The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Lessons learned from AI ethics principles for future actions
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This is increasingly important in scenarios where AI interacts closely with humans, such as in healthcare, finance, and personal assistants, to ensure that AI systems make decisions that are not only technically sound, but also ethically and socially responsible, which means human value alignment is becoming crucial in developing AI systems that are trusted and accepted by society [89], [267].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [267]
In the citing paper
“However, it should be noted that this reliance on principalism [39] is contested within the biomedical community [79] and has been questioned in the AI field [67,92], but at present remains dominant.”
From From computer ethics and the ethics of AI towards an ethics of digital ecosystems (2022) · cites this paper as [67]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 1×
“[7] review 84 AI ethics guidelines publicly released.”
BackgroundFloridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796
(2018) · Cited by 3,510
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,320
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887
Algorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709
(2019) · Cited by 2,637
The age of surveillance capitalism: the fight for a human future at the new frontier of power
Shandler, R. (2019) · Journal of Cyber Policy · doi:10.1080/23738871.2019.1637914 · Cited by 2,113
Research works printed but not linked: 2
- Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., West, S.M., Richardson, R., Schultz, J., Schwartz, O.: AI Now Report 2018, pp. 1–62 https://ainowinstitute.org/AI_Now_2018_Report.pdf (2018)
- Shoham, Y., Perrault, R., Brynjolfsson, E., Clark, J., Manyika, J., Niebles, J.C., Lyons, T., Etchemendy, J., Grosz, B., Bauer, Z.: The AI Index 2018 Annual Report. AI Index Steering Committee, Human-Centered AI Initiative, Stanford University, Stanford (2018)
Cited by
Sorted by citations
Items 101–168 of 168
Artificial Intelligence: Management Challenges and Responsibility
Altenburger, R. (2023) · CSR, sustainability, ethics & governance · doi:10.1007/978-3-031-09245-9_1 · Cited by 2
Artificial Intelligence: A Case for Ethical Design and Multidisciplinarity
Agerwala, T. (2024) · Springer eBooks · doi:10.1007/978-981-97-0503-0_4 · Cited by 2
Artificial intelligence and the ethics of tomorrow
Wyk, V., & Holmner, M. (2024) · Proceedings of the International Conference on AI Research. · doi:10.34190/icair.5.1.3017 · Cited by 2
AI-Driven Social Media Analysis for Disaster Situational Awareness in Smart Urban Environments
Singh, R., Manoharan, G., Singh, S., & Singh, S. (2025) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/979-8-3693-7832-8.ch007 · Cited by 2
Joseph, S., Arar, E. A., Schunka, L., Karaburk, H., & Arar, K. H. (2025) · Power and Education · doi:10.1177/17577438251338540 · Cited by 2
“· Ethical Leadership: State education departments could establish standards for ethical leadership, requiring transparency, accountability, and anti-racist practices in school district policies (Hickok, 2021; Shields, 2017).”
Board of directors’ attributes and artificial intelligence disclosure: evidence from Egyptian banks
El-Sayed, D. H., & Ahmed, E. A. (2025) · Journal of financial reporting & accounting · doi:10.1108/jfra-01-2025-0044 · Cited by 2
Co-Designing Islamic AI Ethics: Insights from the UK Muslim Community
Campbell-Esen, R., Mahoney, J., & Talhouk, R. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790413 · Cited by 6
“In doing so, we contribute to the cultural and epistemic pluralism of AI ethics and governance called for by HCI and ethical AI research [8, 37, 50, 62, 64, 98, 105, 112] and offer actionable ways in which we may go about achieving ethical AI from the perspective of Muslim women in the UK.”
