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arxiv: 2604.23635 · v1 · submitted 2026-04-26 · 💻 cs.HC · cs.AI· cs.IR· cs.LG

From Rights to Rites: Expectations Management in Smart-Home AI

Varad Vishwarupe , Ivan Flechais , Marina Jirotka , Nigel Shadbolt This is my paper

Pith reviewed 2026-05-08 05:50 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.IRcs.LG
keywords expectations managementsmart-home AIethical designhuman-computer interactionvoice assistantsdesign tensionsculturally embedded practicesresponsible AI
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The pith

Smart-home AI practitioners shape user expectations by balancing organisational rights with culturally situated rites.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops Expectations Management as a model for how designers, developers, and researchers at major platforms construct and handle expectations around domestic AI devices. Drawing on interviews, it argues this process is culturally embedded and centers moral judgement and situated action rather than purely technical fixes. The authors distil four recurring design tensions into a five-phase playbook meant to support ethical choices in product development. A sympathetic reader would care because the work turns abstract ethics into concrete guidance for devices already in people's homes and routines.

Core claim

Using a constructivist grounded theory approach on 33 interviews with practitioners from Amazon Alexa, Microsoft Azure IoT, and Google Nest, the authors develop Expectations Management (EM): a culturally embedded model describing how practitioners shape, calibrate, and repair expectations by balancing organisational rights with culturally situated rites. EM differs from expectation-confirmation theory and trust-calibration by foregrounding moral judgement, situated action, and cross-cultural variation, yielding a five-phase EM Design Playbook that addresses four design tensions and supports moral prudence in responsible smart-home design.

What carries the argument

Expectations Management (EM), a culturally embedded model in which practitioners balance organisational rights with culturally situated rites to shape, calibrate, and repair user expectations.

If this is right

  • EM supplies a five-phase Design Playbook that practitioners can use to navigate moral prudence in smart-home AI.
  • The model foregrounds four recurring tensions—automation versus autonomy, helpfulness versus intrusiveness, personalisation versus predictability, and transparency versus obscurity—that must be actively managed.
  • EM differs from prior theories by treating moral judgement and cross-cultural variation as central rather than secondary.
  • The approach offers concrete guidance for responsible smart-home design and human-centred AI development.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Teams building voice or home AI in other cultural contexts could adapt the rights-rites balance to reduce mismatches between company goals and user realities.
  • Applying the EM playbook in pilot projects across different regions would test whether cultural variation is as load-bearing as claimed.
  • Organisations might embed the five-phase process into product roadmaps so ethical considerations shape features earlier rather than after launch.

Load-bearing premise

The assumption that practices observed among practitioners at three specific major platforms can be generalised into a broadly applicable model that meaningfully differs from existing theories of expectation confirmation and trust calibration.

What would settle it

Observations or further interviews showing that smart-home AI practitioners do not balance rights against cultural rites when managing expectations, or that the five-phase playbook produces no measurable improvement in addressing the identified design tensions.

Figures

Figures reproduced from arXiv: 2604.23635 by Ivan Flechais, Marina Jirotka, Nigel Shadbolt, Varad Vishwarupe.

