Can Artificial Intelligence Alleviate Resource Scarcity?

During summer 2017, I explored the implications of the potential application of artificial intelligence (AI) to resource management at the Centre for the Study of Existential Risk (CSER) at the University of Cambridge in the United Kingdom. Alongside my mentor, Dr. Simon Beard, I sought to determine the most noteworthy risks and benefits associated with developing AI that could offer agricultural guidance and that could someday offer insight into more efficient, effective, and equitable resource distribution. My research, funded by a Summer Undergraduate Research Fellowship (SURF) grant, involved discussing AI-related issues in the context of resource scarcity with academics and experts in the fields of AI, climate science, data analytics, economics, ethics, and robotics. I found that while AI could present a solution to the problem of scarcity by harnessing data and algorithms to increase agricultural yield, the technology also must be considered in the context of risks—including bias and a lack of trustworthiness. If the positive potential and risks associated with AI for resource management are thoughtfully considered throughout development, the technology could improve food security and ultimately contribute to a better future

Developing a composite index of economic activity for Australia

This Economics Research Note outlines the development of a monthly Ai Group 'composite' index of economic activity for Australia. A simple weighted composite index is outlined, covering the three Ai Group performance indices as well proxies for the rural and mining sectors using available monthly data. This simple index shows that the economy has been generally in a slight contractionary phase in recent months, consistent with benign recent official data on the Australian economy.Business indices

The Multifaceted Relationship Between AI and Economics: Impacts, Challenges, and Insights

Artificial intelligence (AI) has the potential to enhance decision-making by offering precise and timely information to businesses and policymakers. This study delves into the intricate relationship between AI and economics, with a specific focus on three key domains: Supply Chain Optimization, Financial Fraud Detection, and Automation's Impact on the Workforce. By shedding light on both the advantages and challenges of AI integration in economics, this research aims to contribute to the ongoing discussion. The research objectives encompass exploring AI's influence on the multifaceted relationship with economics, offering valuable insights for policymakers, industry stakeholders, and researchers

3Es for AI: Economics, Explanation, Epistemology

This article locates its roots/routes in multiple disciplinary formations and it seeks to advance critical thinking about an aspect of our contemporary socio-technical challenges by bracketing three knowledge formations—artificial intelligence (AI), economics, and epistemology—that have not often been considered together. In doing so, it responds to the growing calls for the necessity of further transdisciplinary engagements that have emanated from work in AI and also from other disciplines. The structure of the argument here is as follows. First, I begin by demonstrating how and why explanation is a problem in AI (“XAI problem”) and what directions are being taken by recent research that draws upon social sciences to address this, noting how there is a conspicuous lack of reference in this literature to economics. Second, I identify and analyze a problem of explanation that has long plagued economics too as a discipline. I show how only a few economists have ever attempted to grapple with this problem and provide their perspectives. Third, I provide an original genealogy of explanation in economics, demonstrating the changing nature of what was meant by an explanation. These systematic changes in consensual understanding of what occurs when something is said to have been “explained”, have reflected the methodological compromises that were rendered necessary to serve different epistemological tensions over time. Lastly, I identify the various relevant historical and conceptual overlaps between economics and AI. I conclude by suggesting that we must pay greater attention to the epistemologies underpinning socio-technical knowledges about the human. The problem of explanation in AI, like the problem of explanation in economics, is perhaps not only, or really, a problem of satisfactory explanation provision alone, but interwoven with questions of competing epistemological and ethical choices and related to the ways in which we choose sociotechnical arrangements and offer consent to be governed by them

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

Digitalization Strategies in ASEAN MSMEs Harnessing AI for Competitive Advantage in the Global Value Chain: A Model Conceptual

The implementation of artificial intelligence (AI) and digitalization in ASEAN MSMEs has garnered significant attention in optimizing their contributions to the global value chain. This study aims to develop a conceptual model that analyzes digitalization strategies in ASEAN MSMEs, harnessing AI to attain competitive advantage within the global value chain. This model integrates concepts from business strategy theory, information technology, and global economics. The primary contribution of this research lies in conceptualizing a comprehensive view of how ASEAN MSMEs can embrace AI as a primary driver in formulating effective digital strategies, thus enabling them to compete in an increasingly interconnected and competitive global marketplace. Keywords: Digitalization, Artificial Intelligence, ASEAN MSMEs, Global Value Chain, Business Strategy, Competitive Advantage, Information Technology, Global Economics

Avian Influenza Threat and its Potential Impact on Demand for Chicken and Eggs

A highly pathogenic H5N1 strain of avian influenza (AI) has been confirmed in 420 human cases and has caused 257 deaths in the world starting from 2003. Using face-to-face interviews, our data were collected by utilizing a stratified sampling scheme following the distribution of gender and age in three major metropolitan areas in Taiwan, including Taipei, Taichung, and Kaohsiung. The questionnaire was designed to retrieve information including AI knowledge, risk perceptions, and behavioral changes of two types of consumers, primary shoppers and general consumers. In total, 501 primary shoppers and 505 general consumers completed the survey in June 2007 and were recorded for analysis. The empirical results show several interesting findings, especially, that risk perception and some socioeconomic characteristics such as age are the key factor which determines changes in purchasing behavior.avian influenza, knowledge, risk perception, Tobit model, Taiwan, Consumer/Household Economics, Livestock Production/Industries, M30,