Applying the ethics of AI: a systematic review of tools for developing and assessing AI-based systems
Open accessAbstract
Artificial Intelligence (AI)-based systems and their increasingly common use have made it a ubiquitous technology; Machine Learning algorithms are present in streaming services, social networks, and in the health sector. However, implementing this emerging technology carries significant social and ethical risks and implications. Without ethical development of such systems, there is the potential for this technology to undermine people’s autonomy, privacy, and equity, even affecting human rights. Considering the approaches necessary for ethical development and effective governance of AI, such as ethical principles, guidelines, and technical tools, the question arises regarding the limitations of implementing these measures by the highly technical personnel involved in the process. In this context, we propose the creation of a typology that distinguishes the different stages of the AI life-cycle, the high-level ethical principles that should govern their implementation, and the tools with the potential to foster compliance with these principles, encompassing both technical and conceptual resources. In addition, this typology will include relevant information such as developmental level, related tasks, sectors, and language. Our research is based on a systematic review in which we identified 352 resources and tools. We expect this contribution to be valuable in promoting ethical AI development for developers and leaders who manage these initiatives. The complete typology and the comprehensive list of resources are available for consultation at https://ricardo-ob.github.io/tools4responsibleai .
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Ortega-Bolaños et al. emphasize that operationalising AI ethics requires tools connected to the full system lifecycle.
“Ortega-Bolaños et al. likewise emphasize that operationalising AI ethics requires tools connected to the full system lifecycle (Ortega-Bolaños et al., 2024).”
From Human-centric AI governance in the European Union. Accountability, fundamental rights, and institutional resilience in public administration (2026) · cites this paper as Ortega-Bolaños et al., 2024
Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010) · International Journal of Surgery · doi:10.1016/j.ijsu.2010.02.007 · Cited by 12,156 · see citing works
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 3× · see citing works
“An extensive literature on AI project requirements (Fjeld et al. 2020; Hagendorff 2020; Jobin et al. 2019) gives evidence of the need for such principles.”
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812 · see citing works
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · cited 1× · see citing works
“The health sector (Morley et al. 2020; Yu et al. 2018), financial or business sector (Buchanan 2019; Loureiro et al. 2021), educational sector (Chen et al. 2020), security and justice (Hoadley and Lucas 2018; Rigano 2018), among others, are already incorporating AI-based systems, specifically…”
After Virtue: A Study in Moral Theory
Burrell, D. B., & MacIntyre, A. (1984) · Journal of Law and Religion · doi:10.2307/1051043 · Cited by 3,572 · see citing works
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · cited 1× · see citing works
“The misuse and incorrect development of AI have been the focus of debate among academics, policymakers, private companies, and civil society (Cath et al. 2018; Jia and Zhang 2021; Mittelstadt et al. 2016).”
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · cited 9× · see citing works
“Various technical resources have recently been developed to audit biases in models and datasets (Hagendorff 2020; Lee and Singh 2021; Mehrabi et al. 2021).”
InfluentialPrinciples alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886 · cited 2× · see citing works
“The documents’ definitions remain abstract and unhelpful for developers (Mittelstadt 2019; Morley et al. 2021).”
A Unified Framework of Five Principles for AI in Society
Floridi, L., & Cowls, J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.8cd550d1 · Cited by 1,631 · cited 2× · see citing works
“1 Notably, Jobin et al. (2019), Fjeld et al. (2020), Floridi and Cowls (2019), and Hagendorff (2020) scrutinize the most relevant documents proposed by organizations and governments.”
Artificial Intelligence and the Public Sector—Applications and Challenges
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2018.1498103 · Cited by 1,341 · cited 1× · see citing works
“…agencies and private companies Extended author information available on the last page of the article 110 Page 2 of 30 see significant advantages and benefits in these systems for decision-making or support due to their great precision, automation capacity, and data analytic (Wirtz et al. 2018).”
The ethics of AI in health care: A mapping review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113172 · Cited by 1,105 · cited 13× · see citing works
“While we focus primarily on the third pillar in our research, we recognize that the other two ethical approaches will also benefit all those involved in AI development and the target population (Morley et al. 2020).”
InfluentialFjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3518482 · Cited by 998 · cited 5× · see citing works
“Inspired by the research of Morley et al. (2020), Fjeld et al. (2020), and Ayling and Chapman (2021), we created a diagram found at Tools 4Resp onsib leAI, specifically in the ‘Typology’ section of the website.”
InfluentialTechnology and the Virtues: A Philosophical Guide to a Future Worth Wanting
Vallor, S. (2016) · doi:10.1093/acprof:oso/9780190498511.001.0001 · Cited by 910 · see citing works
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882 · cited 13× · see citing works
“While we focus primarily on the third pillar in our research, we recognize that the other two ethical approaches will also benefit all those involved in AI development and the target population (Morley et al. 2020).”
InfluentialThe Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1802.07228 · Cited by 849 · see citing works
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., et al. (2018) · Apollo - University of Cambridge Repository · doi:10.17863/cam.22520 · Cited by 849 · see citing works
Ethical Implications and Accountability of Algorithms
Martin, K. (2019) · Journal of Business Ethics · doi:10.1007/s10551-018-3921-3 · Cited by 757 · see citing works
Governing artificial intelligence: ethical, legal and technical opportunities and challenges
Cath, C. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0080 · Cited by 745 · cited 3× · see citing works
“Artificial Intelligence (AI) can strengthen different economic and social sectors, thus improving our quality of life (Cath 2018).”
On Defining Artificial Intelligence
Wang, P. (2019) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2019-0002 · Cited by 740 · see citing works
Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017) · Science and Engineering Ethics · doi:10.1007/s11948-017-9901-7 · Cited by 733 · see citing works
Toward understanding the impact of artificial intelligence on labor
Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900949116 · Cited by 713 · see citing works
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0279-x · Cited by 709 · see citing works
Trustworthy Artificial Intelligence: A Review
Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2023) · ACM Computing Surveys · doi:10.1145/3491209 · Cited by 709 · see citing works
A Survey of Methods for Explaining Black Box Models
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2019) · ACM Computing Surveys · doi:10.1145/3236009 · Cited by 629 · see citing works
Artificial intelligence in business: State of the art and future research agenda
Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. P. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.001 · Cited by 621 · see citing works
Trustworthy artificial intelligence
Thiebes, S., Lins, S., & Sunyaev, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00441-4 · Cited by 595 · see citing works
Governance of artificial intelligence
Taeihagh, A. (2021) · Policy and Society · doi:10.1080/14494035.2021.1928377 · Cited by 562 · see citing works
Translating Principles into Practices of Digital Ethics: Five Risks of Being Unethical
Floridi, L. (2019) · Philosophy & Technology · doi:10.1007/s13347-019-00354-x · Cited by 524 · cited 3× · see citing works
“However, implementing this emerging technology in this fields brings social, economic, and primarily ethical implications (Benefo et al. 2022; Floridi 2019; Jia and Zhang 2021; Martin 2019).”
Artificial intelligence, systemic risks, and sustainability
Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D., et al. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101741 · Cited by 497 · see citing works
Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them
Santoni de Sio, F., & Mecacci, G. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00450-x · Cited by 490 · cited 2× · see citing works
“It is essential to avoid technical or methodological solutionism, the mis-taken belief that complex socio-technical and political problems can be solved or avoided entirely by applying new techniques (Hagendorff 2022a; Santoni de Sio and Mecacci 2021).”
Ethical framework for Artificial Intelligence and Digital technologies
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102433 · Cited by 469 · see citing works
How to Design AI for Social Good: Seven Essential Factors
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00213-5 · Cited by 430 · cited 1× · see citing works
“As a result, an ethical standard is often not met (Floridi 2019; Floridi et al. 2020).”
Defining organizational AI governance
Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00143-x · Cited by 378 · see citing works
van Noordt, C., & Misuraca, G. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2022.101714 · Cited by 356 · see citing works
Improving public services using artificial intelligence: possibilities, pitfalls, governance
Henman, P. (2020) · Asia Pacific Journal of Public Administration · doi:10.1080/23276665.2020.1816188 · Cited by 310 · see citing works
The Routledge Companion to Virtue Ethics
(2015) · Routledge eBooks · doi:10.4324/9780203071755 · Cited by 258 · see citing works
Ethics as a Service: A Pragmatic Operationalisation of AI Ethics
Morley, J., Elhalal, A., Garcia, F., Kinsey, L., Mökander, J., & Floridi, L. (2021) · Minds and Machines · doi:10.1007/s11023-021-09563-w · Cited by 257 · see citing works
Putting AI ethics to work: are the tools fit for purpose?
