Analyzing Ethical Agency in AI through Political Theory: A Critical Examination
Introduction
The paper leverages concepts from political theory to critically evaluate the prevailing assumptions in AI research about ethical agency in AI systems. It proposes a novel approach by distinguishing between two philosophical perspectives on agency—mechanistic and volitional—and their applicability to Artificial Intelligence, particularly focusing on ethical implications. This analytical framework challenges the common representation of AI as ethical agents and suggests viewing AI's ethical dimensions through the lens of political processes instead.
Mechanistic vs. Volitional Views of Agency
Mechanistic Agency in AI
Mechanistic agency, influenced heavily by Platonic ideals, situates ethics within the field of knowledge and the correct processing of information. AI systems, particularly LLMs, are often evaluated under this lens. Mechanistic agency is characterized by the system's ability to execute tasks based on computational 'knowledge' of the environment—a view deeply entrenched in traditional AI evaluation metrics.
- Definitions and Examples: AI systems are described as having representational states (data and context understanding), motivational states (objectives set by design or interaction), and the capability to act on these states.
- Ethical Implications: When viewed as mechanistic agents, ethical evaluation of AI pivots on the system's capability to simulate ideal human ethical reasoning or decision-making processes, leading to questions about whether AI can genuinely qualify as ‘moral agents.’
Volitional Agency in AI
Contrasting sharply with mechanistic viewpoints, volitional agency is rooted in Aristotelian thought and emphasizes decision-making aligned with internal desires and the active formation of a moral self. This view asserts that true agency involves an inward desire not just to act but to act according to a self-directed, reflective process about one's identity and ethical stance.
- Theoretical Incompatibility with AI: AI lacks the intrinsic motivations and the ability for self-directed ethical reflection crucial for volitional agency. Hence, it cannot be considered an agent in this more profound sense since its 'actions' do not arise from any internal moral or existential deliberations.
Practical and Theoretical Implications
The paper argues that portraying AI as an ethical agent, whether under mechanistic or volitional views, is conceptually flawed. It proposes distancing from the agent-centric perspectives and re-focuses the discourse towards understanding AI through the dynamics of political and social processes.
- Mechanistic Agency: Although it can frame AI systems as agents based on their functional capabilities, it fails to address the moral responsibility of AI comprehensively, as AI systems do not possess moral accountability in a genuine sense.
- Volitional Agency: By definition, AI cannot fulfill the criteria of volitional agency because it does not engage in ethical self-formation or possess desires that motivate actions towards becoming a certain 'kind' of entity.
Alternatives to Viewing AI as an Agent
The paper suggests innovative approaches to conceptualizing AI's role in society that move beyond agent-centric models:
- AI as a Function of Political Processes: It posits that AI’s ethical impacts should be viewed as outcomes of political processes, emphasizing the need to scrutinize the collective human intentions and controls that shape AI development and deployment.
- Increased Focus on Application Specificity: AI should be developed and evaluated within well-defined application contexts to clarify expectations around its functionality and ethical dimensions. This approach also calls for adherence to specific normative standards relevant to the application area.
Future Directions and Speculations
Looking forward, the paper encourages ongoing discourse and research that examine AI not as autonomous ethical entities but as technologies profoundly intertwined with human values, governance structures, and political agendas. It opens potential avenues for exploring how AI can enhance collective decision-making processes without overstepping into realms of moral agency better reserved for humans.
Conclusion
In conclusion, the paper brings a much-needed philosophical perspective into AI ethics discussions, highlighting critical limitations in current approaches while paving the way for more nuanced and politically aware methodologies in AI research.