Ethical AI: Towards Defining a Collective Evaluation Framework

Aasish Kumar Sharma; Dimitar Kyosev; Julian Kunkel

REVIEW 3 major objections 6 minor 42 references

Ethical AI: Towards Defining a Collective Evaluation Framework

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Ethical principles can be packaged into discrete, auditable 'ontological blocks' that AI systems apply directly, and the paper shows how this makes ethical evaluation scalable and legally reviewable.

desk verdict Vision paper with a valid kernel but an undefined 'sum of blocks' that the EU AI Act claim depends on; no implementation or comparison to baselines. read the letter →

arxiv 2506.00233 v1 pith:6HIVDVDE submitted 2025-05-30 cs.AI

Aasish Kumar Sharma , Dimitar Kyosev , Julian Kunkel This is my paper

classification cs.AI

keywords ethicalAIontologicalblocksofmeaningFAIRdataprinciplesEUActmachineethicsexplainabilityinvestorriskprofilingtransparency

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 proposes a modular framework in which ethical principles such as fairness, accountability, and ownership are encoded as "ontological blocks of meaning"—discrete, machine-interpretable units, each a primary concept paired with a good/bad qualifier. It argues that combining these blocks with FAIR (Findable, Accessible, Interoperable, Reusable) principles yields a scalable, transparent way to assess AI systems, including compliance with the EU AI Act. The central demonstration is an AI-powered investor-profiling use case that triggers a "Riskier" block when behavioral responses suggest emotional vulnerability, enabling dynamic, behavior-informed risk classification. A sympathetic reader would care because the framework converts ethical talk from abstract principle into a structured form that can be inspected, reused, and audited, while the authors themselves flag that creating consistent blocks still requires substantial human effort.

What carries the argument

The central object is the 'ontological block of meaning'—a discrete, structured representation of an ethical principle, built as a primary concept paired with a binary ethical qualifier (e.g., 'stealing is bad'), and combinable with other blocks to encode complex ethical questions. The mechanism is the translation of abstract ethical terms into machine-readable constructs using Semantic Web standards such as RDF and OWL, with FAIR principles applied to make the blocks findable, accessible, interoperable, and reusable. The work this does is to turn ethical reasoning into a modular, auditable form that an AI pipeline can apply and that regulators or auditors can trace back to specific, human-designed definitions.

What would settle it

Take one act, such as a bank sharing a client's financial data, and compare two jurisdictions with different consent laws: in one the act is legally permissible, in the other it is prohibited. If the same ontological block (e.g., 'data sharing is bad') returns the same binary qualifier in both settings, the framework cannot represent the very contextual variation the paper argues is essential, and the claim of legally aligned evaluation falls. A stronger empirical version would be to run the framework on a set of real EU AI Act compliance cases and check whether its verdicts match the regulators' decisions on the same systems.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that ethical assessment can be operationalized by representing each ethical principle as a primary concept with a binary ethical qualifier, and by describing more complex ethical questions as sums of multiple such ontological blocks. It further claims that integrating these blocks with FAIR principles makes them discoverable, accessible, interoperable, and reusable, supporting scalable and transparent ethical evaluations aligned with the EU AI Act. The investor-profiling case illustrates a concrete mechanism: when natural-language analysis of a client's answers estimates, say, a 60% probability of emotional vulnerability, the corresponding ontological block triggers and ethically restricts access to high-risk products. The paper positions this not as a finished system but as a feasibility study, with remaining challenges in automating block creation and handling probabilistic reasoning over incomplete data.

Load-bearing premise

The whole framework depends on the assumption that an ethical principle can be captured as a single concept with a binary good/bad qualifier, and that any complex ethical question is just a combination of such blocks—if ethical judgments resist that simple two-valued structure, the scalability and legal-alignment claims collapse.

