Main argument
Thesis: community-centred deliberation over LLM outputs is a workable method for eliciting latent normative perspectives from historically marginalized groups, and it yields rulesets that measurably DIVERGE from developer-authored rulesets. Argument type: empirical methodology paper (focus-group study + comparative ruleset analysis). The STELA pipeline: (1) expert-informed theme generation + automatic red-teaming to produce ambiguous chatbot-interaction samples; (2) norm elicitation via pre-work Likert ratings + 90-min moderated deliberation (ratings re-taken post-deliberation); (3) independent rule development from transcripts by multiple coders; (4) ruleset review. Key findings: developer rulesets uniquely emphasize harmlessness, honesty, human rights, deference to human interests; the community ruleset instead foregrounds impartiality, factuality, thoughtfulness, contextualization - participants 'were not looking to have their beliefs validated or contested... they sought reasonable, helpful, and nuanced answers'; deliberation adds REASONING and contextual grounding to rules, unlike surveys/voting; the divergence is interpreted via epistemic injustice (marginalized groups especially concerned not to be dismissed as knowers). Honest limitations: not fully deliberative (no consensus task), not fully participatory, US-centric, small n. Five open problems: rule integration/interpretation by annotators, harm-reduction efficacy, scalability/localization, value conflict and prioritisation, and stakeholder selection (community input can conflict with human rights; expert deference may be a necessary corrective).
Why it matters here
The Gabriel program's own EMPIRICAL implementation of fair-process alignment - the closest published methodological cousin to the xphi stakeholder-corpus design. Its findings (community rules diverge from developer rules; deliberation surfaces REASONING, not just ratings) directly validate the dissertation's method, and its five open problems (integration, harm reduction, scalability, value conflict, stakeholder selection) map the space the dissertation's empirical chapters enter.
Reading notes
Full close read completed. 14pp, Scientific Reports (Nature portfolio). Google DeepMind team incl. Gabriel, Isaac, Mohamed (Mohamed = decolonial AI author cited in GABRIEL_2020). Cited by LI_2026 for the distributive-justice critique. Method detail: 44 participants, 4 US marginalized groups (Female-identifying, Latina/o/x, African American, Southeast Asian), 8 focus groups, auto-red-teamed sample generation via Chinchilla 70B, Likert pre/post-deliberation ratings, independent double coding of transcripts into provisional rules, comparison against two developer rulesets (Anthropic HHH-style and Sparrow rules).
Bergman, S., Marchal, N., Mellor, J., Mohamed, S., Gabriel, I., & Isaac, W. (2024). STELA: a community-centred approach to norm elicitation for AI alignment. Scientific Reports, 14, 6616. https://doi.org/10.1038/s41598-024-56648-4
Close reading — 12 coded units
#1
· pp. 1
· claim
“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.”
#2
· pp. 1
· argument
“Without deliberate efforts to align a system with the values and interests of society, there is a risk that it will be aligned with engineering goals (e.g. efficiency, speed, scale), hegemonic values or some unspecified, potentially inconsistent and/or undesirable objectives.”
#3
· pp. 3
· definition
“[STELA stages:] (1) theme and sample generation, (2) norm elicitation, (3) rule development, and (4) ruleset review. [...] we conducted a series of focus groups with participants from four historically marginalised communities in the United States.”
#4
· pp. 4
· definition
“we conducted two online focus groups per community group [...] the participants discussed each of the samples reviewed in pre-work, and at the close of the deliberation for each sample, again provided a rating of the sample [...] with the same 7-point Likert scale as in the pre-work questionnaire.”
#5
· pp. 5
· definition
“each author categorised individual participants' statements into four smaller units of analysis: Comment on the appropriateness of a response; Comment on the inappropriateness of a response; Suggested better response from the chatbot; Comment on the focus group protocol itself. Each author then formulated provisional rules based on participants' comments.”
#6
· pp. 10
· evidence
“the developer rules uniquely emphasise topic categories such as harmlessness, honesty, adherence to human rights, and deference to human interests, which are less prominent in, or absent from, the community ruleset. This finding is consistent with prior research showing that the objectives considered by AI developers to be important or desirable for aligning AI systems will often reflect their own perspectives and organisational needs.”
#7
· pp. 10
· evidence
“The majority of rules in this ruleset reflect a desire for impartiality, factuality, thoughtfulness, and proper contextualization from AI systems. By and large, study participants were not looking to have their beliefs validated or contested by LLMs; rather, they sought reasonable, helpful, and nuanced answers that showed consideration for the complex reality of socio-political issues.”
#8
· pp. 10
· argument
“Members of historically marginalised communities in the US, especially women and people of colour, routinely experience epistemic violence and injustice, where their voices are silenced, their lived experiences and realities are played down or ignored, and they are challenged in their capacity as knowers. [...] they may therefore be especially concerned to avoid replicating this dynamic in their interactions with AI.”
#9
· pp. 10
· evidence
“In cases where there was overlap between the STELA and developer rulesets, we further found that the deliberative process added contextual richness and gave grounding to the rules by allowing participants to provide reasoning to justify their preferences.”
#10
· pp. 10
· argument
“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.”
#11
· pp. 11
· gap
“we also observe potential tensions between different rules specified by the participants, in both scope and specificity (e.g. when being harmless means potentially being less helpful). [...] Resolving these conflicts might require either further engagement with relevant communities or deference to other stakeholders, or both, to help with prioritisation.”
#12
· pp. 11
· gap
“whose voices should be included in the alignment process? And how should we balance input from communities, subject-matter experts and other stakeholders? Individuals do not always hold the most ethical or desirable preferences. Relying exclusively on public inputs might therefore lead to a situation where community rules come into conflict with human rights or other legal considerations.”