology. The chapter explores how AI might provide practical answers to current urgent social issues regarding sustainability. The chapter focuses on the ethical dimensions of sustainable technologies that address ecological issues, as well as AI-driven forecasts for ESG indicators, ethical supply chains, and forthcoming legislatio… Show more

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Cited by 3 publications

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Financial Sector Hyper-Automation

Policepatil,

Sharma,

Kumar

et al. 2024

Advances in Finance, Accounting, and Economics

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The advent of hyperautomation is driving the financial services sector into a revolutionary age. The substantial impact of hyperautomation technologies—such as machine learning, robotic process automation (RPA), artificial intelligence (AI), and other cutting-edge digital tools—on banking and investing processes is examined in this paper. This review looks at how these technologies are altering the competitive landscape, boosting customer happiness, and changing and improving corporate processes. The first section of the essay gives a general review of the state of financial services today, highlighting traditional methods and the increasing need for innovation to meet shifting consumer needs and market shifts. It then provides a thorough analysis of how hyperautomation technologies are incorporated into several aspects of financial operations, including risk assessment, algorithmic trading, customized banking services, compliance monitoring, and enhanced customer care. The study mostly looks at case studies where hyperautomation has led to significant improvements in productivity, cost reductions, and increased accuracy in data handling and decision-making processes. According to this study, hyperautomation will be a defining feature of the financial services industry in the future, acting as a catalyst for a comprehensive industry revolution.

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Financial Sector Hyper-Automation

Policepatil,

Sharma,

Kumar

et al. 2024

Advances in Finance, Accounting, and Economics

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The Circular Economy

Shukla,

Jain,

Jain

et al. 2024

Practice, Progress, and Proficiency in Sustainability

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The notion of a circular economy has gained traction as a substitute for the antiquated linear model, especially in emerging nations like India. This chapter examines the challenges, possibilities, legislative frameworks, technological advancements, socioeconomic effects, and future prospects of establishing a circular economy in India. By dissecting these elements, the chapter seeks to provide a thorough explanation of how the circular economy may have a significant influence on India's future. India's population and economy are growing quickly, which has led to a rise in waste production and resource consumption. The “take-make-dispose” approach, which is another name for the conventional linear economic model, has been a major factor in promoting this trend. Resources are depleted and the ecosystem is harmed when they are extracted, utilized to manufacture goods, and then discarded as trash. Given India's unique limits and the global push for sustainable development, the linear economic model is no longer feasible.

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A Combinatorial Deep Learning and Deep Prophet Memory Neural Network Method for Predicting Seasonal Product Consumption in Retail Supply Chains

Ahmad,

Ali,

Namdev

et al. 2024

Advances in Business Information Systems and Analytics

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One of the biggest problems in supply chain networks is demand forecasting. It was created to increase demand, profitability, and sales while maximizing stock efficiency and cutting costs. To improve demand forecasting, historical data may be analyzed using a variety of techniques, such as deep learning models, time series analysis, and machine learning. This study develops a hybrid approach to demand prediction. This paper used a deep learning-based Deep Prophet memory neural network forecasting approach, which combined temporal, historical, trend, and seasonal data, to develop a more accurate model. To our knowledge, this is the first study to integrate prophet with a long short-term memory network (LSTM) for demand forecasting. At first, the data may be obtained here, and linear clipping data normalization (LCDN) was used for pre-processing. After that, the bivariate wrapper forward elimination approach may be used to extract the features. The Sequential Bayesian Inference Optimization (SBIO) approach is used to choose the specialized features from the retrieved characteristics. Ultimately, the demand for the items was examined after the Deep Prophet Memory Neural Network (DPMNN) approach was modified. Using M5 Forecasting and Predict Future Sales datasets in a Python context, the built system is used and evaluated. Numerous exhaustive and comparative trials show that the suggested demand forecasting technique performs much better than state-of-the-art research.

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An Inquiry Into the Obstacles Hindering the Widespread Use of Artificial Intelligence in Environmental, Social, and Governance Practices

Cited by 3 publications

References 46 publications

Financial Sector Hyper-Automation

Financial Sector Hyper-Automation

The Circular Economy

A Combinatorial Deep Learning and Deep Prophet Memory Neural Network Method for Predicting Seasonal Product Consumption in Retail Supply Chains

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