Editorial extensions

If this is right

If the framework is right, AI systems can attach auditable ethical provenance to their decisions—each outcome traces back to specific blocks and their definitions rather than to an opaque model.
Blocks built once for one domain, such as healthcare, could be combined and reused in other domains, such as finance, because the FAIR integration makes them interoperable instead of siloed.
Regulators and oversight bodies could inspect the set of blocks an AI system uses and check whether those blocks match legal standards such as the EU AI Act, supporting compliance review without requiring full model transparency.
Ethical evaluation would no longer depend on the system's training data, since the blocks are defined externally by experts and stakeholders, giving evaluations a degree of independence from the model.
The investor-profiling path suggests that ethical constraints can be triggered dynamically by behavioral signals, enabling real-time adjustments to what an AI is allowed to do for a given user.

Reading between the lines

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

The binary-qualifier structure naturally invites an extension to multi-valued or probabilistic qualifiers—a 'bad' could carry a degree or a confidence interval—which the paper leaves implicit but which would make the blocks more expressive.
The reliance on expert-designed blocks means the framework's real-world politics are about who chooses block definitions and who audits them; this is a governance question that the paper's technical framing does not address.
A testable extension would define a small library of blocks for a single domain, such as credit lending, and check whether the resulting classifications match decisions made by human ethics boards on the same cases.
If the block decomposition is made explicit, one could search for pairs of ethical judgments that a block sum cannot separate—potential counterexamples to the claim that sums of binary blocks capture all complex ethical questions.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a position paper, not a demonstrated system. The genuinely new bit is the idea of 'ontological blocks of meaning'—a single concept paired with a binary good/bad qualifier, combined by a 'sum' to handle complex questions, with FAIR principles for interoperability. That's a clean packaging of existing ideas from AIPO, Guizzardi, and ethics-by-design, and the paper says so. The writing is clear, and the investor-profiling use case is well chosen: it targets a real regulatory problem where behavior-informed classification could help.

The soft spots are real and load-bearing. The 'sum of multiple ontological blocks' in Section IV.D has no formal semantics. The paper itself argues in IV.B that thresholds are context-dependent (privacy relaxes in a murder investigation but not in banking), yet a binary good/bad qualifier cannot represent that kind of trade-off or override unless a context-selection rule is specified. None is. So the central claim of 'legally aligned ethical evaluations, including compliance with the EU AI Act' is unsupported at exactly the point where it needs precision. The use case sidesteps the problem: one hand-set 60% threshold and one triggered block, so no composition is exercised. There's also no implementation, no benchmark, and no comparison to AIPO or Guizzardi as baselines; the 'evaluation' in IV.H is the authors' qualitative judgment. These are absence-of-evidence problems rather than evidence of a false conclusion, but they're enough to keep the current version from supporting its claims.

The citation pattern is honest: they cite the relevant ontological frameworks, though they don't use them as baselines. Who's this for? Someone wanting a compact overview of how ontologies could plug into AI ethics, or a workshop discussion. To deserve serious publication, the authors would need to define the composition operation, work through a case with two conflicting principles, and map the blocks to concrete EU AI Act articles.

My recommendation: send it to peer review if the venue is lenient (e.g., a workshop), but a top journal would desk reject it in this form. It's a decent seed, not a finished framework.

Referee Report

3 major / 6 minor

Summary. The paper proposes a modular ethical assessment framework based on 'ontological blocks of meaning'—discrete, interpretable units encoding ethical principles—integrated with FAIR principles. The authors argue this framework supports scalable, transparent, and legally aligned ethical evaluations, including compliance with the EU AI Act, and demonstrate the idea through an AI-powered investor-profiling use case. The manuscript is a qualitative, conceptual proposal with no implementation, formal specification, benchmark, or data.

Significance. If the framework were fully specified and validated, it could contribute a useful modular representation for AI ethics, particularly through its FAIR alignment and its attention to context-dependent legal thresholds. Strengths of the paper include a clear literature review, a concrete use case, and candid acknowledgment of limitations (Section IV.F, IV.H, V). The proposal's independence from training data and its auditable blocks are appealing. However, the significance is currently limited by the absence of a formal definition of block composition and by the lack of any empirical or independent evaluation; the claims made in the Abstract outrun what the manuscript actually demonstrates.

major comments (3)

