VC-PROC Procedural / political

Artificial Intelligence, Values, and Alignment · Iason Gabriel · 2020

“the task in front of us is not, as we might first think, to identify the true or correct moral theory and then implement it in machines. Rather, it is to find a way of selecting appropriate principles that is compatible with the fact that we live in a diverse world, where people hold a variety of reasonable and contrasting beliefs about value.”

why coded: The paper's central proceduralist reframing · unit #14, pp. 424

“Their agreement therefore takes the form of an 'overlapping consensus' between different perspectives (Rawls 2001, 32). Thus, even without agreement about the fundamental nature of morality, people may still come to a principled agreement about values and standards that are appropriate for a given subject matter or domain.”

why coded: Core mechanism of the procedural solution · unit #17, pp. 425

“On the one hand, negative rights are widely endorsed but have limited scope. They rule out a certain class of actions but do not provide guidance in all situations [...] On the other hand, positive rights address this limitation, providing designers with a richer set of goals and aspirations, but command significantly less global support in practice.”

why coded: Negative/positive rights trade-off inside the procedural proposal · unit #18, pp. 426

“Jobin et al. (2019) observe that beneath the surface there continues to be 'substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issues, domains or actors they pertain to, and how they should be implemented' (389). [...] Mittlestadt (2019a, 5) notes that existing codes largely contain 'abstract and vague concepts [...] which are not specific enough to be action-guiding' [...] 'we must therefore hesitate to celebrate consensus around high-level principles that hide deep political and normative disagreement'.”

why coded: Placeholder-consensus worry: agreement may be nominal only · unit #19, pp. 427

“The problem of alignment is, in this sense, political not metaphysical. To address it, I recommended that we look more closely at principles that would be supported by a global overlapping consensus of opinion, chosen behind a veil of ignorance and/or affirmed through democratic processes.”

why coded: 'Political not metaphysical' - the thesis statement · unit #24, pp. 436

STELA: a community-centred approach to norm elicitation for AI alignment · Stevie Bergman; Nahema Marchal; John Mellor; Shak… · 2024

“Existing scholarship has mainly studied how to encode moral values into agents to guide their behaviour. Less attention has been given to the normative questions of whose values and norms AI systems should be aligned with, and how these choices should be made.”

why coded: The whose-values/how-chosen question moved from theory to method · unit #1, pp. 1

“it is important to acknowledge that the STELA process itself is not fully deliberative, in that study participants were not tasked to arrive at a consensus regarding the chatbot's ideal conduct. Neither is the process fully participatory [...] This work is further limited by its US-centrism.”

why coded: Honest limits: not fully deliberative/participatory; US-centric · unit #10, pp. 10

Disagreement, AI alignment, and bargaining · Harry R. Lloyd · 2024

“another arguably important requirement for fairness here is to ensure that these disadvantaged groups have a fair say in determining the criteria under which the effects of AI systems should be judged as 'fair' or 'unfair.' [...] fair AIs should optimise for objectives that at least partially reflect the values of all of the communities who those AIs will affect.”

why coded: Fair-say requirement: affected groups co-author the fairness criteria themselves · unit #1, pp. 1760

“Jackson: some moral parliament (unsimulated or simulated) faces a choice between three options, A, B, and C. 51% of parliamentarians think that A is the best, B is almost as good, but C is terrible. 49% of parliamentarians think that C is the best, B is almost as good, but A is terrible. [...] a plurality- or majority-rule voting system will select option A. Yet many of us intuit, to the contrary, that it would be better for our alignment approach to select option B [...] plurality- and majority-rule voting can fail to select an attractive compromise option.”

why coded: Tyranny of the majority defeats parliamentary/voting proceduralism on compromise options · unit #4, pp. 1762

“[Biorisk:] under plurality- or majority-rule voting, how much compute the biomedical AI should spend on safety testing depends discontinuously upon how many stakeholders endorse the less as opposed to the more permissive view [...] the parliament's verdict is extremely sensitive to small differences in stakeholder opinion, such as the difference between 49 and 51% endorsement. Moreover, this hypersensitivity strikes me as entirely unnecessary.”

why coded: Hypersensitivity: majority-rule verdicts discontinuous in stakeholder opinion · unit #5, pp. 1763

Beyond Preferences in AI Alignment · Tan Zhi-Xuan; Micah Carroll; Matija Franklin; Hal… · 2024

“Recognizing these issues, Gabriel (2020) argues for an explicitly moral conception of alignment [...] However, it is far from clear how to operationalize these abstract principles. To make progress, we suggest a conception of single-principal alignment that is significantly more constrained: When an AI system only serves an individual in performing a particular task or role, it should be aligned with the normative ideals or criteria that are appropriate for that role. [...] For general-purpose AI assistants, this implies alignment with the normative ideal of an assistant.”

why coded: Explicit critique: Gabriel's moral conception is not operationalizable as stated · unit #10, pp. 1839

A matter of principle? AI alignment as the fair treatment of claims · Iason Gabriel; Geoff Keeling · 2025

“both approaches also suffer from limitations. First, they are incomplete. Intent-alignment does not specify whose intentions AI systems should be aligned with and HHH offers no principled mechanism for resolving trade-offs in cases where the three properties conflict. [...] Third, both approaches lack the right kind of justification given an understanding of the wider social context within which disputes about AI alignment take place.”

