A principle‐based approach to AI: The case for European Union and Italy
Open accessAbstract
Abstract As Artificial Intelligence (AI) becomes more and more pervasive in our everyday life, new questions arise about its ethical and social impacts. Such issues concern all stakeholders involved in or committed to the design, implementation, deployment, and use of the technology. The present document addresses these preoccupations by introducing and discussing a set of practical obligations and recommendations for the development of applications and systems based on AI techniques. With this work we hope to contribute to spreading awareness on the many social challenges posed by AI and encouraging the establishment of good practices throughout the relevant social areas. As points of novelty, the paper elaborates on an integrated view that combines both human rights and ethical concepts to reap the benefits of the two approaches. Moreover, it proposes innovative recommendations, such as those on redress and governance, which add further insight to the debate. Finally, it incorporates a specific focus on the Italian Constitution, thus offering an example of how core legislations of Member States might contribute to further specify and enrich the EU normative framework on AI.
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
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?
The rationale behind responsible AI governance is to ensure that automated systems including ML/DL technologies support individuals and organizations in achieving long-term objectives while safeguarding all stakeholders' interests.
“The rationale behind responsible AI governance is to ensure that automated systems including ML/DL technologies, are supporting individuals and organizations in achieving their long terms objectives, whist safeguarding the interests of all stakeholders (Corea et al., 2022;Hickok, 2022).”
From Artificial intelligence governance: Ethical considerations and implications for social responsibility (2024) · cites this paper as Corea et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727 · 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 · see citing works
Floridi, 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 · cited 2× · see citing works
“Given its social pervasiveness and the profound impact we believe it will have on every aspect of life, AI could have significant effects on respect for human dignity (European Commission High Level Expert Group on Artificial Intelligence 2018; Floridi et al. 2018).”
BackgroundLazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160 · see citing works
Warren, S. D., & Brandeis, L. D. (1890) · University of Washington Press eBooks · doi:10.1515/9780295803661-014 · Cited by 4,092 · see citing works
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796 · 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
“…both from a technical and a social point of view: the lack of accessibility can lead to the suspicion that some correlations on which the decision is based can incorporate ethically reprehensible prejudices and cause discrimination, unfair treatment and injustice (Mittelstadt et al. 2016).”
BackgroundAutomating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Lebovits, H. (2018) · Public Integrity · doi:10.1080/10999922.2018.1511671 · Cited by 3,318 · see citing works
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294 · see citing works
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Doyle, T. (2017) · The Information Society · doi:10.1080/01972243.2017.1354593 · Cited by 3,110 · see citing works
Guidelines for Human-AI Interaction
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300233 · Cited by 2,439 · see citing works
The echo chamber effect on social media
Cinelli, M., De Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023301118 · Cited by 2,092 · cited 1× · see citing works
“The targeted re-pro-posal of similar content (so-called echo chamber), justified by the need to improve the user experience, risks compromising information pluralism (Cinelli et al. 2021).”
BackgroundAwad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018) · Nature · doi:10.1038/s41586-018-0637-6 · Cited by 1,859 · cited 1× · see citing works
“The meanings and values associated with social and ethical requirements change from time to time and from region to region (see for instance Awad et al. 2018).”
BackgroundOn the Morality of Artificial Agents
Floridi, L., & Sanders, J. W. (2004) · Minds and Machines · doi:10.1023/b:mind.0000035461.63578.9d · Cited by 1,218 · see citing works
The Fourth Revolution: How the infosphere is reshaping human reality
Floridi, L. (2014) · International Linear Collider · Cited by 1,216 · see citing works
Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
Nishant, R., Kennedy, M., & Corbett, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102104 · Cited by 1,203 · see citing works
Vaccari, C., & Chadwick, A. (2020) · Social Media + Society · doi:10.1177/2056305120903408 · Cited by 978 · cited 1× · see citing works
“…number of interactions, and, therefore, screen time and associated revenues, companies are led to adopt AI technologies aimed at amplifying divisions and exacerbating spirits, features exploited for the spread of so-called fake news (Lazer et al. 2018) and deep fakes (Vaccari and Chadwick 2020).”