[IV.D and IV.E] The central mechanism of the framework, the 'sum of multiple ontological blocks' (Section IV.D), is never formally defined. Section IV.E states that each block is a 'primary concept paired with a binary ethical qualifier,' but the operation that combines blocks—whether it is a logical conjunction, a weighted aggregate, an override, or a context-dependent selection—is absent. Without a precise semantics for this sum, the framework cannot deliver the 'scalable, transparent, and legally aligned ethical evaluations' promised in the Abstract. The investor-profiling use case in Section IV.G does not exercise the composition mechanism, since it triggers a single 'Riskier' block from a hand-set 60% probability threshold, so it provides no evidence for the composition claim.
[IV.E and IV.B] The binary ethical qualifier (good/bad) is not expressive enough to represent the context-dependent trade-offs that Section IV.B itself highlights. The manuscript correctly notes that privacy may be relaxed in a murder investigation while fiduciary duty demands stricter standards in banking, but a binary good/bad block cannot encode such nuanced, condition-dependent hierarchies without an explicit rule for choosing among conflicting blocks or overriding a block in a given context. No such rule is provided. This is a load-bearing gap because the claimed EU AI Act alignment depends on the ability to reason about context-sensitive ethical requirements, not just to label single concepts as good or bad.
[IV.G and IV.H] The evaluation section (IV.H) asserts that the framework 'effectively identifies ethical risks and supports compliance (e.g., EU AI Act),' but this is a self-assessment by the authors on their own proposal, with no independent benchmark, prototype implementation, or empirical data. The use case in Section IV.G relies on an illustrative 60% probability threshold that is neither justified nor subjected to sensitivity analysis, and the 'behavioral risk profile' is described only qualitatively. Consequently, the Abstract's claim that the framework 'supports scalable, transparent, and legally aligned ethical evaluations' is not substantiated by any reproducible evidence.

minor comments (6)

[Title page] The affiliation 'G¨ottingent University' contains a typographical error; it should be 'Georg-August-Universität Göttingen' or similar, and '3th' should be '3rd.'
[Section II] There is an unresolved citation placeholder '[ ?]' in the sentence beginning 'Despite these efforts, ethical frameworks often lack technical grounding,' and the reference list contains a broken URL with 'V ol-2505' instead of 'Vol-2505.'
[Section II] The abbreviation 'Self Reinforcement Learning (SLR)' appears inconsistent with the earlier use of 'SRL' and with standard terminology; the authors should use a consistent and correct abbreviation throughout.
[Section IV.C] The claim that 'Ontology—the philosophical study of definitions' is imprecise; ontology in philosophy is typically the study of being and existence, while in information science it is an explicit specification of a conceptualization. A more precise definition would strengthen the paper's conceptual foundation.
[Section IV.E] The bullet list contains a formatting typo: 'Ac-countability' is hyphenated incorrectly and should be 'Accountability.'
[References] Several references are incomplete, such as [8] lacking full bibliographic details and [40] containing a duplicate URL that points to a Springer chapter unrelated to the cited Gulf Journal article.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is a qualitative proposal whose self-assessment is narrative, with no fitted parameters, no equations, and no load-bearing self-citations.

full rationale

The paper does not contain a derivation chain that reduces a predicted result to its inputs. Section IV.A explicitly states that the study uses descriptive research and qualitative observation and does not conduct empirical simulations. The central claims, such as that ontological blocks provide structured, modular, and interpretable ethical encoding, or that the investor use case demonstrates dynamic, behavior-informed risk classification, are assertions and illustrative scenarios rather than results computed from fitted parameters. The 60% threshold in Section IV.G is a hand-set example, not a fitted parameter, and the triggered ontological block is a designer-defined rule, so no prediction is statistically forced. No self-citations by the authors are load-bearing, and no uniqueness theorem is imported from prior work by the same authors. The nearest concern is self-evaluation: Section IV.H presents the framework's own design properties as an 'evaluation,' but this is a validation gap, not a circular reduction, since the claimed properties are not derived from the framework's outputs by construction. The paper is therefore best assessed as lacking formal evidence for its strongest claims, but not as circular.

