Compliance Costs of AI Technology Commercialization: A Field Deployment
Perspective
- URL: http://arxiv.org/abs/2301.13454v1
- Date: Tue, 31 Jan 2023 07:22:12 GMT
- Title: Compliance Costs of AI Technology Commercialization: A Field Deployment
Perspective
- Authors: Weiyue Wu and Shaoshan Liu
- Abstract summary: Many AI startups are not financially prepared to cope with a broad spectrum of regulatory requirements.
Complex and varying regulatory processes across the globe subtly give advantages to well-established and resourceful technology firms.
The continuation of this trend may phase out the majority of AI startups and lead to giant technology firms' monopolies of AI technologies.
- Score: 1.637145148171519
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: While Artificial Intelligence (AI) technologies are progressing fast,
compliance costs have become a huge financial burden for AI startups, which are
already constrained on research & development budgets. This situation creates a
compliance trap, as many AI startups are not financially prepared to cope with
a broad spectrum of regulatory requirements. Particularly, the complex and
varying regulatory processes across the globe subtly give advantages to
well-established and resourceful technology firms over resource-constrained AI
startups [1]. The continuation of this trend may phase out the majority of AI
startups and lead to giant technology firms' monopolies of AI technologies. To
demonstrate the reality of the compliance trap, from a field deployment
perspective, we delve into the details of compliance costs of AI commercial
operations.
Related papers
- The Role of AI in Financial Forecasting: ChatGPT's Potential and Challenges [0.9217021281095907]
The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting.
The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things.
The integration of AI challenges regulatory and ethical issues in the financial sector, as well as the implications for data privacy protection.
arXiv Detail & Related papers (2024-11-07T15:35:16Z) - AI Horizon Scanning -- White Paper p3395, IEEE-SA. Part III: Technology Watch: a selection of key developments, emerging technologies, and industry trends in Artificial Intelligence [0.3277163122167434]
Generative Artificial Intelligence (AI) technologies are in a phase of unprecedented rapid development following the landmark release of Chat-GPT.
As the deployment of AI products rises geometrically, considerable attention is being given to the threats and opportunities that AI technologies offer.
This manuscript is the third of a series of White Papers informing the development of IEEE-SA's p3995 it Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence Models'
arXiv Detail & Related papers (2024-11-05T19:04:42Z) - Comprehensive Overview of Artificial Intelligence Applications in Modern Industries [0.3374875022248866]
This paper explores the applications of AI across four key sectors: healthcare, finance, manufacturing, and retail.
We discuss the implications of AI integration, including ethical considerations, the future trajectory of AI development, and its potential to drive economic growth.
arXiv Detail & Related papers (2024-09-19T19:22:52Z) - Choosing the Right Path for AI Integration in Engineering Companies: A
Strategic Guide [4.327763441385369]
The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution.
The framework might also help engineering companies choose the optimum AI approach to create business value.
arXiv Detail & Related papers (2023-12-25T11:58:37Z) - 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) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - A Brief Overview of AI Governance for Responsible Machine Learning
Systems [3.222802562733787]
This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI.
Due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies.
arXiv Detail & Related papers (2022-11-21T23:48:51Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Validate and Enable Machine Learning in Industrial AI [47.20869253934116]
Industrial AI promises more efficient future industrial control systems.
The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models.
arXiv Detail & Related papers (2020-10-30T20:33:05Z) - Qlib: An AI-oriented Quantitative Investment Platform [86.8580406876954]
AI technologies have raised new challenges to the quantitative investment system.
Qlib aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
arXiv Detail & Related papers (2020-09-22T12:57:10Z)
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.