Powell, R. (2023) · BJHS Themes · doi:10.1017/bjt.2023.8 · Cited by 2
AI in High-Stakes and Standardized Testing
Trajkovski, G., & Hayes, H. (2025) · Digital education and learning · doi:10.1007/978-3-031-88252-4_8 · Cited by 2
Generative AI for Customized Public Policy in Maritime Transport
Kumar, R., & Yadav, C. L. (2024) · Advances in logistics, operations, and management science book series · doi:10.4018/979-8-3693-5578-7.ch010 · Cited by 2
Frost, E. K., Bosward, R., Aquino, Y. S. J., Braunack‐Mayer, A., & Carter, S. M. (2021) · Research Square · doi:10.21203/rs.3.rs-1109170/v1 · Cited by 2
Explainability in Process Mining: A Framework for Improved Decision-Making
Nannini, L. (2023) · AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604729 · Cited by 3
“Yet, this so-called “first wave” of AI ethics publications often offered ambiguous if not conflictual terminology, lacking empirical benchmarks [1, 10]: this calls for the current advancements of a “second wave” of AI ethics to focus on empirical and comprehensive experimentations for the AI systems’ lifecycle [13, 17…”
BackgroundIntroduction to Responsible Use of AI in Military Systems
Schraagen, J. M. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003410379-1 · Cited by 1
Ethics in NDE 4.0: Perspectives and Possibilities
Singh, R. R., & Clifford, T. (2025) · Springer eBooks · doi:10.1007/978-3-031-84477-5_61 · Cited by 1
The representation problem in AI ethics
Ye, B. (2025) · Ai & Society · doi:10.1007/s00146-025-02538-w · Cited by 1
Comprehensive and comparative analysis of AI in the energy sector
Worthmann, C., & Malik, K. (2025) · AI and Ethics · doi:10.1007/s43681-025-00728-2 · Cited by 2
“Another perspective is that AI regulations need to be facilitated at an international level to ensure inclusivity, diversity, and safety across the global market [12 , 41 ].”
Exploring AI ethics in global contexts: a culturally responsive, psychologically realist approach
Clancy, R. F., Zhu, Q., & Majumdar, S. (2025) · AI and Ethics · doi:10.1007/s43681-025-00821-6 · Cited by 6
“…and developing frameworks, tools, checklists, and processes that focus on ensuring privacy, transparency, reliability, safety, fairness, trustworthiness, and accountability in the development and application of AI (Corrêa et al. 2023 ; Hickok 2021; Kazim and Koshiyama 2021; Khan et al. 2022).”
Yu, Y. F., & Bo, L. (2025) · Communication technologies et développement · doi:10.4000/155wq · Cited by 1
AI Representation of African Culture: Perspectives, Challenges, and Implications for Media Studies
Ditlhokwa, G. (2025) · Future education and learning spaces · doi:10.1007/978-981-96-9251-4_7 · Cited by 1
The specter of principlism: from bioethics to AI ethics to autonomous weapon systems
Freiberg, C., & Bishop, J. P. (2026) · AI and Ethics · doi:10.1007/s43681-025-00954-8 · Cited by 1
Principle-based AI ethics: challenges and prospects
Kiorgaard, S. (2026) · AI and Ethics · doi:10.1007/s43681-025-00959-3 · Cited by 1
Ethics in NDE 4.0: Perspectives and Possibilities
Singh, R. R., & Clifford, T. (2022) · Springer eBooks · doi:10.1007/978-3-030-73206-6_61 · Cited by 1
Baker-Brunnbauer, J. (2022) · Business guides on the go · doi:10.1007/978-3-031-18275-4_7 · Cited by 1
Seböck, W., Biron, B., & Lampoltshammer, T. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-41617-0_5 · Cited by 5
Martins, R., de Sousa, L. C., Fadel, A. S., da Silva, C. L., Bubniak, M. J., Travagin, I. C., Silva, V. A. F., Maiola, G. G. D. C., Mulinari, B. M., da Costa, C. H., et al. (2024) · Brazilian Archives of Biology and Technology · doi:10.1590/1678-4324-2024231238 · Cited by 1
The Triple Axis of AI, Ethics, and Business Economics
Medina-López, A. (2025) · Contributions to management science · doi:10.1007/978-3-031-88781-9_3 · Cited by 1
Human-centered trust framework: An HCI perspectivePreprint
Sousa, S., Cravino, J., Martins, P., & Lamas, D. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.03306 · Cited by 1
“It will also be difficult to predict AI behaviours and identify errors that can lead to biased decisions, unforeseen risks, and further user trust concerns [4,18,27,59].”