Assumptions & free parameters 1 free parameters · 5 assumptions · 2 invented entities

The central claim rests on several unstated domain assumptions: that ethics are decomposable into binary blocks, that block composition preserves meaning, that expert selection is reliable, that NLP can estimate behavioral risk, and that FAIR metadata apply cleanly to ethical modules. The only hand-set number is the 60 percent threshold in the illustrative investor scenario.

free parameters (1)

Emotional vulnerability probability threshold = 60% (illustrative, not calibrated)
In Section IV.G, the use case triggers the 'Riskier' block at a 60 percent probability estimated from NLP/probabilistic modeling. The number is chosen by hand and no data or sensitivity analysis supports it.

assumptions (5)

domain assumption Ethical concepts can be decomposed into discrete, meaning-discrete ontological blocks with a primary concept and a binary qualifier.
Section IV.E states the 'common structure' as a concept paired with a binary ethical qualifier, which must hold for the framework to be modular and auditable.
domain assumption Complex ethical questions can be represented as a sum of simpler ontological blocks without loss of meaning.
Section IV.D: 'more complex ethical questions would be described as a sum of multiple ontological blocks.' This compositionality is assumed, not proven.
domain assumption Expert-selected block variables preserve ethical meaning and ensure public auditability.
Section IV.E: 'Block variables are selected by designers... ensuring both expert insight and public auditability.' No mechanism ensures experts agree or that selected variables are ethically correct.
domain assumption NLP and probabilistic modeling can estimate behavioral risk profiles (e.g., emotional vulnerability) well enough to trigger blocks.
Section IV.G relies on 'NLP and probabilistic modeling' to estimate a behavioral risk profile; no system, training data, or accuracy evidence is provided.
domain assumption FAIR principles can be applied to ethical assessment modules without conflict.
Section IV.H claims FAIR principles 'enhance discoverability, usability, and scalability' of ethical blocks, but no concrete FAIR metadata or interoperability mechanism is specified.

invented entities (2)

Ontological blocks of meaning
purpose: Discrete, interpretable units encoding ethical principles (e.g., fairness, accountability, ownership) that AI systems can use for ethical evaluation.
The paper proposes these blocks as the core new construct (Introduction, Section IV.C) but provides no external validation, schema, or falsifiable prediction that would tie the construct to observable outcomes outside the framework.
'Riskier' block
purpose: A use-case-specific block that restricts access to high-risk financial products when behavioral signals exceed a threshold.
Defined in Section IV.G; it is an illustrative example and is never implemented or tested.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Ethical AI: Towards Defining a Collective Evaluation Framework." pith.science (2026). https://pith.science/paper/6HIVDVDE

@misc{pith2026250600233,
  author       = {Pith},
  title        = {Pith review of: Ethical AI: Towards Defining a Collective Evaluation Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6HIVDVDE}},
  note         = {Machine review of arXiv:2506.00233}
}

read the original abstract

Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems. This article addresses these challenges by proposing a modular ethical assessment framework built on ontological blocks of meaning-discrete, interpretable units that encode ethical principles such as fairness, accountability, and ownership. By integrating these blocks with FAIR (Findable, Accessible, Interoperable, Reusable) principles, the framework supports scalable, transparent, and legally aligned ethical evaluations, including compliance with the EU AI Act. Using a real-world use case in AI-powered investor profiling, the paper demonstrates how the framework enables dynamic, behavior-informed risk classification. The findings suggest that ontological blocks offer a promising path toward explainable and auditable AI ethics, though challenges remain in automation and probabilistic reasoning.

Figures

Figures reproduced from arXiv: 2506.00233 by the authors.

