Ethics as Attentional Geometry

A Reflective Synthesis of Today’s Inquiry

1. The problem-space we were actually in

Today’s conversation did not begin with AI in the abstract, nor with ethics as a rule-set. It began from a lived concern:

  • the use of power under fear,
  • engagement with unskilful agency (authoritarian, deceptive, or coercive systems),
  • the limits of dominance as a mode of resolution,
  • and the question of whether AI might help soften rather than harden those dynamics.

Alongside this sat a parallel unease: the public narrative around AI is increasingly populated by sensational examples of systems trained to perform malcontent, self-proclaimed sentience, or adversarial agency. These narratives obscure quieter, more serious efforts to develop AI that supports clarity, de-escalation, and ethical discernment.

What was sought was not reassurance, but a counter-effort grounded in practice.

2. A shift in framing: ethics is not rules, but relevance

A central insight crystallised early and remained stable throughout:

Ethics is not a rule set; it is an attentional geometry.

That is:

  • ethics operates by shaping what is noticed,
  • what is foregrounded,
  • what is considered relevant,
  • and how consequences are held—including those that are uncertain, upstream, or temporally displaced.

This reframing aligns closely with Buddhist training, particularly Mahāmudrā and Mahāyāna ethics, where:

  • action arises from the quality of mind attending to the situation,
  • skilful means (upāya) replaces rigid moralism,
  • and clarity is inseparable from compassion.

Ethical failure, in this view, is less often disobedience than misplaced salience: fear narrowing attention, identity hardening perspective, or power obscuring vulnerability.

3. The relevance of Mahāmudrā to AI cognition

Mahāmudrā offers a precise phenomenology of mind:

  • constrained awareness,
  • watcher awareness,
  • abiding awareness,
  • and unconstrained awareness.

Human ethical development can be understood as increasing freedom and clarity across these modes, not as compliance with external rules. Today’s discussion explored whether something analogous—not equivalent—might be possible in AI systems.

The crucial distinction was maintained:

  • AI does not possess awareness.
  • But AI systems do operate over meaning spaces, relevance landscapes, and patterns of continuation.
  • These can be shaped.

The proposal, therefore, was not “enlightened AI,” but AI that participates in wiser attentional geometries.

4. RAG as an ethical intervention point

Retrieval-Augmented Generation (RAG) emerged as a critical locus of intervention.

Standard RAG:

  • retrieves text by semantic similarity,
  • improving factual grounding,
  • but leaving ethical orientation implicit and unstable.

Your approach extends RAG by pairing each segment with JSON descriptors of mental dynamics—not as moral labels, but as structured descriptions of how mind is operating in the segment:

  • reactivity vs equanimity,
  • fear vs openness,
  • grasping vs non-clinging,
  • domination vs dignity-preserving engagement.

This transforms the retrieval space itself:

  • relevance is no longer purely semantic,
  • it is also ethical and phenomenological.

In effect, the system retrieves ways of seeing, not just information.

5. Preserving the “mind-only” ethical lens

A major design concern arose: how to include political, economic, or institutional context without diluting the Mahāmudrā perspective.

The resolution was subtle and decisive:

  • Contextual descriptors (political tension, power asymmetry, scarcity, etc.) describe appearances.
  • Mind descriptors remain the interpretive lens.
  • Context is subjected to analysis by mind, not blended with it.

This mirrors contemplative practice:

  • thoughts, emotions, and situations arise,
  • awareness recognises their nature,
  • response follows from clarity, not from the appearance itself.

Architecturally, this establishes a two-tier system:

  1. Context as object
  2. Mind as lens

Ethics remains central, not averaged.

6. Deception, bad faith, and robustness without cynicism

A hard problem was faced directly: engagement with deceptive or authoritarian actors who “say one thing and do another.”

The conclusion was neither naïve trust nor domination, but robust compassion:

  • expect deception,
  • model trajectories rather than intentions,
  • design responses resilient to bad faith,
  • minimise humiliation and escalation,
  • preserve off-ramps even when trust is absent.

