Artificial intelligence across company borders
- URL: http://arxiv.org/abs/2107.03912v1
- Date: Mon, 21 Jun 2021 11:56:41 GMT
- Title: Artificial intelligence across company borders
- Authors: Olga Fink, Torbj{\o}rn Netland, Stefan Feuerriegel
- Abstract summary: Cross-company AI can be effective without data disclosure.
In this Viewpoint, we discuss the use, value, and implications of this approach in a cross-company setting.
- Score: 17.27331855560747
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Artificial intelligence (AI) has become a valued technology in many
companies. At the same time, a substantial potential for utilizing AI
\emph{across} company borders has remained largely untapped. An inhibiting
factor concerns disclosure of data to external parties, which raises legitimate
concerns about intellectual property rights, privacy issues, and cybersecurity
risks. Combining federated learning with domain adaptation can provide a
solution to this problem by enabling effective cross-company AI without data
disclosure. In this Viewpoint, we discuss the use, value, and implications of
this approach in a cross-company setting.
Related papers
- Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline [0.0]
This paper explores the potential of integrating Artificial Intelligence (AI) into Cyber Threat Intelligence (CTI)
We provide a blueprint of an AI-enhanced CTI processing pipeline, and detail its components and functionalities.
We discuss ethical dilemmas, potential biases, and the imperative for transparency in AI-driven decisions.
arXiv Detail & Related papers (2024-03-05T19:03:56Z) - The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge.
This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI)
We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
arXiv Detail & Related papers (2024-01-03T07:47:22Z) - AI Potentiality and Awareness: A Position Paper from the Perspective of
Human-AI Teaming in Cybersecurity [18.324118502535775]
We argue that human-AI teaming is worthwhile in cybersecurity.
We emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise.
arXiv Detail & Related papers (2023-09-28T01:20:44Z) - 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) - 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) - Explainable Artificial Intelligence and Cybersecurity: A Systematic
Literature Review [0.799536002595393]
XAI aims to make the operation of AI algorithms more interpretable for its users and developers.
This work seeks to investigate the current research scenario on XAI applied to cybersecurity.
arXiv Detail & Related papers (2023-02-27T17:47:56Z) - 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) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - Examining the Differential Risk from High-level Artificial Intelligence
and the Question of Control [0.0]
The extent and scope of future AI capabilities remain a key uncertainty.
There are concerns over the extent of integration and oversight of AI opaque decision processes.
This study presents a hierarchical complex systems framework to model AI risk and provide a template for alternative futures analysis.
arXiv Detail & Related papers (2022-11-06T15:46:02Z) - 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) - Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome.
Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations.
We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
arXiv Detail & Related papers (2021-06-14T20:48:48Z)
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.