BackgroundMeasuring the Effectiveness of Ethics-Based Auditing – A Semi-Systematic Literature ReviewPreprint
Shafik, E., Puncheva, P., Bharathy, G., & Pannai, E. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5498160 · Cited by 1
Ethics in NDE 4.0: Perspectives and Possibilities
Singh, R., & Clifford, T. (2021) · Springer eBooks · doi:10.1007/978-3-030-48200-8_61-1
The urgence of the code of conduct of the professional advocate in Indonesia
Alambara, C., Warka, M., & Suhartono, S. (2022) · Technium Social Sciences Journal · doi:10.47577/tssj.v27i1.5541
Ethics and its role in the future of AI development
Khamayseh, Y., & Jiman, J. B. (2023) · AIP conference proceedings · doi:10.1063/5.0134196
Beinborn, L., & Hollenstein, N. (2023) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-43260-6_6
Coovadia, H., Marx, B., & Botha, I. (2024) · Springer proceedings in business and economics · doi:10.1007/978-3-031-46177-4_15
Ethics Versus Technological Advancements: Partners in Crime
Nemat, A. T., & Charton, J. E. (2024) · Springer eBooks · doi:10.1007/978-3-031-61593-1_2 · Cited by 1
Ethics in NDE 4.0: Perspectives and Possibilities
Singh, R. R., & Clifford, T. (2024) · Springer eBooks · doi:10.1007/978-3-030-48200-8_61-2
Yilma, K. (2025) · AI and Ethics · doi:10.1007/s43681-025-00719-3
“Ó hÉigeartaigh and colleagues note that the global AI ethics discourse is taking place in, and between, countries with significant roles in the devel - opment of AI in the exclusion of countries to which these technologies are exported [ 72, 73].”
The Social Turn in the Ethics of AI
Cossette‐Lefebvre, H. (2025) · doi:10.1002/9781394238651.ch21
The 3D model of ethical AI practice
Ball, B., & Helliwell, A. C. (2025) · AI and Ethics · doi:10.1007/s43681-025-00812-7
“11 See, for example, Canca [6 ], Hagendorff [ 20], Hickok [21], Kazim and Koshiyama [23], Mittelstadt [29], and Zhou and Chen [54].”
Eine globale Analyse von Leitlinien zum Umgang mit Künstlicher Intelligenz in Unternehmen
von Garrel, J., & Vrga, L. (2025) · Springer eBooks · doi:10.1007/978-3-658-49275-5_6
Frost, E. K., Bosward, R., Aquino, Y. S. J., Braunack-Mayer, A., & Carter, S. M. (2022) · Figshare · doi:10.6084/m9.figshare.20329324
Frost, E. K., Bosward, R., Aquino, Y. S. J., Braunack-Mayer, A., & Carter, S. M. (2022) · Figshare · doi:10.6084/m9.figshare.20329321
Additional file 1 of Integrating ethics in AI development: a qualitative study
Ossa, L. A., Lorenzini, G., Milford, S. R., Shaw, D., Elger, B. S., & Rost, M. (2024) · Figshare · doi:10.6084/m9.figshare.25052238
Additional file 2 of Integrating ethics in AI development: a qualitative study
Ossa, L. A., Lorenzini, G., Milford, S. R., Shaw, D., Elger, B. S., & Rost, M. (2024) · Figshare · doi:10.6084/m9.figshare.25052465
Ethics in Artificial Intelligence
Bhosale, P. A., Kulkarni, S., Kausar, A., Shrivastav, A., & Das, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0250-8.ch006
Human-Centered Insights Into ChatGPT in General Education
Ravi, S. S., Bhagat, S., Paramasivam, S., & Yohannan, S. S. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6831-3.ch008
Ethical Considerations and Societal Impact
Saranya, R., Vijayakumari, G., Srividhya, S., Karthi, V., & Al-Khasawneh, M. A. (2026) · Springer eBooks · doi:10.1007/978-3-032-06088-4_14
Transforming Government Through AI: Navigating Adoption, Barriers and Solutions
Aikins, S. K., & Dimitrijevska‐Markoski, T. (2026) · Springer eBooks · doi:10.1007/978-3-032-12344-2_2
Aikins, S. K., & Dimitrijevska‐Markoski, T. (2026) · Springer eBooks · doi:10.1007/978-3-032-12344-2_9
Ensuring Safety, Accountability, and Drift Resistance in LLM-Based Supply Chain Optimization
Jingar, N. K. (2023) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset2310372
Machine Learning‐Based Risk Prediction Models for Pregnancy‐Related Syndromes
Wu, Y. (2026) · Birth Defects Research · doi:10.1002/bdr2.70038
“However, translating these models to obstetrics requires comprehensive ethical and regulatory frameworks based on beneficence, autonomy, and justice principles (Farhud and Zokaei 2021; Hickok 2021; Floridi et al. 2022).”
