【GDPR】支持GDPR合规决策的人工智能框架:第六部分

1. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), https://eur-lex.europa.eu/eli/reg/2016/679/oj

2. Norms contained in the Italian Personal Data Protection Code (Legislative Decree 196/2003) were aligned with provisions introduced by GDPR with the legislative decree n. 101/2018 published in the Official Gazette n. 205 on September 4, 2018.

3. https://www.dataguidance.com/news/italy-garante-fines-trento-health-authority-150000

4. https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelligence-feb2020_en.pd (last access: 2021/10/13)

5. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206 (last access: 2021/10/13)

6. We used doccano as the platform for the annotation: https://github.com/doccano/doccano.

7. Legal references were extracted by the Linkoln tool https://gitlab.com/IGSG/LINKOLN/linkoln.

8. https://tika.apache.org/

9. https://github.com/tesseract-ocr/tesseract

10. https://ufal.mff.cuni.cz/conll2009-st/task-description.html

11. https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)

12. https://spacy.io/

13. https://tika.apache.org/

14. https://spacy.io/

References

• Adams, A., Aili, E., Aioanei, D., Jonson, R., Mickelsson, L., Mikmekova, D., Roberts, F., Mikmekova, D., Fernandez Valencia, J., & Wechsler, R. (2019). Anonymate: a toolkit for anonymizing unstructured chat data. In Proceedings of the workshop on NLP and pseudonymisation, pp. 1–7. Finland: Linköping Electronic Press, Turku.

• Al-Abdulkarim, L., Atkinson, K., & Bench-Capon, T. (2016). A methodology for designing systems to reason with legal cases using abstract dialectical frameworks. Artificial Intelligence and Law, 24, 1–49. https://doi.org/10.1007/s10506-016-9178-1.

  Article Google Scholar 

• Attardi, G., Basile, V., Bosco, C., Caselli, T., Dell’Orletta, F., Montemagni, S., Patti, V., Simi, M., & Sprugnoli, R. (2015). State of the art language technologies for italian: the EVALITA 2014 perspective. Intelligenza Artificiale, 9(1), 43–61. https://doi.org/10.3233/IA-150076.

  Article Google Scholar 

• Bansal, A., & Kaur, S. (2018). Extreme gradient boosting based tuning for classification in intrusion detection systems. In M. Singh, P. K. Gupta, V. Tyagi, J. Flusser, & T. Ören (Eds.) Advances in computing and data sciences, communications in computer and information science, (vol. 905 pp. 372–380). https://doi.org/10.1007/978-981-13-1810-8_37. Singapore: Springer.

• Biesner, D., Ramamurthy, R., Stenzel, R., Lu̇bbering, M., Hillebrand, L. P., Ladi, A., Pielka, M., Loitz, R., Bauckhage, C., & Sifa, R. (2022). Anonymization of german financial documents using neural network-based language models with contextual word representations. International Journal of Data Science and Analytics, 13(2), 151–161. https://doi.org/10.1007/s41060-021-00285-x.

  Article Google Scholar 

• Blume, P. (2016). Impact of the EU general data protection regulation on the public sector. Journal of Data Protection & Privacy, 1(1), 53–63.

  Google Scholar 

• Brandsen, A., Verberne, S., Wansleeben, M., & Lambers, K. (2020). Creating a dataset for named entity recognition in the archaeology domain. In Proceedings of the 12th Language Resources and Evaluation Conference, LREC 2020, pp. 4573–4577. European Language Resources Association (ELRA).

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324 .

  Article MATH Google Scholar 

• Chen, T., & Guestrin, C. (2016). Xgboost: a scalable tree boosting system. In B. Krishnapuram, M. Shah, A. J. Smola, C.C. Aggarwal, D. Shen, & R. Rastogi (Eds.) Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785–794. Association for Computing Machinery (ACM). https://doi.org/10.1145/2939672.2939785.

• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37–46.

  Article Google Scholar 

• Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmȧn, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised cross-lingual representation learning at scale. In D. Jurafsky, J. Chai, N. Schluter, & J.R. Tetreault (Eds.) Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, pp. 8440–8451. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.747.

