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An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This empirical study claims that the binding constraint on responsible AI software is not a lack of ethical principles but their operationalization in day-to-day engineering decisions.

desk verdict A decent exploratory study on responsible AI decision-making with honest limitations, but the headline gap claim is an interpretive synthesis rather than a directly measured result. read the letter →

arxiv 2501.15691 v3 pith:3WTHLHYI submitted 2025-01-26 cs.SE

Lekshmi Murali Rani , Faezeh Mohammadi , Robert Feldt , Richard Berntsson Svensson This is my paper · ORCID
classification cs.SE
keywords ResponsibleAIsoftwareengineeringEthicaldecision-makingBehavioralTransparencyAccountabilityOperationalguidelinesHumanfactors
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that the main obstacle to responsible software engineering for AI is a failure to turn ethical principles into operational decisions inside the software life cycle. Drawing on seven interviews, 51 survey responses, and validation interviews with four industry experts, it argues that practitioners feel personal responsibility and report awareness of AI's societal impact, yet organizations lack concrete frameworks, checklists, and resources to apply those values at the project level. The authors propose that interdisciplinary collaboration, dual ethical-technical competence ("H-shaped" professionals), and a management-supported culture of ethics are what would close the gap. A sympathetic reader would care because the claim redirects attention from writing ethics codes to designing decision-support mechanisms for engineers.

What carries the argument

The study's central object is the decision-making process in responsible AI engineering, examined through mixed methods: semi-structured interviews analyzed by thematic and narrative analysis, a Likert-scale and open-ended survey, and static validation of findings with industry experts. The mechanism that carries the argument is the comparison between responsible AI development and conventional software development across organizational, team, and individual levels, with data as a new decision-driving element and probabilistic outcomes requiring continuous monitoring. Named constructs include H-shaped (also called π-shaped) competency, defined as professional depth in two distinct domains, one technical and one ethical, and the "principles-to-practice gap," the observed disconnect between stated ethical guidelines and operational decisions.

What would settle it

A concrete check is to audit a random sample of AI project artifacts—requirements documents, design records, test plans, and issue trackers—for documented ethical trade-off decisions, such as a fairness-accuracy choice or a privacy-utility trade-off. If such decisions are routinely recorded in specific, scenario-level terms across many organizations, the paper's central claim that guidelines are not operationalized would be contradicted; if such records are rare or vague, the claim is supported.

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Extended reading notes

Core claim

The central discovery is a gap between the state of the art and the state of practice in responsible AI engineering: ethical issues in AI development largely mirror those in conventional software development, but they are more pronounced and are not operationalized. Practitioners' decisions are shaped by personal values, emerging specialized roles such as data engineers, machine learning engineers, and AI ethicists, and by organizational culture, but current ethical guidelines are too abstract to guide concrete choices about data, transparency, accountability, and post-deployment monitoring. The paper concludes that H-shaped competencies (deep skill in one technical domain plus deep skill in ethics), interdisciplinary collaboration, and an organizational culture of ethics are critical enablers, with transparency and accountability as the values practitioners weight most heavily.

Load-bearing premise

The study assumes that seven interviews, 51 self-selected survey responses, and four expert validation interviews, recruited through convenience and snowball sampling, fairly represent AI engineering practice, and that participants' descriptions of their decisions match what they actually do.

Editorial extensions

If this is right

  • If the gap claim is right, adding scenario-driven ethical checklists and review boards to the software life cycle would do more than issuing additional principle statements.
  • If H-shaped competency matters, hiring and training should reward dual technical-ethical depth rather than treating ethics as a separate staff function.
  • If organizational culture is decisive, smaller companies may embed ethics more easily, while larger companies need explicit frameworks to keep ethics from becoming bureaucratized.
  • If data is the key decision determinant in AI, the requirements and design phases deserve ethics review earlier than in conventional software development.
  • If post-deployment monitoring depends on resources, responsible-AI guidance must include cost and capacity conditions, not just obligations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct extension is a testable check: count whether project artifacts such as requirements documents, design records, and test plans record explicit ethical trade-off decisions; the paper's evidence is self-reported, so artifact-level data would independently verify the gap.
  • The H-shaped competency recommendation implies an educational pipeline change: software engineering curricula would need ethics modules deep enough to count as a second domain, not merely an elective.
  • The study's emphasis on transparency and accountability suggests that future regulation may target decision documentation duties rather than model behavior alone.
  • Because the sample skews toward professionals already engaged with AI, the gap may be even larger in organizations without self-selected interest in responsible AI; a random industry sample could test that.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper reports an exploratory, interpretivist mixed-methods study of decision-making in responsible software engineering for AI (RSE for AI). The empirical basis is seven semi-structured interviews with practitioners, a survey of 51 respondents (Likert and open-ended questions), and four expert interviews used for static validation of findings. The authors use thematic and narrative analysis to derive themes under three research questions concerning the differences between responsible AI development and conventional software development, the influence of emerging roles, and the role of individual motivations and values. On this basis they claim a gap between the state of the art and industrial practice in RSE for AI, particularly a failure to operationalize ethical decision-making in the software engineering lifecycle, and they recommend interdisciplinary collaboration, H-shaped ethical-technical competences, and a stronger organizational ethics culture.