Ayling, J., & Chapman, A. (2022) · AI and Ethics · doi:10.1007/s43681-021-00084-x · Cited by 253 · see citing works
Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00003-6 · Cited by 240 · see citing works
Soft ethics, the governance of the digital and the General Data Protection Regulation
Floridi, L. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0081 · Cited by 234 · see citing works
Operationalising AI Ethics: Barriers, Enablers and next Steps
Morley, J., Kinsey, L., Elhalal, A., Garcia, F., Ziosi, M., & Floridi, L. (2023) · AI & SOCIETY · doi:10.1007/s00146-021-01308-8 · Cited by 215 · cited 8× · see citing works
“In addition, this abstraction “tends to encourage a public opinion that there are good and bad algorithms, rather than well designed or poorly designed algorithms” (Morley et al. 2021, p. 2).”
InfluentialAI ethics should not remain toothless! A call to bring back the teeth of ethics
Rességuier, A., & Rodrigues, R. (2020) · Big Data & Society · doi:10.1177/2053951720942541 · Cited by 214 · see citing works
AI and Big Data: A blueprint for a human rights, social and ethical impact assessment
Mantelero, A. (2018) · Computer Law and Security Review · doi:10.1016/j.clsr.2018.05.017 · Cited by 203 · see citing works
Artificial Intelligence & Human Rights: Opportunities & Risks
Raso, F., Hilligoss, H., Krishnamurthy, V., Bavitz, C., & Kim, L. Y. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3259344 · Cited by 189 · see citing works
Shafer‐Landau, R. (2007) · Cited by 179 · see citing works
Ethics from Within: Google Glass, the Collingridge Dilemma, and the Mediated Value of Privacy
Kudina, O., & Verbeek, P.-P. (2019) · Science, Technology, & Human Values · doi:10.1177/0162243918793711 · Cited by 177 · see citing works
Ethical Considerations in Artificial Intelligence Courses
Burton, E., Goldsmith, J., Koenig, S., Kuipers, B. J., Mattei, N., & Walsh, T. (2017) · AI Magazine · doi:10.1609/aimag.v38i2.2731 · Cited by 172 · see citing works
Ethics of AI: a systematic literature review of principles and challenges
Khan, A. A., Badshah, S., Liang, P., Waseem, M., Khan, B., Ahmad, A., Fahmideh, M., Niazi, M., & Akbar, M. A. (2022) · The International Conference on Evaluation and Assessment in Software Engineering 2022 · doi:10.1145/3530019.3531329 · Cited by 170 · see citing works
Lessons learned from AI ethics principles for future actions
Hickok, M. (2021) · AI and Ethics · doi:10.1007/s43681-020-00008-1 · Cited by 168 · cited 1× · see citing works
“Regulation and establishing solid ethical standards are essential to ensure that AI is used responsibly and with respect for people’s rights and well-being (Hickok 2021).”
A Virtue-Based Framework to Support Putting AI Ethics into Practice
Hagendorff, T. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00553-z · Cited by 149 · see citing works
Bogina, V., Hartman, A., Kuflik, T., & Shulner-Tal, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00248-0 · Cited by 142 · see citing works
Kroll, J. A. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0084 · Cited by 132 · see citing works
The Landscape and Gaps in Open Source Fairness Toolkits
Lee, M. S. A., & Singh, J. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445261 · Cited by 129 · see citing works
FairSight: Visual Analytics for Fairness in Decision Making
Ahn, Y., & Lin, Y.-R. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934262 · Cited by 126 · see citing works
AI lifecycle models need to be revised
Haakman, M., Cruz, L., Huijgens, H., & Arie Van Deursen, A. (2021) · Empirical Software Engineering · doi:10.1007/s10664-021-09993-1 · Cited by 110 · see citing works
Artificial intelligence in finance
Buchanan, B. (2019) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2612536 · Cited by 109 · see citing works
Companies Committed to Responsible AI: From Principles towards Implementation and Regulation?
