arXiv:2404.16957v1 Announce Type: new
Abstract: The pervasive integration of Artificial Intelligence (AI) has introduced complex challenges in the responsibility and accountability in the event of incidents involving AI-enabled systems. The interconnectivity of these systems, ethical concerns of AI-induced incidents, coupled with uncertainties in AI technology and the absence of corresponding regulations, have made traditional responsibility attribution challenging. To this end, this work proposes a Computational Reflective Equilibrium (CRE) approach to establish a coherent and ethically acceptable responsibility attribution framework for all stakeholders. The computational approach provides a structured analysis that overcomes the limitations of conceptual approaches in dealing with dynamic and multifaceted scenarios, showcasing the framework’s explainability, coherence, and adaptivity properties in the responsibility attribution process. We examine the pivotal role of the initial activation level associated with claims in equilibrium computation. Using an AI-assisted medical decision-support system as a case study, we illustrate how different initializations lead to diverse responsibility distributions. The framework offers valuable insights into accountability in AI-induced incidents, facilitating the development of a sustainable and resilient system through continuous monitoring, revision, and reflection.
Analysis of the Content: Computational Reflective Equilibrium for Responsibility Attribution in AI-Enabled Systems
The rapid integration of Artificial Intelligence (AI) in various domains has brought about numerous challenges in terms of responsibility and accountability, especially in the event of incidents involving AI-enabled systems. This paper introduces a novel approach called Computational Reflective Equilibrium (CRE) to address these challenges and establish a coherent and ethically acceptable responsibility attribution framework for all stakeholders.
The article highlights the complexity of responsibility attribution in AI-induced incidents. Interconnectivity of these systems, coupled with ethical concerns and uncertainties surrounding AI technology, further complicate the task. It emphasizes the need for a computational approach that can analyze dynamic and multifaceted scenarios effectively, offering explainability, coherence, and adaptivity in the responsibility attribution process.
The proposed CRE framework offers valuable insights into accountability in AI-induced incidents by integrating a structured computational analysis. This multi-disciplinary approach takes into account various factors, including the initial activation level associated with claims in equilibrium computation. By considering different initializations, the framework demonstrates how responsibility distributions can vary in AI-assisted medical decision-support systems.
The significance of this research is its potential to address the lack of regulations and guidelines in determining responsibility in AI incidents. By providing a comprehensive and adaptable framework, it promotes the development of sustainable and resilient systems through continuous monitoring, revision, and reflection. This approach encourages stakeholders to collaborate and establish an ethically acceptable responsibility attribution process.
Interdisciplinary Nature of the Concepts
The concepts discussed in this article highlight the multi-disciplinary nature of responsibility attribution in AI-enabled systems. The integration of AI technology in various domains requires expertise from diverse fields, including computer science, ethics, law, and philosophy.
From a computer science perspective, the computational approach proposed in the CRE framework allows for efficient analysis of complex scenarios by leveraging AI algorithms and techniques. It provides a systematic way to evaluate the responsibility distribution and ensures transparency in decision-making processes.
Ethics plays a crucial role in determining the ethical acceptability of responsibility attribution frameworks. The ethical concerns associated with AI-induced incidents, such as algorithmic bias and privacy violations, need to be addressed to establish trust and accountability. The CRE framework emphasizes the importance of ethically acceptable responsibility attribution and offers a structured approach to align AI systems with ethical considerations.
The legal dimension of responsibility attribution is also essential in defining liability and accountability in AI incidents. The absence of corresponding regulations for AI technology creates challenges in determining legal responsibilities. The CRE framework can provide a foundation for developing future legal frameworks by offering a transparent and adaptable responsibility attribution process.
Lastly, philosophy contributes to the conceptual underpinnings of responsibility attribution. The CRE framework incorporates reflective equilibrium, a philosophical concept rooted in balancing conflicting claims and beliefs. This integration allows for a coherent and justifiable responsibility attribution process, considering various perspectives and values.
Future Directions
While the proposed CRE framework presents a pioneering approach to responsibility attribution in AI-enabled systems, there are several avenues for further research and development.
Firstly, the computational analysis of responsibility attribution could benefit from advancements in AI explainability. By enhancing the interpretability of AI systems, it becomes easier to understand the reasoning behind responsibility distributions and ensure fairness and transparency.
Secondly, extending the framework to include real-world case studies from different domains would enhance its applicability and practical value. Each domain may pose unique challenges and ethical considerations, and analyzing them within the CRE framework would provide domain-specific insights.
Additionally, exploring the integration of stakeholder perspectives and values in responsibility attribution can further enhance the ethical acceptability of the framework. Incorporating diverse viewpoints and allowing for stakeholder input can lead to fairer responsibility distributions.
In conclusion, the Computational Reflective Equilibrium (CRE) framework offers a novel and multi-disciplinary approach to responsibility attribution in AI-enabled systems. By addressing the complexities and uncertainties associated with AI-induced incidents, it promotes ethical acceptability and contributes to the development of sustainable and resilient AI systems.