Artificial Intelligence: Ethical Risks, Frameworks, and Governance – Part 2
Ethical Theories and Frameworks Guiding Responsible AI
1. Introduction
Following the identification of core ethical risks associated with artificial intelligence (AI) in Part 1, this section explores the ethical frameworks and theories that underpin responsible AI development and application. As AI systems grow in autonomy and impact, ethical clarity becomes essential for guiding design, implementation, and evaluation. Philosophical traditions, interdisciplinary models, and human-centred approaches provide crucial foundations for aligning AI with societal values.
2. Classical Ethical Theories and Their Application to AI
Several well-established moral theories offer insights into the ethical dilemmas posed by AI technologies. These serve as the philosophical bedrock for formulating AI principles and evaluating their consequences.
2.1 Deontological Ethics (Duty-Based Ethics)
- Origin: Associated with Immanuel Kant.
- Principle: Actions are morally right if they adhere to duty or rules, regardless of outcomes.
Application to AI:
- Prioritises inviolable principles such as human dignity, non-harm, and fairness.
- Supports rule-based restrictions in AI systems (e.g., AI must not make lethal decisions).
- Forms the basis of many AI ethics codes and legal safeguards.
Limitation: May lead to rigid outcomes in complex or ambiguous scenarios.
2.2 Utilitarianism (Consequentialism)
- Origin: Jeremy Bentham and John Stuart Mill.
- Principle: An action is right if it produces the greatest good for the greatest number.
Application to AI:
- Favourable for cost-benefit analysis in deploying AI for public health, logistics, or infrastructure.
- Encourages maximising efficiency and well-being through automation and intelligent systems.
Limitation: Risks overlooking minority rights or individual dignity in pursuit of utility.
2.3 Virtue Ethics
- Origin: Aristotle.
- Principle: Focuses on moral character and the cultivation of virtues such as wisdom, justice, and compassion.
Application to AI:
- Encourages value-sensitive design rooted in ethical character, not just outcomes or rules.
- Promotes a culture of ethical awareness among developers, researchers, and policymakers.
Limitation: Less prescriptive in terms of specific actions; relies on contextual judgement.
3. Contemporary Ethical Frameworks for AI
In response to the unique challenges posed by AI, several interdisciplinary frameworks have been developed. These integrate classical ethics with modern technical realities.
3.1 The EFAT Principles
A widely adopted set of foundational principles for trustworthy AI includes:
| Principle | Description |
|---|---|
| Explainability | AI decisions must be understandable and traceable. |
| Fairness | AI systems must not discriminate or reinforce injustice. |
| Accountability | Clear responsibility must exist for AI outcomes. |
| Transparency | Processes and data used in AI must be open and auditable. |
These principles have informed the guidelines of the OECD, IEEE, and EU High-Level Expert Group on AI.
3.2 Human-Centred and Value-Aligned AI
Human-centred AI design ensures that technology serves human well-being, autonomy, and social good.
- Value-sensitive design (VSD) integrates ethical values throughout the development lifecycle.
- Human-in-the-loop (HITL) models ensure that human judgement remains essential in decision-making, especially in high-stakes applications such as healthcare and defence.
- Alignment problem research focuses on ensuring that AI systems reflect human goals and moral values, particularly in the context of AGI.
3.3 The Four Pillars of Responsible AI (based on European and global policy initiatives)
- Beneficence – Promote human welfare and positive social impact.
- Non-maleficence – Avoid harm, including unintended consequences.
- Justice – Ensure fairness, inclusiveness, and equality.
- Autonomy – Respect individual rights and decision-making capacity.
These principles echo bioethical frameworks and have been incorporated into AI codes of ethics across academic, governmental, and corporate sectors.
4. Industry and Institutional Approaches to Ethical AI
Several organisations and technology companies have developed AI ethics frameworks. While these vary in scope, common commitments include:
- Microsoft’s Responsible AI Principles: fairness, reliability, privacy, inclusiveness, transparency, accountability.
- Google’s AI Principles: avoiding harm, incorporating privacy design, being socially beneficial.
- UNESCO AI Ethics Guidelines: universal human rights focus, especially regarding cultural diversity and environmental sustainability.
These illustrate attempts to operationalise ethics into concrete guidelines. However, critics argue that voluntary principles lack enforcement mechanisms, often leading to “ethics-washing.”
5. Challenges in Applying Ethical Frameworks
Despite growing agreement on key principles, several challenges remain:
- Contextual complexity: Ethical choices in AI are rarely binary and often depend on cultural, economic, and situational variables.
- Conflict between principles: For example, increasing transparency may compromise privacy.
- Lack of enforcement: Many frameworks are non-binding, leaving compliance optional.
- Rapid innovation: Technological development often outpaces ethical or regulatory responses.
Hence, while ethical frameworks are necessary, they must be complemented by enforceable governance mechanisms, addressed in Part 3.
6. Conclusion (Part 2)
The ethical evaluation of AI draws from both classical moral theories and contemporary interdisciplinary frameworks. Principles such as fairness, accountability, and transparency are essential but must be embedded in practice through proactive design and institutional commitment.
As AI systems become more integrated into everyday life, an ethics-by-design approach—where ethics is built into the system architecture, developer mindset, and organisational culture—is no longer optional, but essential.
The final part of this article (Part 3) will examine how these ethical insights translate into global policy, legal regulation, and governance structures that can ensure responsible AI on a societal scale.