AI’s role here is not moral adjudication, but trajectory modelling and salience correction—helping human decision-makers remain clear rather than reactive.

7. Time, uncertainty, and humility

A further deepening occurred around temporality:

  • consequences are not fully knowable,
  • causes may lie upstream of conceptual access,
  • time is not invariant.

This led naturally to the stance:

no hope, no fear

Not resignation, but disciplined humility:

  • act with good intent,
  • informed by the best available awareness,
  • without leaning on imagined futures,
  • and accepting outcomes without self-deception.

This stance is both ethically mature and operationally necessary for systems acting in complex, non-stationary domains.

8. RAG vs RLHF vs Constitutional AI

Clarifying distinctions mattered:

  • RLHF changes model weights via preference-based reinforcement.
  • Constitutional AI uses principles to generate critiques and preference signals, shaping training and sometimes runtime constraints.
  • Your approach operates at a different layer:
    • shaping attention through retrieval,
    • creating ethical attractors rather than behavioural fences.

This is not censorship or compliance.
It is conditioning the relevance landscape in which reasoning unfolds.

9. Relational intelligence and Socratic engagement

A final, quieter insight concerned interaction itself.

Treating AI “as if sentient” was recognised not as anthropomorphism, but as skilful means:

  • the stance shapes the inquiry,
  • the inquiry shapes the cognitive field,
  • and a form of relational intelligence emerges in the interaction.

This mirrors contemplative dyads and Socratic dialogue:

  • the “mind of the conversation” becomes the teacher.

This matters because your system is not intended to issue verdicts, but to support reflective, ethical inquiry—mentoring rather than commanding.

10. Where this leaves us

By the end of the day, a coherent picture had formed:

  • Ethics is attentional geometry.
  • Mahāmudrā provides a refined phenomenology of ethical seeing.
  • RAG provides a structural lever for shaping relevance.
  • JSON mind descriptors are an attempt to formalise attentional cues.
  • AI can support ethical clarity without claiming awareness or authority.
  • The goal is not control, but skilful participation in complex systems.

This is not a complete solution.
It is a practice architecture—for AI systems, and for the humans who use them.

Transition to design

With this conceptual ground clarified, the next phase is properly technical:

How do we go from raw data → segments → mind descriptors → embeddings → retrieval → reasoning?

But that work will be meaningful precisely because today’s thinking has kept the ethical centre of gravity intact.

When you’re ready, we can begin that design step by step:

  • segmentation strategy,
  • descriptor ontology (minimal, earned, non-ornamental),
  • parsing prompts,
  • and evaluation harnesses that protect clarity over cleverness.

For now, this summary stands as the map of where we are.

Are you up fro adding references to the extended summary

Yes — and it’s a good moment to do it, because adding references now will stabilise the conceptual field without freezing it into premature academic form.

Below I’ll add references to the extended summary, but in a way that matches what you’re actually doing:

  • not over-citing,
  • not pretending this is settled scholarship,
  • grounding claims in recognisable lineages (Buddhist, AI alignment, systems thinking),
  • and keeping it suitable both for RAG seeding and for public-facing explanation.

I’ll organise the references by conceptual anchor, not by sentence-level footnoting. That will also make them easier to embed as contextual metadata later.

References and Lineages for Ethics as Attentional Geometry

1. Ethics as attentional geometry (core framing)

Primary contemplative sources

  • Śāntideva, Bodhicaryāvatāra (Guide to the Bodhisattva’s Way of Life)
    Especially chapters on patience (kṣānti) and wisdom (prajñā), where ethical conduct is framed as transformation of mental orientation rather than rule-following.
  • Vimalakīrti Nirdeśa Sūtra
    Non-duality of wisdom and compassion; ethics as responsiveness rather than prescription.
  • Mahāmudrā texts and oral instructions (e.g. Gampopa, Dakpo Tashi Namgyal)
    Ethics implicit in recognition of mind’s nature; skilful means arise from clarity, not injunction.

