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Super-forecasting the 'technological singularity' risks from artificial intelligence
Petar Radanliev1, David De Roure1, Carsten Maple2
1Oxford e-Research Centre, Department of Engineering Sciences, University of Oxford, Oxford, UK.
Evolving Systems
|July 31, 2023
Summary
This study introduces a new framework for managing artificial intelligence (AI) risks, focusing on system resilience against cyber threats and AI failures. It forecasts emerging AI-driven cyber risks and develops AI-based defense strategies.
Area of Science:
- Computer Science
- Artificial Intelligence
- Cybersecurity
Background:
- The increasing integration of artificial intelligence (AI) presents novel cybersecurity challenges.
- Traditional security measures are insufficient against sophisticated AI-driven threats and potential system failures.
Purpose of the Study:
- To develop a framework for forecasting and counteracting AI-related cybersecurity risks.
- To analyze system responses to internal and external failures and compromises.
- To explore AI's role in defense against AI-specific cyber threats.
Main Methods:
- Constructing multiple risk forecasts related to AI and cybersecurity.
- Synthesizing forecasts with existing literature and data sources.
- Developing novel methodologies for AI-driven cyber defense.
Main Results:
- A new framework for counteracting AI risks, emphasizing system resilience.
- Forecasts of emerging cyber risks from AI integration.
- Methodologies for using AI to defend against AI attacks and rogue devices.
Conclusions:
- Securing all systems is infeasible; focus must shift to resilience and response.
- AI can be leveraged for advanced cyber defense and prevention of AI-related threats.
- Novel AI-driven methodologies are crucial for future cybersecurity.
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