de Laat, P. D. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00474-3 · Cited by 107 · see citing works
Hagendorff, T. (2021) · AI and Ethics · doi:10.1007/s43681-021-00122-8 · Cited by 105 · see citing works
Tolerant Paternalism: Pro-ethical Design as a Resolution of the Dilemma of Toleration
Floridi, L. (2015) · Science and Engineering Ethics · doi:10.1007/s11948-015-9733-2 · Cited by 87 · see citing works
An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists
Krafft, P. M., Young, M., Katell, M. A., Lee, J. E., Narayan, S., Epstein, M., Dailey, D., Herman, B., Tam, A., Guetler, V., et al. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445938 · Cited by 87 · see citing works
The ICO and artificial intelligence: The role of fairness in the GDPR framework
Butterworth, M. L. (2018) · Computer Law and Security Review · doi:10.1016/j.clsr.2018.01.004 · Cited by 84 · see citing works
Franzke, A. S., Muis, I., & Schäfer, M. T. (2021) · Ethics and Information Technology · doi:10.1007/s10676-020-09577-5 · Cited by 67 · see citing works
Cummings, C. L., & Mercurio, M. R. (2010) · Pediatrics in Review · doi:10.1542/pir.31-6-252 · Cited by 46 · see citing works
Benefo, E. O., Tingler, A., White, M., Cover, J. D., Torres, L., Broussard, C., Shirmohammadi, A., Pradhan, A. K., & Patra, D. (2022) · AI and Ethics · doi:10.1007/s43681-021-00124-6 · Cited by 41 · see citing works
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Hardt, M., Chen, X., Cheng, X., Donini, M., Gelman, J., Gollaprolu, S., He, J., Larroy, P., Liu, X., McCarthy, N., et al. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467177 · Cited by 39 · see citing works
An AI ethics ‘David and Goliath’: value conflicts between large tech companies and their employees
Ryan, M., Christodoulou, E., Antoniou, J., & Iordanou, K. (2022) · AI & Society · doi:10.1007/s00146-022-01430-1 · Cited by 34 · see citing works
A Global Perspective of Soft Law Programs for the Governance of Artificial IntelligencePreprint
Gutierrez, C. I., & Marchant, G. E. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3855171 · Cited by 34 · see citing works
Christoforaki, M., & Beyan, O. (2022) · Applied Sciences · doi:10.3390/app12094130 · Cited by 33 · see citing works
Concepts of Ethics and Their Application to AI
Stahl, B. C. (2021) · SpringerBriefs in research and innovation governance · doi:10.1007/978-3-030-69978-9_3 · Cited by 32 · see citing works
Designing Up with Value-Sensitive Design: Building a Field Guide for Ethical ML Development
Boyd, K. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3534626 · Cited by 30 · see citing works
Becker, S. J., Nemat, A., Lucas, S., Heinitz, R. M., Klevesath, M., & Charton, J. (2022) · Ai & Society · doi:10.1007/s00146-021-01376-w · Cited by 27 · see citing works
Los valores éticos de la profesión sanitaria
Camps, V. (2015) · Educación Médica · doi:10.1016/j.edumed.2015.04.001 · Cited by 26 · see citing works
Addressing Ethical Issues in AI
Stahl, B. C. (2021) · SpringerBriefs in research and innovation governance · doi:10.1007/978-3-030-69978-9_5 · Cited by 23 · see citing works
Categorization and eccentricity of AI risks: a comparative study of the global AI guidelines
Jia, K., & Zhang, N. (2021) · Electronic Markets · doi:10.1007/s12525-021-00480-5 · Cited by 23 · see citing works
AI & Human Values - Inequalities, Biases, Fairness, Nudge, and Feedback Loops
Devillers, L., Fogelman-Soulié, F., & Baeza-Yates, R. (2021) · Reflections on Artificial Intelligence for Humanity · doi:10.1007/978-3-030-69128-8_6 · Cited by 18 · see citing works
The social dilemma in artificial intelligence development and why we have to solve it
Strümke, I., Slavkovik, M., & Madai, V. I. (2021) · AI and Ethics · doi:10.1007/s43681-021-00120-w · Cited by 16 · see citing works
Stiftung, B., & VDE (2020) · Bertelsmann Stiftung · doi:10.11586/2020013 · Cited by 10 · see citing works
Responsible use of AI for public policy: Project formulation manual
Hermosilla, M. P., Alarcón, N. G., Pombo, C., Ávalos, R. S., Denis, G., & Aracena, C. (2021) · doi:10.18235/0003631 · Cited by 7 · see citing works
Lane, G., Angus, A., & Murdoch, A. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.2667807 · Cited by 6 · see citing works
Namatēvs, I., Sudars, K., & Dobrājs, A. (2022) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.24423/cames.518 · Cited by 3 · see citing works
Ética de las virtudes aplicada a la deontología médica
Sganzerla, A., de Siqueira, J. E., & Guérios, T. R. (2022) · Revista Bioética · doi:10.1590/1983-80422022303541es · Cited by 1 · see citing works
Flagged first, then by citations
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Soulami, M., Benchekroun, S., & Galiulina, A. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1473872 · Cited by 101 · see citing works
Artificial intelligence tool development: what clinicians need to know?
