Panel: Economic Policy and Governance during Pandemics using AI
- URL: http://arxiv.org/abs/2010.15585v1
- Date: Tue, 20 Oct 2020 22:09:59 GMT
- Title: Panel: Economic Policy and Governance during Pandemics using AI
- Authors: Feras A. Batarseh and Munisamy Gopinath
- Abstract summary: Outlier events create uncertainty along the entire supply chain in addition to intervening policy responses to mitigate their adverse effects.
Artificial Intelligence (AI) methods provide an opportunity to better understand outcomes during outlier events.
Employing AI can provide guidance to decision making suppliers, farmers, processors, wholesalers, and retailers along the supply chain.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The global food supply chain (starting at farms and ending with consumers)
has been seriously disrupted by many outlier events such as trade wars, the
China demand shock, natural disasters, and pandemics. Outlier events create
uncertainty along the entire supply chain in addition to intervening policy
responses to mitigate their adverse effects. Artificial Intelligence (AI)
methods (i.e. machine/reinforcement/deep learning) provide an opportunity to
better understand outcomes during outlier events by identifying regular,
irregular and contextual components. Employing AI can provide guidance to
decision making suppliers, farmers, processors, wholesalers, and retailers
along the supply chain, and policy makers to facilitate welfare-improving
outcomes. This panel discusses these issues.
Related papers
- Where does AI come from? A global case study across Europe, Africa, and Latin America [0.0]
This article examines the supply chains that shape the supply chains of artificial intelligence (AI) through outsourced and offshored data work.
We conduct a global case study of the digitally enabled organisation of data work in France, Madagascar, and Venezuela.
arXiv Detail & Related papers (2025-02-07T11:54:02Z) - From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
Much of this debate has concentrated on direct impact without addressing the significant indirect effects.
This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption.
We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.
arXiv Detail & Related papers (2025-01-27T22:45:06Z) - Managing extreme AI risks amid rapid progress [171.05448842016125]
We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems.
There is a lack of consensus about how exactly such risks arise, and how to manage them.
Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
arXiv Detail & Related papers (2023-10-26T17:59:06Z) - The Promise and Peril of Artificial Intelligence -- Violet Teaming
Offers a Balanced Path Forward [56.16884466478886]
This paper reviews emerging issues with opaque and uncontrollable AI systems.
It proposes an integrative framework called violet teaming to develop reliable and responsible AI.
It emerged from AI safety research to manage risks proactively by design.
arXiv Detail & Related papers (2023-08-28T02:10:38Z) - An Overview of Catastrophic AI Risks [38.84933208563934]
This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories.
Malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs.
organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents.
rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans.
arXiv Detail & Related papers (2023-06-21T03:35:06Z) - Trustworthy, responsible, ethical AI in manufacturing and supply chains:
synthesis and emerging research questions [59.34177693293227]
We explore the applicability of responsible, ethical, and trustworthy AI within the context of manufacturing.
We then use a broadened adaptation of a machine learning lifecycle to discuss, through the use of illustrative examples, how each step may result in a given AI trustworthiness concern.
arXiv Detail & Related papers (2023-05-19T10:43:06Z) - Empowering Agrifood System with Artificial Intelligence: A Survey of the Progress, Challenges and Opportunities [86.89427012495457]
We review how AI techniques can transform agrifood systems and contribute to the modern agrifood industry.
We present a progress review of AI methods in agrifood systems, specifically in agriculture, animal husbandry, and fishery.
We highlight potential challenges and promising research opportunities for transforming modern agrifood systems with AI.
arXiv Detail & Related papers (2023-05-03T05:16:54Z) - Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time.
We discuss how biased models can lead to more negative real-world outcomes for certain groups.
If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
arXiv Detail & Related papers (2023-04-16T11:22:59Z) - Public Policymaking for International Agricultural Trade using
Association Rules and Ensemble Machine Learning [0.0]
Recent shocks to the free trade regime, especially trade disputes among major economies, raise the need for improved predictions to inform policy decisions.
We present novel methods that predict and associate food and agricultural commodities traded internationally.
arXiv Detail & Related papers (2021-11-15T02:58:03Z) - The challenges and realities of retailing in a COVID-19 world:
Identifying trending and Vital During Crisis keywords during Covid-19 using
Machine Learning (Austria as a case study) [0.0]
It is recommended to opt for forecasting against trending based benchmark because auditing a future forecast puts more focus on seasonality.
The forecasting models provide with end-to-end, real time oversight of the entire supply chain.
arXiv Detail & Related papers (2021-05-10T18:31:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.