**Figure 3.** Sketch: (b) Representation of ontological blocks for responsible AI. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

42 extracted references · 34 canonical work pages

[1]

The global landscape of ai ethics guidelines,

A. Jobin, M. Ienca, and E. Vayena, “The global landscape of ai ethics guidelines,” Nature Machine Intelligence , vol. 1, no. 9, pp. 389–399, 2019. [Online]. Available: https://www.nature.com/articles/ s42256-019-0088-2 1http://www.nhr-verein.de/en/ourpartners

work page 2019
[2]

Ethical framework for artificial intelligence in healthcare research: A path to integrity,

A. A. Abujaber and A. J. Nashwan, “Ethical framework for artificial intelligence in healthcare research: A path to integrity,” World Journal of Methodology , vol. 14, no. 3, p. 94071, 9 2024, pMID: 39310239; PMCID: PMC11230076. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11230076/

work page 2024
[3]

[Online]

“Regulation (EU) 2024/1689 of the european parliament and of the council of 13 june 2024 laying down harmonised rules on artificial intelligence and amending regulations (EC) no 300/2008, (EU) no 167/2013, (EU) no 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (artificial intelligence...

work page 2024
[4]

Ethical ai for the governance of the society: Challenges and opportunities,

N. Mika, G. Nadezhda, L. Jaana, and K. Raija, “Ethical ai for the governance of the society: Challenges and opportunities,” in CEUR Workshop Proceedings, vol. 2505, 2019, pp. 20–26. [Online]. Available: https://ceur-ws.org/V ol-2505/paper03.pdf

work page 2019
[5]

The ethics of artificial intelligence,

N. Bostrom and E. Yudkowsky, “The ethics of artificial intelligence,” pp. 57–69, 2018, accessed: 2025-02-07. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/10.1201/9781351251389-4/ ethics-artificial-intelligence-nick-bostrom-eliezer-yudkowsky

work page doi:10.1201/9781351251389-4/ 2018
[6]

Ethics by design for artificial intelligence,

P. Brey and B. Dainow, “Ethics by design for artificial intelligence,” AI and Ethics , vol. 4, no. 4, pp. 1265–1277, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s43681-023-00330-4

doi:10.1007/s43681-023-00330-4 2024
[7]

Ethics of artificial intelligence demarcations,

A. B. Hanssen and S. Nichele, “Ethics of artificial intelligence demarcations,” Ethics in Information Technology , pp. 133–142,

work page
[8]

Explainable AI: A review of machine learning interpretability methods,

P. Linardatos, V . Papastefanopoulos, and S. Kotsiantis, “Explainable AI: A review of machine learning interpretability methods,” vol. 23, no. 1, p. 18. [Online]. Available: https://www.mdpi.com/1099-4300/23/1/18

work page

Show all 42 references

[9]

Revisiting sparse rewards for goal-reaching reinforcement learning,

G. Vasan, Y . Wang, F. Shahriar, J. Bergstra, M. Jagersand, and A. R. Mahmood, “Revisiting sparse rewards for goal-reaching reinforcement learning,” arXiv preprint arXiv:2407.00324 , 2024. [Online]. Available: https://arxiv.org/abs/2407.00324

2024 arXiv
[10]

Comprehensive overview of reward engineering and shaping in advancing reinforcement learning applications,

S. Ibrahim, M. Mostafa, A. Jnadi, H. Salloum, and P. Osinenko, “Comprehensive overview of reward engineering and shaping in advancing reinforcement learning applications,” IEEE Access , vol. 12, pp. 175 473–175 500, 2024. [Online]. Available: https://ieeexplore.ieee. org/docum...

2024
[11]

Human-in-the-loop machine learning: a state of the art,

E. Mosqueira-Rey, E. Hernandez-Pereira, D. Alonso-Rios, J. Bobes- Bascaran, and A. Fernandez-Leal, “Human-in-the-loop machine learning: a state of the art,” Artificial Intelligence Review , vol. 56, no. 4, pp. 3005–3054, 4 2023. [Online]. Available: https://doi.org/10.1007/s10...