• Contissa, G., Docter, K., Lagioia, F., Lippi, M., Micklitz, H. W., Palka, P., Sartor, G., & Torroni, P. (2018). CLAUDETTE meets gdpr: automating the evaluation of privacy policies using artificial intelligence. SSRN Electronic Journal, 1–59.

• Csányi, G. M., Nagy, D., Vági, R., Vadász, J. P., & Orosz, T. (2021). Challenges and open problems of legal document anonymization. Symmetry, 13(8).

• Dadgostari, F., Guim, M., Beling, P. A., Livermore, M. A., & Rockmore, D. N. (2020). Modeling law search as prediction. Artificial Intelligence and Law, 29, 3–34. https://doi.org/10.1007/s10506-020-09261-5.

  Article Google Scholar 

• Datta, P. (2020). Digital transformation of the italian public administration: a case study. Communications of the Association for Information Systems pp. 252–272. https://doi.org/10.17705/1CAIS.04611.

• Davari, M., & Bertino, E. (2019). Access control model extensions to support data privacy protection based on GDPR. In C. Baru, J. Huan, L. Khan, X. Hu, R. Ak, Y. Tian, R. S. Barga, C. Zaniolo, K. Lee, & Y.F. Ye (Eds.) Proceedings of the 2019 IEEE international conference on big data, big data 2019, pp. 4017–4024. IEEE. https://doi.org/10.1109/BigData47090.2019.9006455.

• De Felice, I., Dell’Orletta, F., Venturi, G., Lenci, A., & Montemagni, S. (2018). Italian in the trenches: linguistic annotation and analysis of texts of the great war. In E. Cabrio, A. Mazzei, & F. Tamburini (Eds.) Proceedings of the 5th italian conference on computational linguistics, CLiC-it 2018, CEUR Workshop Proceedings, (vol. 2253 pp. 1–5).

• De Martino, G., Pio, G., & Ceci, M. (2022). PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments. Artificial Intelligence and Law, 30, 359–390. https://doi.org/10.1007/s10506-021-09297-1.

  Article Google Scholar 

• Di Cerbo, F., & Trabelsi, S. (2018). Towards personal data identification and anonymization using machine learning techniques. In A. Benczúr, B. Thalheim, T. Horváth, S. Chiusano, T. Cerquitelli, C. Sidló, & P. Z. Revesz (Eds.) New trends in databases and information systems, ADBIS 2018, communications in computer and information science, pp. 118–126. https://doi.org/10.1007/978-3-030-00063-9_13. Cham: Springer.

• Di Nicola, P., Grossi, P., & Preti, A. (2016). Rethinking the organization of public administration through the enhancement of human resources. The Istat case. RIEDS-Rivista Italiana di Economia, Demografia e Statistica- The Italian Journal of Economic. Demographic and Statistical Studies, 70(1), 17–28.

  Google Scholar 

• Dias, M., Bone, J., Ferreira, J., Ribeiro, R., & Maia, R. (2020). Named entity recognition for sensitive data discovery in portuguese. Applied Sciences, 10, 2303. https://doi.org/10.3390/app10072303.

  Article Google Scholar 

• Francopoulo, G., & Schaub, L. P. (2020). Anonymization for the GDPR in the context of citizen and customer relationship management and NLP. In Proceedings of the of the workshop on legal and ethical issues (Legal2020), pp. 9–14. European Language Resources Association (ELRA).

• Ghosh, M., Raihan, M. M., Raihan, M., Akter, L., Bairagi, A., Alshamrani, S., & Masud, M. (2021). A comparative analysis of machine learning algorithms to predict liver disease. Intelligent Automation and Soft Computing, 29, 917–928. https://doi.org/10.32604/iasc.2021.017989.

  Article Google Scholar 

• Grouin, C., Rosset, S., Zweigenbaum, P., Fort, K., Galibert, O., & Quintard, L. (2011). Proposal for an extension of traditional named entitites: from guidelines to evaluation, an overview. In Proceedings of the 5th linguistics annotation workshop (The LAW V), pp. 92–100. USA: Association for Computational Linguistics, Portland, Oregon.

