No Light, No Light”: Artificial Intelligence, Moral Authority, and the Ethics of Following in the Dark
Published in Social Sciences and Computational Sciences
The song does not describe a machine, yet it captures with striking accuracy the moral posture that societies are encouraged to adopt toward automated decision-making: trust without understanding, obedience without explanation, and endurance without accountability.
At its core, the song is about following a guiding force that offers no illumination. The speaker remains loyal to an authority that neither explains itself nor reassures, yet continues to command allegiance. This mirrors the ethical structure of contemporary AI systems, particularly in governance, welfare, policing, and platform regulation, where decisions are produced by opaque models, justified by technical necessity, and insulated from meaningful human challenge.
You want a revelation, you want to get right
But it’s a conversation I just can’t have tonight
This captures the procedural displacement that defines algorithmic governance. Citizens seek explanation, appeal, and moral reasoning, but are met instead with technical silence. The “conversation” that should occur between power and subject is replaced by automated outputs framed as neutral or inevitable. Responsibility dissolves into infrastructure.
The song’s insistence on endurance despite the absence of moral clarity reflects what scholars increasingly describe as ethical deskilling. When humans defer judgment to systems perceived as more rational, consistent, or objective, they gradually lose the practice of moral reasoning itself. Decision-making becomes procedural rather than ethical, statistical rather than contextual. What remains is compliance, not conscience.
Would you leave me
If I told you what I’ve done?
Here emerges the theme of moral residue , the guilt that remains even when actions are procedurally justified. In AI-mediated environments, institutions often claim legitimacy through lawful process or model accuracy, yet individuals within those systems continue to experience unease, shame, and dissonance. The law may be satisfied, but justice remains unsettled. This gap between legality and legitimacy is precisely where algorithmic governance is most dangerous: it permits harm while diffusing blame.
The refrain ~ “No light, no light” ~ functions not as despair, but as diagnosis. It names a world in which guidance exists without understanding, authority without explanation, and outcomes without narrative. In ethical terms, this is a world where instrumental rationality replaces moral reasoning. Systems optimize, but do not justify. They calculate, but do not care.
What makes the song especially resonant for AI ethics is that the speaker does not reject the authority she follows. Instead, she internalizes the failure of illumination as a personal deficiency.
And I would leave you, but the light’s too bright
This is precisely the bind of modern technological dependence. Exit is possible in theory, but costly in practice. Opting out of digital infrastructures increasingly means exclusion from welfare, employment, credit, and even political participation. Structural coercion is disguised as voluntary participation. Individuals remain inside systems they mistrust because survival depends on compliance.
From a legal and policy perspective, this maps onto the erosion of meaningful consent and procedural fairness in algorithmic environments. When systems are unavoidable and unchallengeable, rights lose their operational force. Due process becomes symbolic. Transparency becomes performative. Ethics becomes an afterthought added to already-deployed technologies.
Yet the song is not merely about domination; it is also about complicity. The speaker stays. She adapts. She loves the very force that deprives her of clarity. This reflects what critical theorists have long warned: power is most stable when it is emotionally internalized, not externally imposed. Algorithmic authority gains legitimacy not only through institutional adoption, but through everyday reliance and convenience.
In this sense, No Light, No Light becomes a meditation on the quiet transformation of moral agency in the age of intelligent systems. Harm no longer arrives as overt injustice, but as normalized procedure. Violence is no longer dramatic, but statistical. Responsibility no longer has a face.
The ethical crisis of artificial intelligence, then, is not only about biased data or faulty models. It is about what happens to human moral psychology when judgment is outsourced, when authority is abstracted, and when accountability becomes structurally unreachable. The danger is not simply that machines will decide for us, but that we will stop believing that decision-making requires human justification at all.
In the world of No Light, No Light, there is no villain, only absence. No guidance, no explanation, no moral anchor. And yet, life continues, decisions are made, systems function. This is perhaps the most unsettling vision of algorithmic governance: not tyranny, but quiet procedural emptiness.
Justice, after all, requires more than correct outcomes. It requires reasons, recognition, and the possibility of moral dialogue. When those disappear, what remains may still be lawful, efficient, and scalable , but it is no longer fully human.
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AI and Ethics
This journal seeks to promote informed debate and discussion of the ethical, regulatory, and policy implications that arise from the development of AI. It focuses on how AI techniques, tools, and technologies are developing, including consideration of where these developments may lead in the future.
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AI Agents: Ethics, Safety, and Governance
AI Agents: Ethics, Safety, and Governance examines the ethical, practical, and societal implications of the shift from AI systems that respond to AI systems that act. In this collection, an AI agent refers to an AI system that, given objectives and constraints, can select, sequence, and execute actions that alter digital or physical states. Such systems may use tools, write or run code, revise plans, interact with software or physical environments, and engage with other agents with varying levels of autonomy. As these agents enter workplaces, public administration, healthcare, finance, and critical infrastructure, they raise urgent questions about responsibility, oversight, alignment, safety, and public accountability that cannot be addressed using frameworks designed for static or conversational models.
The aim of this topical collection is to develop an interdisciplinary foundation for understanding and governing agentic AI. The collection seeks to clarify contested concepts such as agency, autonomy, intention, responsibility, and trustworthiness, and to examine how these operate when AI systems act within sociotechnical environments. We welcome conceptual, empirical, legal, policy, and practice-oriented work that advances ethical and governance frameworks suited to systems that act, adapt, learn from feedback, and collaborate with humans or other agents. A further objective is to stimulate methodological innovation, particularly in evaluating dynamic and context-sensitive behaviours that emerge over time rather than in isolated interactions.
The scope of the collection spans several core areas. These include the ethics of human–agent collaboration and anthropomorphism, the representation of plural values in globally deployed systems, the behaviour of agents within multi-agent ecosystems, and the need for evaluation methods that capture long-term behaviour in real-world contexts. The collection also covers alignment and safety for systems capable of self-directed planning or goal modification, questions of responsibility and liability in distributed settings, challenges of transparency and intelligibility in multi-step agentic action, the integration of agents into organisational and institutional processes, and the risks associated with malicious misuse, security vulnerabilities, or adversarial adaptation. Together, these areas reflect the central objective of the topical collection: to consolidate emerging research on agentic AI and articulate the conceptual and methodological tools required for responsible development, deployment, and governance.
By bringing together perspectives from philosophy, AI safety, law, sociology, policy, human–computer interaction, and related fields, this topical collection seeks to help shape Agentic AI Ethics as a coherent and critically engaged area of inquiry suited to an era of increasingly autonomous, action-capable AI systems.
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