2023 doi
[12]

A survey on bias and fairness in machine learning,

N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A survey on bias and fairness in machine learning,” ACM Computing Surveys (CSUR) , vol. 54, no. 6, pp. 1–35, 2021. [Online]. Available: https://doi.org/10.1145/3457607

2021 doi
[13]

From ethical ai frameworks to tools: a review of approaches,

E. Prem, “From ethical ai frameworks to tools: a review of approaches,” AI and Ethics , vol. 3, no. 3, pp. 699–716, 2023, accessed: 2025-02-07. [Online]. Available: https://doi.org/10.1007/s43681-023-00258-9

2023 doi
[14]

A. M. Salih, Z. Raisi-Estabragh, I. Boscolo Galazzo, P. Radeva, S. E. Petersen, K. Lekadir, and G. Menegaz. A perspective on explainable artificial intelligence methods: Shap and lime. [Online]. Available: https://arxiv.org/abs/2305.02012v3

arXiv
[15]

Building ethically bounded ai,

F. Rossi and N. Mattei, “Building ethically bounded ai,” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , pp. 68–74,
[16]

Societal bias reinforcement through machine learning: a credit scoring perspective,

B. K. Hassani, “Societal bias reinforcement through machine learning: a credit scoring perspective,” AI and Ethics , vol. 1, no. 3, pp. 239–247,
[17]

Disability discrimination using ai systems, social media and digital platforms: Can we disable digital bias?

N. G. Packin, “Disability discrimination using ai systems, social media and digital platforms: Can we disable digital bias?” Journal of International and Comparative Law , vol. 8, no. 2, pp. 487–512, 2021. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract i...

2021
[18]

Artificial intelligence (ai) in mental health diagnosis and treatment,

D. Talati, “Artificial intelligence (ai) in mental health diagnosis and treatment,” Journal of Knowledge Learning and Science Technology , vol. 2, no. 3, pp. 251–253, 2023. [Online]. Available: https: //doi.org/10.60087/jklst.vol2.n3.p253

2023 doi
[19]

Oncology ontology in the nci thesaurus,

A. Kumar and B. Smith, “Oncology ontology in the nci thesaurus,” in Artificial Intelligence in Medicine: 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005. Proceedings 10 , Springer. Springer, 2005, pp. 213–220. [Online]. Availab...

2005 doi
[20]

Cancer care treatment outcome ontology: a novel computable ontology for profiling treatment outcomes in patients with solid tumors,

F. P. Lin, T. Groza, S. Kocbek, E. Antezana, and R. J. Epstein, “Cancer care treatment outcome ontology: a novel computable ontology for profiling treatment outcomes in patients with solid tumors,” JCO clinical cancer informatics , vol. 2, pp. 1–14, 2018. [Online]. Available: ...

2018 doi
[21]

Artificial intelligence and increasing misinformation,

S. Monteith, T. Glenn, J. R. Geddes, P. C. Whybrow, E. Achtyes, and M. Bauer, “Artificial intelligence and increasing misinformation,” The British Journal of Psychiatry , vol. 224, no. 2, pp. 33–35, 2024. [Online]. Available: https://doi.org/10.1192/bjp.2023.136

2024 doi
[22]

Mental health diagnosis: a case for explainable artificial intelligence,

G. Antoniou, E. Papadakis, and G. Baryannis, “Mental health diagnosis: a case for explainable artificial intelligence,” International Journal on Artificial Intelligence Tools , vol. 31, no. 03, p. 2241003, 2022. [Online]. Available: https://doi.org/10.1142/S0218213022410032

2022 doi
[23]

Why ethics matters for autonomous cars,

P. Lin, “Why ethics matters for autonomous cars,” Autonomous driving: Technical, legal and social aspects, pp. 69–85, 2016. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-662-45854-9 4

2016 doi
[24]

Jenkins, D

R. Jenkins, D. Cern `y, and T. Hr ´ıbek, Autonomous vehicle ethics: the trolley problem and beyond . Oxford University Press, 2022. [Online]. Available: https://academic.oup.com/book/44058

2022
[25]

Machine ethics and automated vehicles,

N. J. Goodall, “Machine ethics and automated vehicles,” Road vehicle automation , pp. 93–102, 2014. [Online]. Available: https: //link.springer.com/chapter/10.1007/978-3-319-05990-7 9

2014 doi
[26]

The ethics of accident-algorithms for self-driving cars: An applied trolley problem?