Chew, B.-H., & Ngiam, K. Y. (2025) · BMC Medicine · doi:10.1186/s12916-025-04076-0 · Cited by 41 · see citing works
Artificial intelligence in the context of digital marketing communication
Bormane, S., & Blaus, E. (2024) · Frontiers in Communication · doi:10.3389/fcomm.2024.1411226 · Cited by 58 · see citing works
Ethical concerns of generative AI and mitigation strategies: A systematic mapping study
Huang, Y., Arora, C., Huong, W. C., Kanij, T., Madugalla, A. K., & Grundy, J. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114789 · Cited by 21 · see citing works
Kuehnert, B., Kim, R., Forlizzi, J., & Heidari, H. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732191 · Cited by 28 · see citing works
Chan, A., Rahimi‐Ardabili, H., Rogers, W., & Coiera, E. W. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf167 · Cited by 17 · see citing works
Wang, X., Yang, J., Liu, Y., Wang, Y., Wang, F., Kang, M., Tian, Y., Rudas, I. J., Li, L., Fanti, M. P., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10861-9 · Cited by 16 · see citing works
Kuai, H., Huang, J. X., Tao, X., Pasi, G., Yao, Y. Y., Liu, J., & Zhong, N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11203-z · Cited by 11 · see citing works
Rules of engagement: ethical issues and value chain introspection in Artificial Intelligence systems
Reyes, J. A. P., & Rajagopal, A. (2024) · Quality & Quantity · doi:10.1007/s11135-024-01990-y · Cited by 9 · see citing works
Learning about AI ethics from cases: a scoping review of AI incident repositories and cases
Knight, S., McGrath, C., Viberg, O., & Pargman, T. C. (2025) · AI and Ethics · doi:10.1007/s43681-024-00639-8 · Cited by 9 · see citing works
Multi-agent collaborative pathways for Chinese traditional architectural image generation
Lu, Y., Yuan, W., Wang, M., Wang, P., Wu, S., Wu, J., Xing, W., Xie, W., & Yu., F. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-18130-7 · Cited by 7 · see citing works
The Challenges of Agentic AI Safety
Watson, N. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198940272.013.0006 · Cited by 6 · see citing works
Towards a Responsible AI Organizational Maturity Model
Heger, A., Passi, S., Dhanorkar, S., Kahn, Z., Wang, R., & Vorvoreanu, M. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757514 · Cited by 6 · see citing works
Decoding AI ethics: What do ethical tools tell us about ethics?