why coded: The two reigning paradigms fail completeness + justification tests · unit #1, pp. 1952

“The central idea is that when a technology has profound societal effects it ought to be regulated by principles that are amenable to public rather than private justification. [...] To arrive at a solution to the problem of normative alignment that is acceptable to different stakeholders, we need to ground our understanding of AI alignment in principles or ideals that treat competing claims fairly and that can be justified to all reasonable parties.”

why coded: The positive thesis: public justification via fair treatment of claims · unit #6, pp. 1958

“Stakeholders should be able to present concerns in their own voice and in a relatively un-mediated manner, so long as they are directed at a general audience. This will often involve making claims directly about rights, interests, fairness or well-being in a language with which they are already familiar. [...] the notion of fair process needs to be scoped dynamically on a case-by-case basis [...] leading to what Archon Fung refers to as 'overlapping circles of inclusion'.”

why coded: Claims in own voice + dynamically scoped circles of inclusion · unit #9, pp. 1961

“public deliberation aims to generate more granular principles for AI that are tailored to its particular characteristics or deployment scenario, and that are also – to varying degrees – actively affirmed. [...] Efforts to bridge this gap between theory and practice, often reveal a justificatory gap which we believe is better addressed through actual consultation with affected people.”

why coded: Alignment assemblies + actual consultation over idealized theory · unit #15, pp. 1968

Moral disagreement and the limits of AI value alignment: a dual challenge of epistemic ju… · Nick Schuster; Daniel Kilov · 2025

“we argue, all three ultimately fail to accommodate reasonable moral disagreement. Despite appearances, the outputs of AI systems aligned via these approaches are neither epistemically justified nor politically legitimate, and so those who reasonably disagree with them lack good reason to accept them.”

why coded: Headline result: all three procedural alignment methods fail both tests · unit #1, pp. 6073

“Anthropic (2023) researchers acknowledge that this raises a challenge for the general acceptability of their system's outputs: 'While Constitutional AI is useful for making the normative values of our AI systems more transparent, it also highlights the outsized role we as developers play in selecting these values—after all, we wrote the constitution ourselves.' [...] In general, we found a high degree of consensus on most statements, though Polis did identify two separate opinion groups.”

why coded: Collective Constitutional AI as the best real procedural attempt - still two irreconcilable opinion groups · unit #14, pp. 6083

“even if the initial principle selection and specification processes are done through deliberative democratic procedures, the crucial further process of transforming principles into decision-making algorithms is not sufficiently similar to standard democratic procedures to legitimize the system's outputs. And so, even constitutional AI fails on political grounds.”

why coded: The encoding gap: democratic legitimacy does not survive the principles-to-algorithm transformation · unit #15, pp. 6083

Disentangling AI Alignment: A Structured Taxonomy Beyond Safety and Ethics · Kevin Baum · 2026

“[The proceduralist practical syllogism:] (P1) Moral pluralism (at least when understood descriptively) is a fact. (P2) There is deep moral uncertainty and persistent moral disagreement. (P3) Imposing values should be avoided. (P4) If P1-P3 and we must nevertheless align AIAs, we should aim for alignment relative to publicly justifiable norms rather than moral alignment. (P5) We must align AIAs. (P6) Publicly justifiable norms can (only? best?) be achieved through a fair and public process [...] (C) We should aim for alignment relative to norms that are the result of such a fair and public process.”

why coded: The proceduralist inference reconstructed as explicit syllogism P1-P6 - the argument the dissertation contests, now attackable premise by premise · unit #4, pp. 166

“It remains somewhat unclear whether this approach ultimately aims to approximate moral ground truth via public procedures, or rather represents a shift in normative aims—from moral alignment to public justifiability understood as an independent aim.”

why coded: Baum's own diagnosis: proceduralism is ambiguous between truth-approximation and aim-replacement · unit #5, pp. 166

Beyond Preference-based Value-alignment (IEAI Research Brief Q2 2026) · Julia Li · 2026

“participatory forms of alignment carry their own distributive risks, as the onus falls on societies and groups of individuals to produce and communicate their values. [...] Participatory and deliberative value alignment methods could exacerbate existing societal inequalities between those with the capacity to participate and those without.”

why coded: Distributive-justice critique of participatory/procedural alignment - Gabriel's own solution has an equity problem · unit #10, pp. 6

Justifications for Democratizing AI Alignment and Their Prospects · Andre Steingrüber; Kevin Baum · 2026

“Schuster and Kilov argue that current proposals for democratic approaches all invoke procedures that fail to be democratic. However, we believe this is based on a misunderstanding. [...] these three techniques should not be understood as solutions to the normative problem, but rather as solutions to the technical problem.”

why coded: Defends democratic approaches against S&K by relocating crowdsourcing/RLHF/CAI to the technical problem · unit #1, pp. 147

“neither purely epistocratic nor purely democratic approaches may be sufficient on their own, pointing toward hybrid frameworks that combine expert judgment with participatory input alongside institutional safeguards against AI monopolization.”

why coded: Hybrid expert+participatory verdict with institutional safeguards - the landing point · unit #9, pp. 158