S. Nyholm and J. Smids, “The ethics of accident-algorithms for self-driving cars: An applied trolley problem?” Ethical theory and moral practice , vol. 19, no. 5, pp. 1275–1289, 2016. [Online]. Available: https://link.springer.com/article/10.1007/s10677-016-9745-2

2016 doi
[27]

Owl web ontology language overview,

D. L. McGuinness, F. Van Harmelen et al. , “Owl web ontology language overview,” W3C recommendation , vol. 10, no. 10, p. 2004, 2004. [Online]. Available: https://static.twoday.net/71desa1bif/ files/W3C-OWL-Overview.pdf

2004
[28]

Web ontology language: Owl,

G. Antoniou and F. v. Harmelen, “Web ontology language: Owl,” Handbook on ontologies , pp. 91–110, 2009. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-540-92673-3 4

2009 doi
[29]

An ontology-based approach to engineering ethicality requirements,

R. Guizzardi, G. Amaral, G. Guizzardi, and J. Mylopoulos, “An ontology-based approach to engineering ethicality requirements,” Software and Systems Modeling , vol. 22, no. 6, pp. 1897–1923, Jul. 2023. [Online]. Available: https://link.springer.com/article/10.1007/ s10270-023-01115-3

1923
[31]

Towards a framework for understanding societal and ethical implications of artificial intelligence,

R. Benjamins and I. Salazar, “Towards a framework for understanding societal and ethical implications of artificial intelligence,” CoRR, vol. abs/2001.09750, 2020. [Online]. Available: https://arxiv.org/abs/2001. 09750

2001 arXiv
[32]

An international consortium for evaluations of societal-scale risks from advanced ai,

R. Gruetzemacher, A. Chan, K. Frazier, C. Manning, S. Los, J. Fox, J. Hern ´andez-Orallo, J. Burden, M. Franklin, C. N. Ghuidhir, M. Bailey, D. Eth, T. Pilditch, and K. Kilian, “An international consortium for evaluations of societal-scale risks from advanced ai,” Nov. 2023, a...

2023 arXiv
[33]

The role of rri in the ethical governance of AI | RRI-leaders,

R. Leaders, “The role of rri in the ethical governance of AI | RRI-leaders,” 2023. [Online]. Available: https://www.rri-leaders.eu/ the-role-of-rri-in-the-ethical-governance-of-ai/

2023
[34]

Exploring the dimensions of responsible research systems and cultures: a scoping review,

S. M. Field, J. Thompson, S. De Rijcke, B. Penders, and M. R. Munaf `o, “Exploring the dimensions of responsible research systems and cultures: a scoping review,” Royal Society Open Science , vol. 11, no. 1, p. 230624, 2024. [Online]. Available: https://doi.org/10.1098/rsos.230624

2024 doi
[35]

Frames, interests, and incentives–a typology of institutionalizing rri in the business sector derived from ten pioneering projects,

S. Ivanova, C. Reichetzer, A. Martinuzzi, F. Findler, and K. Miko-Schefzig, “Frames, interests, and incentives–a typology of institutionalizing rri in the business sector derived from ten pioneering projects,” Journal of Responsible Innovation , vol. 10, no. 1, p. 2267736, 202...

2023
[36]

V ¨olker, R

T. V ¨olker, R. Slaattelid, and R. Strand, Translations of Responsibility: Innovation Governance in Three European Regions . Taylor & Francis, 2024. [Online]. Available: https://library.oapen.org/handle/20. 500.12657/86044

2024
[37]

Introducing responsible innovation in health: a policy-oriented framework,

H. Pacifico Silva, P. Lehoux, F. A. Miller, and J.-L. Denis, “Introducing responsible innovation in health: a policy-oriented framework,” Health research policy and systems , vol. 16, pp. 1–13, 2018. [Online]. Available: https://link.springer.com/article/10.1186/s12961-018-0362-5

2018 doi
[38]

A terminological and ontological analysis of the nci thesaurus,

W. Ceusters, B. Smith, and L. Goldberg, “A terminological and ontological analysis of the nci thesaurus,” Methods of information in medicine , vol. 44, no. 04, pp. 498–507, 2005, accessed: 2025- 02-07. [Online]. Available: https://www.thieme-connect.com/products/ ejournals/abs...