BackgroundAssistive social robots in elderly care: a review
Broekens, J., Heerink, M., & Rosendal, H. (2009) · Gerontechnology · doi:10.4017/gt.2009.08.02.002.00 · Cited by 950 · see citing works
The Risk of Automation for Jobs in OECD Countries
Arntz, M., Gregory, T., & Zierahn, U. (2016) · OECD social employment and migration working papers · doi:10.1787/5jlz9h56dvq7-en · Cited by 881 · see citing works
How AI can be a force for good
Taddeo, M., & Floridi, L. (2018) · Science · doi:10.1126/science.aat5991 · Cited by 760 · see citing works
Artificial intelligence and sustainable development
Goralski, M. A., & Tan, T. (2020) · The International Journal of Management Education · doi:10.1016/j.ijme.2019.100330 · Cited by 628 · 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 · see citing works
Effect of online social networking on student academic performance
Paul, J. A., Baker, H. M., & Cochran, J. D. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2012.06.016 · Cited by 426 · see citing works
Establishing the rules for building trustworthy AI
Floridi, L. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0055-y · Cited by 394 · see citing works
A multilayer perceptron-based medical decision support system for heart disease diagnosis
YAN, H., JIANG, Y., Zheng, J. J., Peng, C., & LI, Q. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.07.022 · Cited by 327 · see citing works
Lynskey, O. (2014) · International and Comparative Law Quarterly · doi:10.1017/s0020589314000244 · Cited by 187 · see citing works
A Review on Artificial Intelligence in Special Education
Drigas, A. S., & Ioannidou, R.-E. (2013) · Communications in computer and information science · doi:10.1007/978-3-642-35879-1_46 · Cited by 139 · see citing works
Regulate artificial intelligence to avert cyber arms race
Taddeo, M., & Floridi, L. (2018) · Nature · doi:10.1038/d41586-018-04602-6 · Cited by 138 · see citing works
Big Data, Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives
Lucivero, F. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00171-7 · Cited by 136 · see citing works
Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model
Sîrbu, A., Pedreschi, D., Giannotti, F., & Kertész, J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0213246 · Cited by 112 · see citing works
The Artificial Intelligence Arms Race: Trends and World Leaders in Autonomous Weapons Development
Haner, J. K., & Garcia, D. (2019) · Global Policy · doi:10.1111/1758-5899.12713 · Cited by 93 · see citing works
Pham, Q., Madhavan, R., Righetti, L., Smart, W. D., & Chatila, R. (2018) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2018.2822058 · Cited by 67 · see citing works
Kandlhofer, M., & Steinbauer, G. (2021) · Künstliche Intell. · doi:10.1007/s13218-021-00715-9 · Cited by 46 · see citing works
Chalmers, D., Davies, G., & Monti, G. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108654173 · Cited by 46 · see citing works
Integrating Technology in Positive Psychology Practice
Villani, D., Cipresso, P., Gaggioli, A., & Riva, G. (2016) · Advances in psychology, mental health, and behavioral studies (APMHBS) book series · doi:10.4018/978-1-4666-9986-1 · Cited by 33 · see citing works
Vedder, A. (2019) · Intersentia eBooks · doi:10.1017/9781780688909.002 · Cited by 9 · see citing works
When Wearable Computing Meets Smart Cities
Neto, J. S. D. O., Silva, A. L. M., Nakano, F., Pérez-Alcázar, J. J., & Kofuji, S. T. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-7030-1.ch060 · Cited by 4 · see citing works
Palmirani, M. (2020) · Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · doi:10.4477/97021 · Cited by 3 · see citing works
Pagallo, U. (2020) · Institutional Research Information System University of Turin (University of Turin) · doi:10.4477/97022 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- Capucha L, Nunes N, Calado A (2020) Artificial intelligence as a resil- ient tool for fighting inequalities in the covid-19 crisis. Eur J Eng Form Sci 4(2):10-19
- Kharbat FF, Alshawabkeh A, Woolsey ML (2021) Identifying gaps in using artificial intelligence to support students with intellectual disabilities from education and health perspectives. Aslib J Inf Manag 73(1):101-128. https:// doi. org/ 10. 1108/ AJIM-02-2020- 0054/ full/ html · DOI
- Korinek A, Stiglitz JE (2019) Artificial intelligence and its implica- tions for income distribution and unemployment. In: Agrawal A, Gans J, Goldfarb A (eds) The economics of artificial intelligence. Communications in computer and information science. University of Chicago Press, Chicago, pp 349-390
- McGregor S (2021) Preventing repeated real world AI failures by cata- loging incidents: the AI incident database. Proc AAAI Conf Artif Intell 35:15458-15463
- Pasquale F (2015) The black box society. Harvard University Press, Harvard
- Rachels J (1975) Why privacy is important. Philos Public Aff 4(4):323-333
- IEEE (2017) Ethically aligned design: a vision for prioritizing human well-being with autonomous and intelligent systems, version 2. https:// stand ards. ieee. org/ conte nt/ dam/ ieee-stand ards/ stand ards/ web/ docum ents/ other/ ead_ v2. pdf
- Loreggia A, Mattei N, Quintarelli S (2020) Artificial intelligence research for fighting political polarisation: a research agenda. In: Proceedings of the first international forum on digital and democracy. Towards a sustainable evolution 2020, Venice, Italy, December 10-11, 2020, volume 2781 of CEUR workshop pro- ceedings, p 24-33
- Pham QC, Madhavan R, Righetti L, Smart W, Chatila R (2018) The impact of robotics and automation on working conditions and employment. In: IEEE robotics and automation magazine June, p 126-128. https:// ieeex plore. ieee. org/ docum ent/ 83854 01
- Rossi A, Ducato R, Haapio H, Passera S (2019) Legal design patterns: towards a new language for legal information design. In: 22nd international legal informatics symposium IRIS 2019. https:// cris. unibo. it/ retri eve/ handle/ 11585/ 673870/ 438421/ legal_ design_ patte rns_ new_ langu age_ legal_ info_ design. pdf SAE International (2018) J3016: taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. https:// www. sae. org/ stand ards/ conte nt/ j3016_ 201806/
Cited by
Sorted by citations
Camilleri, M. A. (2024) · Expert Systems · doi:10.1111/exsy.13406 · Cited by 331 · see citing works
- Authors
- Corea, F., Fossa, F., Loreggia, A., Quintarelli, S., & Sapienza, S.
- Affiliations
- Politecnico di Milano (IT)
- University of Brescia (IT)
- University of Bologna (IT)
- Corresponding author
- Loreggia, Andrea
- Venue
- AI & SOCIETY
- Published
- 2022-05-25
- Volume
- 38
- Issue
- 2
- Pages
- 521-535
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W4281487808
- Cited by
- 25
- References
- 40
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 9 · 2023: 4
- FWCI
- 5.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI & Society (publishedVersion, cc-by)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
- Institutional Research Information System (Università degli Studi di Brescia) (submittedVersion)
- Institutional Research Information System (Università degli Studi di Brescia) (submittedVersion, other-oa)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Funders
- Università degli Studi di Brescia
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Keywords
- AI ethics, AI governance, human rights, AI regulation, EU AI policy, Italian Constitution, redress, best practices, societal impact of AI
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
