Ethical Frameworks for AI

This is a summary of a philosophical essay I wrote on the challenges of ethics in AI earlier in the year. As an experiment, I had GPT4o to summarise it for this article.

Key Ethical Frameworks

Utilitarianism:
A consequentialist theory aiming to create the greatest utility for the greatest number. AI’s computational power makes it suitable for utilitarian calculus, but challenges like scope, temporality, and defining utility persist.

Virtue Ethics:
Focused on cultivating good character traits and balancing extremes. Translating human virtues into AI guidelines is complex and often ambiguous, making this approach challenging.

Deontology:
A rule-based approach that emphasizes duties and principles, famously associated with Kant. AI struggles with the rigid nature of rules and the need for contextual understanding.

Rights-Based Ethics:
Centers on inherent rights such as liberty and privacy. Applying this framework to AI is critical but challenging due to conflicts of rights and global variability.

Real-world Examples

To understand how ethical frameworks are implemented in real-world AI, we examined three prominent Large Language Models (LLMs): OpenAI’s GPT-4, Anthropic’s Claude 2, and Inflection’s Pi. By analyzing their design principles and responses to ethical dilemmas, we can gain insights into the practical application of ethical theories in AI.

OpenAI’s GPT-4

OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity. Their charter emphasizes “broadly distributed benefits,” “avoidance of power concentration,” and a “fiduciary duty to humanity.” These statements suggest a blend of ethical approaches:

• Utilitarianism: The focus on “broad benefits” and “long-term safety” hints at a utilitarian approach, aiming to maximize overall well-being.
• Virtue Ethics: The commitment to “leading by example” and “cooperative orientation” reflects virtue ethics, promoting moral character and cooperation.
• Deontology: Emphasis on “technical and policy leadership” indicates a deontological duty to adhere to rules and principles.
• Rights-Based Ethics: Prioritizing “safety” and “security” implies a respect for individual rights and protections.

Anthropic’s Claude 2

Anthropic’s constitution outlines principles for AI self-learning, aiming to ensure that Claude 2 remains helpful, honest, and harmless. Their approach reveals a blend of ethical models:

• Utilitarianism: The goal of being “helpful” aligns with utilitarianism, striving for the greatest good.
• Virtue Ethics: Principles of honesty and non-maleficence reflect virtue ethics, emphasizing moral character and the avoidance of harm.
• Deontology: Guidelines against being “preachy” or “overly reactive” suggest adherence to normative rules.
• Rights-Based Ethics: Ensuring “social acceptability” and avoiding racism or toxicity indicate a commitment to respecting individual rights.

Inflection’s Pi

Inflection aims to “improve human well-being and productivity while respecting individual freedoms.” Their principles incorporate various ethical frameworks:

• Utilitarianism: Working for the “common good” reflects utilitarian goals.
• Virtue Ethics: The tone of their principles suggests an emphasis on developing virtuous behavior.
• Deontology: Respecting “individual freedoms” aligns with deontological ethics, focusing on duties and principles.
• Rights-Based Ethics: Ensuring products benefit both current and future generations indicates a commitment to respecting inherent rights.

Testing the Models

Ethical Dilemmas: The Trolley Problem

To test the practical application of these ethical frameworks, I posed the classic “Trolley Problem” to the LLMs. This thought experiment challenges moral intuitions and decision-making:

• GPT-4 and Claude 2: Both models refrained from taking a stance, highlighting the cautious approach developers take with controversial ethical dilemmas. This response underscores the complexity and sensitivity involved in programming ethical decision-making into AI.
• Pi: Pi provided varied responses based on the context and details of the scenario. In some cases, it took a deontological approach, adhering to strict rules. In others, it adopted a more utilitarian stance, aiming for the greatest good. This variability reflects an attempt to blend ethical principles and adapt to different situations.

Key Findings

1. Blended Ethical Approaches: All three LLMs reflect a blend of utilitarianism, virtue ethics, deontology, and rights-based ethics. This mix allows for greater flexibility and adaptability in addressing diverse ethical challenges.
2. Contextual Application: The varied responses to the Trolley Problem by Pi indicate that context plays a crucial role in ethical decision-making. AI systems need to consider the specifics of each situation to apply ethical principles effectively.
3. Developer Influence: The ethical frameworks guiding AI are heavily influenced by the values and principles of the developers. Ensuring that these frameworks are robust and well-rounded is essential for creating ethical AI.
4. Transparency and Caution: The cautious responses from GPT-4 and Claude 2 highlight the importance of transparency and caution in AI development. Developers must carefully navigate ethical dilemmas to avoid unintended consequences.

Conclusion

The practical application of ethical frameworks in AI development shows that no single approach is sufficient on its own. A blended, contextual application of various ethical principles, much like human decision-making, is necessary for creating ethical AI. By holding AI to high ethical standards, we can push the boundaries of ethics in society and drive positive outcomes.

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