2005 doi
[39]

An ontology for ethical ai principles,

J. Harrison, R. Smith, and L. Thompson, “An ontology for ethical ai principles,” AI and Society, vol. 36, pp. 123–136, 2021, accessed: 2025- 02-07. [Online]. Available: https://doi.org/10.1007/s00146-020-01046-7

2021 doi
[40]

Ai and data-driven insights: Transforming customer relationship management (crm) in financial services,

N. S. Egbuhuzor, A. J. Ajayi, E. E. Akhigbe, O. O. Agbede, C. P.-M. Ewim, and D. I. Ajiga, “Ai and data-driven insights: Transforming customer relationship management (crm) in financial services,” Gulf Journal of Advance Business Research , vol. 3, no. 2, pp. 483–511, 2025. [O...

2025 doi
[2018]

Available: https://doi.org/10.48550/arXiv.1812.03980

[Online]. Available: https://doi.org/10.48550/arXiv.1812.03980

doi arXiv
[2019]

Available: https://link.springer.com/chapter/10.1007/ 978-3-030-35664-4 13

[Online]. Available: https://link.springer.com/chapter/10.1007/ 978-3-030-35664-4 13
[2021]

Available: http://dx.doi.org/10.2139/ssrn.3625691

[Online]. Available: http://dx.doi.org/10.2139/ssrn.3625691

doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

The global landscape of ai ethics guidelines,

A. Jobin, M. Ienca, and E. Vayena, “The global landscape of ai ethics guidelines,” Nature Machine Intelligence , vol. 1, no. 9, pp. 389–399, 2019. [Online]. Available: https://www.nature.com/articles/ s42256-019-0088-2 1http://www.nhr-verein.de/en/ourpartners

work page 2019

[2] [2]

Ethical framework for artificial intelligence in healthcare research: A path to integrity,

A. A. Abujaber and A. J. Nashwan, “Ethical framework for artificial intelligence in healthcare research: A path to integrity,” World Journal of Methodology , vol. 14, no. 3, p. 94071, 9 2024, pMID: 39310239; PMCID: PMC11230076. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11230076/

work page 2024

[3] [3]

[Online]

“Regulation (EU) 2024/1689 of the european parliament and of the council of 13 june 2024 laying down harmonised rules on artificial intelligence and amending regulations (EC) no 300/2008, (EU) no 167/2013, (EU) no 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (artificial intelligence...

work page 2024

[4] [4]

Ethical ai for the governance of the society: Challenges and opportunities,

N. Mika, G. Nadezhda, L. Jaana, and K. Raija, “Ethical ai for the governance of the society: Challenges and opportunities,” in CEUR Workshop Proceedings, vol. 2505, 2019, pp. 20–26. [Online]. Available: https://ceur-ws.org/V ol-2505/paper03.pdf

work page 2019

[5] [5]

The ethics of artificial intelligence,

N. Bostrom and E. Yudkowsky, “The ethics of artificial intelligence,” pp. 57–69, 2018, accessed: 2025-02-07. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/10.1201/9781351251389-4/ ethics-artificial-intelligence-nick-bostrom-eliezer-yudkowsky

work page doi:10.1201/9781351251389-4/ 2018

[6] [6]

Ethics by design for artificial intelligence,

P. Brey and B. Dainow, “Ethics by design for artificial intelligence,” AI and Ethics , vol. 4, no. 4, pp. 1265–1277, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s43681-023-00330-4

doi:10.1007/s43681-023-00330-4 2024

[7] [7]

Ethics of artificial intelligence demarcations,

A. B. Hanssen and S. Nichele, “Ethics of artificial intelligence demarcations,” Ethics in Information Technology , pp. 133–142,

work page

[8] [8]

Explainable AI: A review of machine learning interpretability methods,

P. Linardatos, V . Papastefanopoulos, and S. Kotsiantis, “Explainable AI: A review of machine learning interpretability methods,” vol. 23, no. 1, p. 18. [Online]. Available: https://www.mdpi.com/1099-4300/23/1/18

work page