Predicting savings adequacy using machine learning: A behavioural economics approach

Alam, Muhammad Aizat bin Zainal; Yong, Chen‐Chen; Mansor, Norma Binti

doi:10.1016/j.eswa.2022.117502

Cited by 3 publications

(1 citation statement)

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“…Project #2-A behavioral economics approach to predicting savings adequacy using machine learning [27] This project has the aim of proposing a machine-learning-based method that can predict individuals' savings adequacy in the presence of mental accounting. The proposed predictive model classifies wealth and consumption.…”

Section: Resultsmentioning

confidence: 99%

The Dilemma of Rapid AI Advancements: Striking a Balance between Innovation and Regulation by Pursuing Risk-Aware Value Creation

Ricciardi Celsi

2023

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This paper proposes the concept of risk-aware actual value as a pivotal metric for evaluating the viability and desirability of AI projects and services in accordance with the AI Act. The framework establishes a direct correlation between the level of risk associated with a product or service and the resulting actual value generated. The AI Act reflects a concerted effort to harness the potential of AI while mitigating risks. The risk-based approach aligns regulatory measures with the specific attributes and potential hazards of distinct AI applications. As trilogue negotiations continue, the regulatory approach of the EU is evolving, highlighting its commitment to responsible and forward-thinking AI governance. Through a dedicated analysis of the AI Act, it becomes evident that products or services categorized as high-risk carry substantial compliance obligations, consequently diminishing their potential value. This underscores the imperative of exercising caution when engaging in projects with elevated risk profiles. Conversely, products or services characterized by lower risk levels are poised to accrue more substantial benefits from their AI and data potential, highlighting the incentive for a discerning approach to risk assessment. Methodologically, we propose an extension of an integrated AI risk management framework that is already existing in the literature, combining it with existing frameworks for measuring value creation from harnessing AI potential. Additionally, we contribute to the applied field of AI by implementing the proposed risk framework across nine industry-relevant use cases. In summation, this paper furnishes a comprehensive approach to achieving equilibrium between innovation and regulation in the realm of AI projects and services. By employing the risk-aware actual value metric, stakeholders are empowered to make informed decisions that prioritize safety and maximize the potential benefits of AI initiatives. This framework may stand as a reference point in this time when fostering responsible and sustainable AI development within the industry becomes of paramount importance.

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Section: Resultsmentioning

confidence: 99%