Figure 1
Figure 1. Figure 1: The Expectations Management lifecycle—shaping, calibrating, and repairing—with feedback loops across stages. 4.2 Shaping Expectations Shaping is the pre-deployment phase where practitioners establish the ethical and affective baseline of a system. Participants described three key inputs. Cultural and relational values. Norms of care, politeness, and shared privacy inform what users consider respectful beha… view at source ↗
Figure 2
Figure 2. Figure 2: Shaping expectations: inputs from cultural, organisational, and professional values feed user expectations, which yield met outcomes (satisfaction, adoption) or unmet outcomes (astonishment, distrust) that, in turn, surface design tensions. 4.3 Calibrating Expectations Calibration is the ongoing work of keeping behaviour proportionate as contexts shift. Unlike shaping, calibration is driven by field eviden… view at source ↗
Figure 3
Figure 3. Figure 3: Calibrating expectations: continuous adjustment based on field signals and practitioner judgement, organised around sensing, interpretation, and proportional response. 4.4 Repairing Expectations Repair occurs when expectations are violated and legitimacy is disrupted. Participants consistently distinguished technical correction (functional faults) from ethical restoration (acknowledging social meaning and … view at source ↗
Figure 4
Figure 4. Figure 4: Repairing expectations: technical correction and ethical restoration as complementary work, organised around accountability, empathy, and continuity. 4.5 Design Tensions Across shaping, calibration, and repair, four recurring tensions structured Expectations Management. These tensions are not problems to be resolved once; they are conditions to be managed through situated judgement view at source ↗
Figure 5
Figure 5. Figure 5: Design tensions that structure Expectations Management across lifecycle stages. 5 Discussion 5.1 Beyond Expectation-Confirmation and Trust Calibration Expectations Management intersects with expectation-confirmation and trust calibration in recognising that expectations and trust shape adoption, reliance, and satisfaction [3, 4, 6]. The difference lies in analytic emphasis. ECT treats expectations primaril… view at source ↗
Figure 6
Figure 6. Figure 6: The EM Design Playbook: five reflective phases derived from recurring practitioner strategies. 6 Scope, Limitations, and Future Directions This study examines expectations management through interviews with practitioners in large smart-home platforms with mature policy, safety, and compliance regimes; findings may not transfer directly to smaller firms or informal deployments where such structures are weak… view at source ↗
read the original abstract

Domestic voice assistants and smart-home devices are increasingly embedded in everyday routines, yet their ethics are often treated as an afterthought or delegated to compliance teams. To explore how expectations about smart-home AI are constructed and managed, we conducted 33 semi-structured interviews with designers, developers, and researchers from major smart-home platforms (Amazon Alexa, Microsoft Azure IoT, and Google Nest). Using a constructivist grounded theory approach, we develop Expectations Management (EM): a culturally embedded model describing how practitioners shape, calibrate, and repair expectations by balancing organisational rights with culturally situated rites. We show that EM differs from expectation-confirmation theory and trust-calibration by foregrounding moral judgement, situated action, and cross-cultural variation. Our analysis reveals four recurring design tensions: automation vs. autonomy, helpfulness vs. intrusiveness, personalisation vs. predictability, and transparency vs. obscurity and distils them into a five-phase EM Design Playbook that supports moral prudence. We discuss implications for responsible smart-home design and offer guidance for human-centred AI.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper reports a constructivist grounded theory analysis of 33 semi-structured interviews with designers, developers, and researchers at Amazon Alexa, Microsoft Azure IoT, and Google Nest. It develops an Expectations Management (EM) model describing how practitioners shape, calibrate, and repair user expectations in smart-home AI by balancing organisational rights with culturally situated rites. The analysis identifies four design tensions (automation vs. autonomy, helpfulness vs. intrusiveness, personalisation vs. predictability, transparency vs. obscurity) and distils them into a five-phase EM Design Playbook for moral prudence. The model is positioned as distinct from expectation-confirmation theory and trust-calibration through its emphasis on moral judgement, situated action, and cross-cultural variation, with implications for responsible smart-home design.

Significance. If the EM model is supported by the interview data and can be appropriately scoped, the work provides a practitioner-grounded framework that integrates ethical and cultural considerations into expectation management for smart-home AI. This offers concrete design guidance via the identified tensions and playbook, extending beyond purely technical or psychological accounts and contributing to human-centred AI research in HCI.