Portegies, T., Hajri, O., Willemsen, L., & Harbers, M. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09860-3 · Cited by 5 · see citing works
Shormani, M. Q. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11332-5 · Cited by 5 · see citing works
Abe, K., Quan, C., Cao, S., & Luo, Z. (2025) · Applied Sciences · doi:10.3390/app15073466 · Cited by 4 · see citing works
People and Management Debt in ML-Integrated Software Projects: Structuring Industry Insights
Akman, P. D., Özcan‐Top, Ö., & Temizel, T. T. (2025) · IEEE Access · doi:10.1109/access.2025.3595609 · Cited by 3 · see citing works
Addressing Bias and Ensuring Fairness in AI Systems for Healthcare
Zarnegar, A. (2025) · Health Informatics and Knowledge Management Conference (HIKM) · doi:10.1145/3774816.3774817 · Cited by 3 · see citing works
AI and the Rule of Law in Urban Security
Mansoor, S. I. U. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0245-4.ch003 · Cited by 4 · see citing works
Mansoor, S. I. U., & Wani, S. A. (2025) · Advances in social networking and online communities book series · doi:10.4018/979-8-3693-9904-0.ch003 · Cited by 3 · see citing works
Artificial Intelligence Risk Identification: Challenges, Impacts, and Mitigation Strategies
Syukrina, U., & Nugraha, I. G. D. (2025) · International Journal of Electrical, Computer, and Biomedical Engineering · doi:10.62146/ijecbe.v3i2.109 · Cited by 2 · see citing works
Proposing a Secure Framework for Industrial AI Security in 21 st Century
Gudlur, V. V. R. (2025) · IEEE International Conference on Robotics and Technologies for Industrial Automation (ROBOTHIA) · doi:10.1109/robothia63806.2025.10986430 · Cited by 2 · see citing works
From consultors to collaborators – An SOP for advancing ethics engagement in science
Paleri, V. A., & Hens, K. (2025) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2025.06.006 · Cited by 2 · see citing works
Customer experience and chatbots disruption: current landscape and research needs
Farjas, C., Herrando, C., & Melero‐Polo, I. (2025) · Service Industries Journal · doi:10.1080/02642069.2025.2575165 · Cited by 3 · see citing works
Enabling ethics mechanisms in the governance of algorithmic artificial persons (ALAP)
Poti, S. P., Stanton, C. J., & Stevens, C. J. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100143 · Cited by 2 · see citing works
Competências digitais discentes para o uso de inteligência artificial no ensino superior a distância
Pinto, A. D. S., Longhi, M. T., & Behar, P. A. (2025) · RDBCI Revista Digital de Biblioteconomia e Ciência da Informação · doi:10.20396/rdbci.v24i00.8679434 · Cited by 2 · see citing works
Albulayhi, O., & Alkhalifah, A. (2026) · Sustainability · doi:10.3390/su18062986 · Cited by 2 · see citing works
Bernal-Salcedo, J., Álvarez, C. V., Tabares, M. T., Rendón, S. M., Gonzáles-Martínez, G., & Castaño-Ramírez, O. M. (2025) · Current Psychology · doi:10.1007/s12144-025-07373-2 · Cited by 2 · see citing works
Self-citationGovernance and Ethics in the Use of Artificial Intelligence in Health
da Costa, P. G., Machado, A. D. B., Willerding, I. A. V., & Lápolli, É. M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7630-0.ch015 · Cited by 2 · see citing works
FastUGI-Net: Enhanced Real-Time Endoscopic Diagnosis with Efficient Multi-task Learning
Chan, I. N., Wong, P. K., Yan, T., Hu, Y., Chan, C. I., Ge, P., Li, Z., Hu, Y., Gao, S., & Yu, H. H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127444 · Cited by 2 · see citing works
Viganò, E., Hauser, C., Cacciatori, E., Ferrario, A., & Sedláková, J. (2025) · IEEE Swiss Conference on Data Science (SDS) · doi:10.1109/sds66131.2025.00023 · Cited by 1 · see citing works
Strategien für die KI-Implementierung: Fachliche und ethische Grundlagen
Plafky, C. S., & Badertscher, H. (2025) · Springer eBooks · doi:10.1007/978-3-658-49009-6_11 · Cited by 1 · see citing works
The Ethical Concerns of Human–Computer Interface in Assistive Systems: A Systematic Survey