• Harkous, H., Fawaz, K., Lebret, R., Schaub, F., Shin, K. G., & Aberer, K. (2018). Polisis: automated analysis and presentation of privacy policies using deep learning. In Proceedings of the 27th USENIX conference on security symposium, SEC’18 (pp. 531–548). USA: USENIX Association.

• Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning. Springer Series in Statistics. New York: Springer. https://doi.org/10.1007/978-0-387-84858-7.

  MATH Google Scholar 

• Hoofnagle, C. J., van der Sloot, B., & Borgesius, F. Z. (2019). The European Union general data protection regulation: what it is and what it means. Information & Communications Technology Law, 28(1), 65–98. https://doi.org/10.1080/13600834.2019.1573501.

  Article Google Scholar 

• Hripcsak, G., & Rothschild, A. S. (2005). Agreement, the F-measure, and reliability in information retrieval. Journal of the American Medical Informatics Association, 12(3), 296–298. https://doi.org/10.1197/jamia.M1733.

  Article Google Scholar 

• Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In C. Nédellec C. Rouveirol (Eds.) Proceedings of 10th european conference on machine learning: ECML-98, lecture notes in computer science, (vol. 1398 pp. 137–142). Berlin, Heidelberg: Springer. https://doi.org/10.1007/BFb0026683.

• Kingston, J. (2017). Using artificial intelligence to support compliance with the general data protection regulation. Artificial Intelligence and Law, 25, 429–443. https://doi.org/10.1007/s10506-017-9206-9.

  Article Google Scholar 

• Magnini, B., Pianta, E., Girardi, C., Negri, M., Romano, L., Speranza, M., Bartalesi Lenzi, V., & Sprugnoli, R. (2006). I-CAB: the italian content annotation bank. In Proceedings of the 5th international conference on language resources and evaluation (LREC ’06), pp. 963–968. Italy: European Language Resources Association (ELRA), Genoa.

• Mc Cullagh, K., Tambou, O., & Bourton, S. (eds.) (2019). National adaptations of the GDPR, 1st edn. Blogdroiteuropéen: Collection Open Access Book.

• Meszaros, J., & Ho, C. (2021). AI research and data protection: can the same rules apply for commercial and academic research under the GDPR? Computer Law & Security Review, 105532, 41. https://doi.org/10.1016/j.clsr.2021.105532.

  Article Google Scholar 

• Mozes, M., & Kleinberg, B. (2021). No intruder, no validity : evaluation criteria for privacy-preserving text anonymization . Preprint at arXiv:2103.09263.

• Nothman, J., Ringland, N., Radford, W., Murphy, T., & Curran, J. R. (2013). Learning multilingual named entity recognition from wikipedia. Artificial Intelligence, 194, 151–175. https://doi.org/10.1016/j.artint.2012.03.006.

  Article MathSciNet MATH Google Scholar 

• Palmero Aprosio, A., & Moretti, G. (2018). Tint 2.0: an all-inclusive suite for NLP in italian. In Proceedings of the 5th italian conference on computational linguistics, CLiC-it 2018, CEUR workshop proceedings, (vol. 2253, pp. 1–7).

• Passaro, L. C., Lenci, A., & Gabbolini, A. (2017). Informed PA: a NER for the italian public administration domain. In R. Basili, M. Nissim, & G. Satta (Eds.) Proceedings of the 4th italian conference on computational linguistics, CLiC-it 2017, CEUR Workshop Proceedings, Vol. 2006.

• Ricci, A. (2018). E-government, transparency and personal data protection.: a new analysis’ approach to an old juridical issue. Central and Eastern European eDem and eGov Days, 325, 125–135. https://doi.org/10.24989/ocg.v325.11.

  Article Google Scholar 

• Romano, M. F., Baldassarini, A., & Pavone, P. (2020). Text mining of public administration documents: preliminary results on judgments. In D. F. Iezzi, D. Mayaffre, & M. Misuraca (Eds.) Text analytics: advances and challenges. proceedings of the 14th international conference on the statistical analysis of textual data (JADT 2018), studies in classification, data analysis, and knowledge organization, pp. 117–126. Cham: Springer. https://doi.org/10.1007/978-3-030-52680-1_10.