Significance. The paper addresses a timely and relevant topic and provides a useful descriptive account of how practitioners perceive ethical decision-making in AI development. Its main strengths are the mixed-method design, the explicit interpretivist stance, the use of both thematic and narrative analysis, and the public supplementary material. The reported quotes and tables give the reader a concrete view of participant responses. However, the contribution is primarily exploratory and hypothesis-generating; the sample is small and self-selected, the Likert items are not validated and are analyzed only descriptively, and the static validation is a member check rather than independent evidence. If the claims are reframed as participants' perceived experiences and recommendations, the paper can be a useful contribution to the empirical SE-for-AI literature; in its current form the abstract and conclusions state the central gap claim more strongly than the data support.

major comments (4)
  1. [Section IV-C and abstract/conclusion] The headline claim that 'current ethical guidelines are insufficiently implemented at the operational level' is not directly operationalized by the survey instrument. Q3 (organizational preparedness) and Q7 (effectiveness of methodologies) ask about perceived readiness and effectiveness, not about whether respondents actually apply ethical guidelines in concrete engineering tasks; the mildly positive Q3 and neutral Q7 distributions therefore do not measure the presence or absence of operationalization. The narrative themes in Sections IV-B2 and IV-B3 provide evidence that participants call for more structured support and describe implementation challenges, but these are perceived gaps, not a measured baseline of practice against a defined state of the art. I recommend rewording the abstract and Section VII to say that practitioners in this sample perceive a lack of operational frameworks and describe ethics as difficult to implement, rather than asserting as an empirical finding that guidelines are insufficiently implemented.
  2. [Section III-B and Section V] The research questions use causal language ('influence', 'impact') and the discussion repeatedly states that personal values 'can critically influence' decisions and that roles 'impact' decision-making, but the study design is cross-sectional self-report (interviews and a one-time survey). Such a design cannot establish causal influence or impact; it can only document participants' beliefs, attributions, and self-reported experiences. The empirical statements should be cast in terms of perceived influence or reported attribution, and the causal claims should be reserved for future longitudinal or intervention studies.
  3. [Section IV-D and Section VI] The static validation with four experts recruited from the authors' professional network is presented as evidence that the findings are 'confirmed' and applicable in industry, but it is a member check over the same findings, not independent corroboration. The paper itself concedes in Section VI that the validation interviews 'have the potential to introduce bias due to the possibility of leading questions'; given that the findings were presented to the experts before discussing them, this is a significant limitation. The conclusions should either present the validation as an expert credibility check with its own verbatim evidence, or drop the suggestion that four experts independently confirmed the findings.
  4. [Section III-C and Table I] The claim that data saturation was attained by the seventh interview is asserted without supporting detail (e.g., how saturation was assessed, which codes/themes became redundant), and the seven participants are heterogeneous in roles, company sizes, and experience; with convenience sampling from the authors' network, this cannot support generalizations about industry-wide practice. The 51 survey responses are also self-selected and no response rate is reported. While Section VI acknowledges limited generalizability, this limitation is in tension with the broad wording of the key finding in the abstract, so the conclusion should consistently restrict its claims to the sample and context.
minor comments (5)
  1. [Section I, first paragraph] The phrase 'in software engineering (software engineering) practices' appears to contain a duplicated parenthetical; the intended meaning should be clarified.
  2. [Table II] The column header 'Years in current role' appears twice and the current-company-size values are mixed with role tenure; the table should be cleaned so that each column has a unique header and consistent units.
  3. [Figures 2-4] The diverging stacked bar charts are described in the text but the axis labels and exact item wording are only in captions; for a journal version, ensure the charts carry numeric labels or a table of frequencies so that the distributions are independently inspectable.
  4. [Throughout] The terms 'responsible software engineering for AI', 'responsible AI engineering', and 'RAI' are used interchangeably; define the acronym once and use it consistently throughout the manuscript.
  5. [Section IV-C, Figures 2 and 4] Q2 under RQ1 and Q3 under RQ3 both ask about conflicting personal values, but they receive different wordings; clarify whether these are intended to be the same construct repeated for different units of analysis or two distinct items.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's conclusions are interpretive syntheses of primary interview and survey data, not derived quantities, and its self-citations are contextual rather than load-bearing.