major comments (2)
  1. [Abstract] Abstract and Discussion: The claim that EM is a 'culturally embedded' model foregrounding 'cross-cultural variation' rests on interviews with practitioners from only three US-based platforms (Amazon, Microsoft, Google). This sample homogeneity risks the four design tensions and five-phase playbook reflecting organisational norms specific to these Western tech giants rather than broadly applicable rites, weakening the generalizability and distinction from existing theories.
  2. [Methods] Methods: The constructivist grounded theory approach is suitable, but without explicit evidence of theoretical saturation, coding examples, or data excerpts supporting the derivation of the EM model and playbook, it is difficult to assess the rigor and load-bearing support for the central claims about moral judgement and situated action.
minor comments (1)
  1. [Abstract] The abstract introduces the five-phase EM Design Playbook but does not preview the phases themselves; including a brief enumeration in the abstract or a dedicated table in the findings would improve clarity and reader orientation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive feedback on our manuscript. We address each major comment below with honest responses and indicate where revisions will be made to strengthen the work.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Discussion: The claim that EM is a 'culturally embedded' model foregrounding 'cross-cultural variation' rests on interviews with practitioners from only three US-based platforms (Amazon, Microsoft, Google). This sample homogeneity risks the four design tensions and five-phase playbook reflecting organisational norms specific to these Western tech giants rather than broadly applicable rites, weakening the generalizability and distinction from existing theories.

    Authors: We acknowledge the limitation in our participant sample, which consists of practitioners from three US-headquartered companies. This does constrain direct claims of broad cross-cultural variation, as the data primarily reflects perspectives from these organisational contexts rather than comparative data across non-Western settings. However, the practitioners described adapting to diverse user expectations in global deployments, and the EM model's emphasis on culturally situated rites emerges from their accounts of moral judgement in context-specific scenarios. The distinction from expectation-confirmation theory and trust-calibration is primarily grounded in the foregrounding of moral judgement and situated action, which are supported by the data. We will revise the abstract and discussion to qualify these claims, explicitly noting the sample scope and organisational influences, while preserving the core contributions. This addresses the concern without overstating generalizability. revision: partial

  2. Referee: [Methods] Methods: The constructivist grounded theory approach is suitable, but without explicit evidence of theoretical saturation, coding examples, or data excerpts supporting the derivation of the EM model and playbook, it is difficult to assess the rigor and load-bearing support for the central claims about moral judgement and situated action.

    Authors: We agree that greater methodological transparency is needed to demonstrate rigor. In the revised manuscript, we will expand the Methods section to report how theoretical saturation was assessed (no new categories emerged after the 28th interview, with the final five confirming stability), provide brief examples of the iterative coding process (e.g., open codes on expectation repair leading to the five-phase structure), and include 3-4 anonymized data excerpts illustrating how the design tensions and playbook were derived from participant descriptions of moral judgement and situated action. These additions will directly support the central claims without changing the findings. revision: yes

Circularity Check

0 steps flagged

No circularity: inductive model-building from interview data

full rationale

The paper applies constructivist grounded theory to 33 semi-structured interviews to inductively derive the Expectations Management (EM) model, its four design tensions, and five-phase playbook. No derivation step reduces by construction to fitted parameters, self-definitional loops, or load-bearing self-citations; the model is presented as an outcome of open coding and constant comparison rather than presupposed. Distinctions from expectation-confirmation theory arise from the data analysis itself, not from renaming or smuggling prior results. The chain remains self-contained as empirical theory-building without mathematical or definitional equivalence to inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the validity of the grounded theory analysis of interview data and the assumption that the resulting model captures general patterns in smart-home AI practice.

axioms (1)
  • domain assumption Constructivist grounded theory is a valid approach for deriving a general model from practitioner interviews.
    Invoked to justify developing the EM model from the 33 interviews.
invented entities (1)
  • Expectations Management (EM) model no independent evidence
    purpose: To describe and guide how practitioners manage expectations in smart-home AI.
    Newly developed framework presented as the main contribution.

pith-pipeline@v0.9.0 · 5494 in / 1268 out tokens · 37711 ms · 2026-05-08T05:50:19.312228+00:00 · methodology

discussion (0)

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Reference graph

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