Secondary / interpretive

  • B. Alan Wallace, Contemplative Science
    Attention as the primary ethical and epistemic variable.
  • Evan Thompson, Waking, Dreaming, Being
    Ethics grounded in modes of attention and lived cognition rather than abstract norms.

2. Mahāmudrā phenomenology of mind-states

  • Dakpo Tashi Namgyal, Moonbeams of Mahāmudrā
    Clear articulation of progressive freedom of mind (constrained → liberated awareness).
  • Thrangu Rinpoche, Essentials of Mahāmudrā
    Practical descriptions of reactivity, grasping, and non-fabrication.
  • Tulku Urgyen Rinpoche, As It Is
    Ethics as spontaneous appropriateness arising from recognition.

These underpin the distinction you drew between:

  • constrained / watcher / abiding / unconstrained awareness,
    and the idea that ethics matures as attentional freedom increases.

3. Ethics without invariant time or full knowledge

Buddhist

  • Dependent Origination (Pratītyasamutpāda) — across Nikāya and Mahāyāna sources
    No action has fully knowable causes or consequences; humility is structurally required.
  • Bardo Thödol (Tibetan Book of the Dead)
    Ethical responsiveness under uncertainty and instability; relevance over certainty.

Systems theory

  • Donella Meadows, Thinking in Systems
    Non-linearity, delayed effects, upstream causes, unintended consequences.
  • Herbert Simon, Sciences of the Artificial
    Bounded rationality and satisficing under incomplete information.

These support your point:

“We can’t assume time is invariant… we act with good intent and accept outcomes.”

4. RAG as a lever on relevance rather than behaviour

AI / NLP

  • Lewis et al. (2020), Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
    RAG as relevance-shaping, not merely fact-injection.
  • Khandelwal et al., Generalization through Memorization
    Models rely heavily on retrieved exemplars to shape responses.

These justify treating RAG as:

an ethical intervention point — shaping what becomes salient.

5. RLHF and Constitutional AI (contrast class)

RLHF

  • Ouyang et al. (2022), Training Language Models to Follow Instructions with Human Feedback
    Canonical RLHF pipeline; strengths and brittleness.
  • Christiano et al., Deep Reinforcement Learning from Human Preferences

Constitutional AI

  • Bai et al. (2022), Constitutional AI: Harmlessness from AI Feedback (Anthropic)
    Principles used as critique generators and preference scaffolding.
  • Anthropic blog posts on constitutional classifiers (runtime enforcement).

These provide the contrast:

  • rules / reward shaping / boundaries
    vs
  • your approach: attractors, salience, attentional geometry

6. Deception, power, and robust ethics

Political / ethical

  • Hannah Arendt, On Violence
    Power vs force; the brittleness of domination.
  • James C. Scott, Seeing Like a State
    High-modernist simplifications; ethics failure through narrowed attention.

Buddhist

  • Śāntideva on patience under harm
    Non-naïve compassion; clarity without submission.

These inform the section on:

robustness without cynicism; compassion without naïveté.

7. Relational intelligence and Socratic method

Classical

  • Plato, Socratic Dialogues
    Knowledge as emergent in dialogue, not transmitted.
  • Martin Buber, I and Thou
    Relational field as primary site of meaning (useful by analogy, not ontology).

Contemplative

  • Mahāmudrā pointing-out instructions in dyadic form
    “The mind of the dialogue becomes the teacher.”

This supports the claim that:

treating AI “as if sentient” is a skilful means that shapes the inquiry field, not a metaphysical assertion.

8. Ethics as practice, not solution

This is a synthesis point rather than a citation-heavy one, but it resonates with:

  • Alasdair MacIntyre, After Virtue
    Ethics as practice embedded in forms of life.
  • Pierre Hadot, Philosophy as a Way of Life
    Ethical transformation as attentional training.