Extended Abstract:

In this work, we explore the challenge of improving the reliability, fairness, and transparency of AI decision-making. Our goal is to propose a novel approach using adversarial collaboration in AI decision-making, which involves training a set of "defender" and "attacker" agents to work together towards a common goal. Adversarial collaboration, a technique used in game theory and conflict resolution, has the potential to address these issues by bringing together multiple AI agents with different objectives and training them to work collaboratively towards a common goal [1].

We aim to demonstrate the effectiveness of our approach through a series of experiments on benchmark datasets and real-world problems. We propose a methodology for training a set of "defender" and "attacker" agents using adversarial collaboration. Our approach involves training two neural networks, one acting as a "defender" and the other as an "attacker", to work together to improve the performance of the AI system. The "defender" network aims to improve the accuracy, robustness, and interpretability of the AI system, while the "attacker" network attempts to identify weaknesses and vulnerabilities in the system. By training these networks together in an adversarial setting, we can improve the overall performance of the AI system.

Our approach has yielded promising results, demonstrating significant improvements in the accuracy, robustness, and interpretability of AI systems. And it suggests that adversarial collaboration has the potential to revolutionize the way we design and deploy AI systems, and could pave the way for a new era of safe, trustworthy, and ethical AI.

Nevertheless, further challenges need to be addressed to evaluate the efficacy of this approach in addressing real-world challenges, including but not limited to fraud detection, medical diagnosis, and autonomous driving. 

Reference: [1] M. Jaderberg et al., "Human-level performance in 3D multiplayer games with population-based reinforcement learning," Science, vol. 364, no. 6443, pp. 859-865, May 2019.