Ejaz, S., Ahmad, J., Shafi, I., Rehman, H. M. R. U., & Ashraf, I. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2659302 · Cited by 1 · see citing works
How ethics operationalization breaks down in practice: expert perspectives from healthcare AI
Minocher, R., Velarde, I., Buerger, V. L., Pluktaite, G., & Madai, V. (2026) · AI and Ethics · doi:10.1007/s43681-026-01381-z · Cited by 1 · see citing works
How AI Influences Marketing From the Consumer Perspective
Azevedo, F. G., Oliveira, F. S. S., Thielen, K., & Imamović, I. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-5340-0.ch002 · Cited by 1 · see citing works
DisMed-LLM: De-Identifying Spanish Medical Text with Large Language Models
Alzate-Grisales, J. A., Bernal-Salcedo, J., Saborit-Torres, J. M., Serrano, J. M., Fons, M. P., Garcia‐García, F., Tabares-Soto, R., & de la Iglesia-Vayá, M. (2026) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-026-00256-6 · Cited by 1 · see citing works
Self-citationAdamiczka, B. (2025) · Architectus · doi:10.37190/arc250110 · Cited by 1 · see citing works
Dragomir, A. N., Bulea, I., & Tarnu, L. I. (2026) · Frontiers in Political Science · doi:10.3389/fpos.2026.1928481 · Cited by 1 · see citing works
Kandukuri, N. R., Yadla, M., Butool, S., Shruthi, T., Hasan, N., & Nikitha, V. (2024) · doi:10.1109/icstem61137.2024.10560553
Kuriakose, S., & Monga, Y. (2024) · Springer eBooks · doi:10.1007/978-3-031-69433-2_11
Abe, K., Quan, C., Cao, S., & Luo, Z. (2025) · Applied Sciences · doi:10.3390/app15095092
Ethical Considerations in AI-Enabled Services
Gupta, A., Verma, D. C., & Mangla, U. (2025) · Progress in IS · doi:10.1007/978-3-031-86958-7_1
Adamiczka, B. (2025) · Biblioteka Regionalisty · doi:10.15611/br.2025.1.01
Tu, J., & Tang, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-02534-0_11
A Review of Privacy Protection in the News Industry Driven by Artificial Intelligence Technologies
Gao, J., Sannusi, S. N., Aziz, J., & Liang, Q. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06326-7_3
Von der Entwicklung zur Praxis: KI-gestützte Systeme in der Sozialen Arbeit einführen
Plafky, C. S., & Badertscher, H. (2025) · Springer eBooks · doi:10.1007/978-3-658-49009-6_9
Plafky, C. S., & Badertscher, H. (2025) · Springer eBooks · doi:10.1007/978-3-658-49009-6_8
Romani, G., Avendaño, C., & Santisteban, J. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1694979
Schlicht, L. (2025) · TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis · doi:10.14512/tatup.7226
Evaluation of AI ethics tools in language models: a developers' perspective case study
Silva, J., Moreira, D. A. B., dos Santos, G. D., Ferreira, A. I., Maia, H. A., Avila, S., & Pedrini, H. (2025) · AI and Ethics · doi:10.1007/s43681-025-00914-2
Building AI-Ready DevOps Platforms: Operationalizing SageMaker and Bedrock in a Multi-Tenant Cloud
Bobbala, S. R. (2025) · International Conference on Electronic Circuits and Signaling Technologies (ICECST) · doi:10.1109/icecst66106.2025.11307479
Castro-Toledo, F. J., & Gómez-Bellvís, A. B. (2026) · Crime Law and Social Change · doi:10.1007/s10611-025-10251-4
Against AI ethics: challenging the conventional narratives
Afroogh, S., Pouresmaeil, Y., & Dhurandhar, A. (2026) · AI and Ethics · doi:10.1007/s43681-025-00978-0
Relevance of Spirituality to AI Ethics in BANI Times
Kanuri, R. R. (2026) · Responsible leadership and sustainable management · doi:10.1007/978-981-95-5929-9_13
KindMind: Implementing Politeness and Fairness in a Conversational Tutoring Robot
Chander, Y., Yusof, R. J. R., Lee, K. K., Chen, X., Li, L., Sheng, M., & Kaptelinin, V. (2025) · International Conference on Education Technology and Computer · doi:10.1109/icetc66579.2025.11387550
The transformative nature of AI on academic scholarship
Wright, S. (2025) · Journal of English for Research Publication Purposes · doi:10.1075/jerpp.00032.wri
Research Redefined AI Tools, Trends, and Innovations in the Modern Era
Urazmetov, M., Samanova, S. B., Sultanov, S. X., & Ahmad, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0434-0.ch005