• Sartor, G., & Lagioia, F. (2020). The impact of the General Data Protection Regulation (GDPR) on artificial intelligence. European Parliamentary Research Service. https://doi.org/10.2861/293.

• Savic, D., & Veinovic, M. (2018). Challenges of general data protection regulation (GDPR). In Proceeding of the 5th international scientific conference on information technology and data related research, sinteza 2018, pp. 23–30. Serbia: Singidunum University, Belgrade. https://doi.org/10.15308/Sinteza-2018-23-30.

• Selbst, A. D., & Powles, J. (2017). Meaningful information and the right to explanation. International Data Privacy Law, 7(4), 233–242. https://doi.org/10.1093/idpl/ipx022.

  Article Google Scholar 

• Silva, P., Gonçalves, C., Godinho, C., Antunes, N., & Curado, M. (2020). Using natural language processing to detect privacy violations in online contracts. In Proceedings of the 35th annual ACM symposium on applied computing, SAC 2020, pp. 1305–1307. New York: Association for Computing Machinery (ACM), DOI 10.1145/3341105.3375774, (to appear in print).

• Sovrano, F., Vitali, F., & Palmirani, M. (2020). Modelling GDPR-compliant explanations for trustworthy ai. In A. Kȯ, E. Francesconi, G. Kotsis, A. M. Tjoa, & I. Khalil (Eds.) Electronic Government and the Information Systems Perspective. Proceedings of the 9th international conference on electronic government and the information systems perspective, EGOVIS 2020, lecture notes in computer science, (vol. 12394 pp. 219–233). Cham: Springer. https://doi.org/10.1007/978-3-030-58957-8_16.

• Stamova, I., & Draganov, M. (2020). Artificial intelligence in the digital age. In Proceedings of the international scientific conference “digital transformation on manufacturing, infrastructure and service”, IOP conference series: materials science and engineering, vol. 940. https://doi.org/10.1088/1757-899X/940/1/012067.

• Sánchez, D., Viejo, A., & Batet, M. (2021). Automatic assessment of privacy policies under the GDPR. Applied Sciences 11(4). https://doi.org/10.3390/app11041762.

• Tagarelli, A., & Simeri, A. (2021). Unsupervised law article mining based on deep pre-trained language representation models with application to the italian civil code. Artificial Intelligence and Law, 30, 417–473. https://doi.org/10.1007/s10506-021-09301-8.

  Article Google Scholar 

• van der Aalst, W. M. P. (2016). Process Mining- Data Science in Action, 2nd edn. Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-662-49851-4.

  Book Google Scholar 

• van Engers, T. M. (2005). Legal engineering: a structural approach to improving legal quality. In A. Macintosh, R. Ellis, & T. Allen (Eds.) Proceedings of the 25th SGAI international conference on innovative techniques and applications of artificial intelligence, AI-2005. https://doi.org/10.1007/1-84628-224-1_1 (pp. 3–10). London: Springer.

• Yadav, V., & Bethard, S. (2019). A survey on recent advances in named entity recognition from deep learning models. Preprint at arxiv:1910.11470.

• Zaman, R., Cuzzocrea, A., & Hassani, M. (2019). An innovative online process mining framework for supporting incremental GDPR compliance of business processes. In C. Baru, J. Huan, L. Khan, X. Hu, R. Ak, Y. Tian, R.S. Barga, C. Zaniolo, K. Lee, & Y.F. Ye (Eds.) Proceedings of the 2019 IEEE international conference on big data, big data 2019, pp. 2982–2991. https://doi.org/10.1109/BigData47090.2019.9005705.

• Zaman, R., & Hassani, M. (2020). On enabling GDPR compliance in business processes through data-driven solutions. SN Computer Science, 1(4), 210. https://doi.org/10.1007/s42979-020-00215-x.

  Article Google Scholar 

Download references

Acknowledgements

We acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU, as well as the PON “Governance e capacità istituzionale” 2014–2020 project “Modelli, Sistemi e Competenze per l’implementazione dell’Ufficio per il Processo/Start UPP” (CUP: H29J22000390006), funded by the Italian Ministry for Universities and Research (MIUR).