full rationale

This paper makes no formal derivation or prediction; it reports an interpretivist mixed-method empirical study. The central finding of a principles-to-practice gap is an inductive thematic and narrative synthesis from seven interviews, 51 survey responses, and four expert validation interviews. No quantity is fitted to data and then renamed as a prediction, and no equation equates an output with an input by construction. The survey does not directly measure operationalization, and the paper's self-acknowledged limitations in Section VI (social desirability bias, possible leading questions, small convenience sample, reliance on personal interpretation) are validity or generalizability concerns, not circularity. Self-citations such as the Behavioral Software Engineering framing in [9] and [22] and the supplementary material in [30] provide context and data access, but the empirical conclusions stand on primary qualitative and survey data rather than on those citations. The static validation with four industry experts is an external credibility check, not a derivation from the study's inputs. Therefore no circular step is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No fitted parameters, equations, or hand-tuned constants appear; this is a qualitative empirical study. The paper introduces no new conceptual entities; H-shaped competency is adopted from reference [41], and ethical review boards are organizational structures, not new entities.

assumptions (4)
  • domain assumption Seven interviews reached data saturation and therefore suffice to characterize the phenomenon.
    Section III.C states saturation was attained by the seventh participant; saturation is a judgment call rather than a formal guarantee of coverage.
  • domain assumption Participants' self-reported accounts correspond to real decision-making processes in their organizations.
    The study relies on interviews and surveys; Section VI acknowledges social desirability bias and attempts to mitigate it with anonymity and triangulation.
  • domain assumption Interpretivist thematic and narrative analyses produce themes that are reliable across researchers and contexts.
    Section III.C describes coding by two researchers and conflict resolution, but theme extraction remains interpretative and context-bound.
  • domain assumption Convenience and snowball samples from the authors' network and online forums can support generalizable practice recommendations.
    Section III.C describes sampling; Section VI concedes limited generalizability, yet Sections V and VII generalize to industry practice and make recommendations.

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Pith. "Pith review of An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI." pith.science (2026). https://pith.science/paper/3WTHLHYI

@misc{pith2026250115691,
  author       = {Pith},
  title        = {Pith review of: An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3WTHLHYI}},
  note         = {Machine review of arXiv:2501.15691}
}
read the original abstract

Incorporating responsible practices into software engineering (SE) for AI is essential to ensure ethical principles, societal impact, and accountability remain at the forefront of AI system design and deployment. This study investigates the ethical challenges and complexities inherent in responsible software engineering (RSE) for AI, underscoring the need for practical,scenario-driven operational guidelines. Given the complexity of AI and the relative inexperience of professionals in this rapidly evolving field, continuous learning and market adaptation are crucial. Through qualitative interviews with seven practitioners(conducted until saturation), quantitative surveys of 51 practitioners, and static validation of results with four industry experts in AI, this study explores how personal values, emerging roles, and awareness of AIs societal impact influence responsible decision-making in RSE for AI. A key finding is the gap between the current state of the art and actual practice in RSE for AI, particularly in the failure to operationalize ethical and responsible decision-making within the software engineering life cycle for AI. While ethical issues in RSE for AI largely mirror those found in broader SE process, the study highlights a distinct lack of operational frameworks and resources to guide RSE practices for AI effectively. The results reveal that current ethical guidelines are insufficiently implemented at the operational level, reinforcing the complexity of embedding ethics throughout the software engineering life cycle. The study concludes that interdisciplinary collaboration, H-shaped competencies(Ethical-Technical dual competence), and a strong organizational culture of ethics are critical for fostering RSE practices for AI, with a particular focus on transparency and accountability.

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