Navigating the AI Landscape: Ethics, Governance and Responsible AI
Kaurav, R. P. S., & Koul, S. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83708-888-120261010
Mascareño, A., & Gardella, N. (2026) · Springer eBooks · doi:10.1007/978-3-031-70581-6_39-1
Consumer Data Privacy on User-Created Content – Designing Consumer Personas to Privacy Concern
Jagani, K., & Vikram, S. (2026) · Springer eBooks · doi:10.1007/978-981-95-6509-2_11
Ethical Principles Application in the Development of AI Systems: a Systematic Mapping Study
Martins, H. C., Lana, C. A., & Villela, M. L. B. (2026) · Conferência Latino-Americana de Interação Humano-Computador (CLIHC) · doi:10.5753/clihc.2026.20902
Ethical Challenges and AI Governance in Social Work
Huang, J., Yang, F., & Lee, J. (2026) · Springer eBooks · doi:10.1007/978-3-032-18443-6_22
HTA and Ethical Impact Assessment―Preliminary Perspectives
Hakula, J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28812-7_14
An Enhancement Alternative to the Principled Approach to AI Ethics
Ye, B. (2026) · Philosophy of engineering and technology · doi:10.1007/978-981-95-8023-1_4
Yang, X., Ma, L., Zheng, S., & Ma, Y. (2026) · SAGE Open · doi:10.1177/21582440261462644 · Cited by 1 · see citing works
A multi-layer social-theoretical framework for AI ethics
Fakrudeen, M., & Otieno, J. O. (2026) · AI and Ethics · doi:10.1007/s43681-026-01307-9
Artificial intelligence integration in livestock genomics
Hossein‐Zadeh, N. G. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45663-3.00012-9
Alhalawany, R. M., Khatatbeh, Y. M., & Jawkhab, A. A. (2026) · Healthcare · doi:10.3390/healthcare14172721
Leading With CARE in the Age of AI
Johnson, T., & Blake, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7709-4.ch003
El cerebro que aprende en la era de la IA
Ilaquize, D. K. M., Barcia, G. E. B., Ortiz, S. D. C. V., & Viteri, J. A. M. (2026) · Revista Multidisciplinar Ciencia y Descubrimiento · doi:10.63816/4gj0vw70
Ahmed, S. S., Jasim, N. S. A.-A. L., & Staikutsa, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-39720-1_15
Ethics and Fairness in Machine Learning: Equity Guidelines for Brazilian Public Policies.
Barros, V. J. M., Souza, G. B., Fonseca, B., Baía, D. D. M., & Vieira, T. (2026) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2026.6602
Leveraging Internal Audits as the Lever for Scoping and Aligning High-Impact Information Products
Marumolwa, L., & Maota, T. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-0090-8.ch003
Jachak, S., Vispute, P., Sonar, S., Bhalerao, R., Rode, M., Shaikh, M., & Suryawanshi, M. (2026) · Next research. · doi:10.1016/j.nexres.2026.102569
Responsible Artificial Intelligence: A Systematic Mapping of Ethical Principles and Guidelines
Arias-Chávez, D., Postigo-Zumarán, J. E., Ramos-Quispe, T., Núñez, A. C., Acra-Despradel, C., & Ponce-Aranibar, M. D. P. (2024) · Congreso Internacional de Tendencias en Innovación Educativa (CITIE) · doi:10.1109/citie64175.2024.11733212
Legal Challenges of Artificial Intelligence in Global Health Policy
Abuseridze, G., Kikilashvili, G., & Khasia, G. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-4881-8.ch007
- Authors
- Ortega-Bolaños, R., Bernal-Salcedo, J., Germán Ortiz, M., Galeano Sarmiento, J., Ruz, G. A., & Tabares-Soto, R.
- Affiliations
- Universidad Autonoma de Manizales (CO)
- Adolfo Ibáñez University (CL)
- Universidad de Caldas (CO)
- Corresponding author
- Ortega-Bolaños, Ricardo
- Venue
- Artificial Intelligence Review
- Published
- 2024-04-05
- Volume
- 57
- Issue
- 5
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W4393928775
- Cited by
- 76
- References
- 82
- Citations by year
- 2026: 29 · 2025: 39 · 2024: 8
- FWCI
- 5.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Artificial Intelligence Review (publishedVersion, cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- AI ethics, ethical AI development, AI governance, AI lifecycle, ethical principles, responsible AI, human rights, privacy, equity
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