Tasking AI Fairly. How to Empower AI Practitioners With sXAI?
Alpsancar, S., & Stamboliev, E. (2026) · Springer eBooks · doi:10.1007/978-981-96-5290-7_29
Ethics Without Teeth? Challenges and Opportunities in AI Declarations for Platform Governance
Haidar, A. (2026) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer21040103 · Cited by 2
La preuve en droit des systèmes d'intelligence artificielle
Pons, R. (2024) · theses.fr (ABES) · doi:10.70675/1e108f6ezfa39z422az8706zed4d031bb110
Loughrey, D. G., Brosens, C., Moiselle, R., Shterionov, D., Vandeghinste, V., Way, A., & Leeson, L. M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01230-z · Cited by 1
“Additionally, the guidelines on the ethical development of AI technologies and the data used may not reflect the diversity of the global population for which they are intended [94, 95].”
Guechtouli, M., & Arulanandam, B. (2026) · Question(s) de management · doi:10.3917/qdm.239.0049
Erin, O. A. (2026) · CSR, sustainability, ethics & governance · doi:10.1007/978-3-032-20091-4_10
Understanding Human Motivations in Generative Artificial Intelligence Adoption and Use
Strydom, S. C. (2026) · Springer eBooks · doi:10.1007/978-3-031-52643-5_91-1
Rethinking AI ethics as a temporal socio-technical system: a critical synthesis
Ali, A. (2026) · AI and Ethics · doi:10.1007/s43681-026-01317-7
Xie, J., 时春艳, Dou, L., Peng, D., Li, L., Xu, R., & Wu, Y. (2026) · Pregnancy Hypertension · doi:10.1016/j.preghy.2026.101506 · Cited by 1
Artificial Intelligence as Infrastructure for Epistemic Trust
Göksu, D. Y., & Duran, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1232-1.ch018
Emotional and Social Intelligence in AI Systems
Sarkar, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6836-8.ch004
Responsible AI Disclosures in the Fortune Global 500
Oliver, D., Minetola, M., Bright, T., & Burch, S. (2026) · Journal of Computer Information Systems · doi:10.1080/08874417.2026.2723098
Balayn, A., Rancourt, F., Casati, F., & Gadiraju, U. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816909
“More broadly, AI researchers and policymakers propose structuring principles [64, 106, 154], frameworks and guidelines [15, 109], toolkits [16, 85], checklists [93, 123], and other documentation tools [8, 57, 105], to support responsible AI development within and across organizations.”
Kim, E., Lee, S., & Son, J. (2026) · SAGE Open · doi:10.1177/21582440261494461
Schultz, M. D., & Seele, P. (2026) · AI and Ethics · doi:10.1007/s43681-026-01420-9
“These issues have prompted public-and private-sector organizations to develop ethical principles and responsible AI practices to ensure accountability and alignment with societal values [69, 73 , 124].”
Jiříčková, L., & Sigmund, T. (2026) · Kybernetes · doi:10.1108/k-04-2026-0992
Metadata
- Authors
- Hickok, M.
- Corresponding author
- Hickok, Merve
- ORCID
- Hickok, Merve
- Venue
- AI and Ethics
- Published
- 2020-10-06
- Volume
- 1
- Issue
- 1
- Pages
- 41-47
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- OpenAlex
- W3092523763
- Cited by
- 168
- References
- 34
- Citations by year
- 2026: 25 · 2025: 34 · 2024: 44 · 2023: 27 · 2022: 26 · 2021: 12
- FWCI
- 22.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- AI and Ethics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- AI ethics principles, AI ethics guidelines, algorithmic bias, accountability mechanisms, responsible AI development, big data, AI governance, private sector influence, diversity of voices, ethics in practice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
