Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms

Eleonora Bonel, Luca Nannini, Davide Bassi, Michele Joshua Maggini
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:4329-4344, 2024.

Abstract

While machine learning shows promise in automated knowledge generation, current techniques such as large language models and micro-targeted influence operations can be exploited for harmful purposes like the proliferation of disinformation. The European Union’s Digital Services Act (DSA) is an exemplary policy response addressing these harms generated by online platforms. In this regard, it necessitates a comprehensive evaluation of its impact on curbing the harmful downstream effects of these opaque practices. Despite their harmful applications, we argue that machine learning techniques offer immense, yet under-exploited, potential for unraveling the impacts of regulations like the DSA. Following an analysis that reveals possible limitations in the DSA’s provisions, we call for resolute efforts to address methodological barriers around appropriate data access, isolating marginal regulatory effects, and facilitating generalization across different contexts. Given the identified advantages of data-driven approaches to regulatory delivery, we advocate for machine learning research to help quantify the policy impacts on online harms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-bonel24a, title = {Position: Machine Learning-powered Assessments of the {EU} Digital Services Act Aid Quantify Policy Impacts on Online Harms}, author = {Bonel, Eleonora and Nannini, Luca and Bassi, Davide and Maggini, Michele Joshua}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {4329--4344}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/bonel24a/bonel24a.pdf}, url = {https://proceedings.mlr.press/v235/bonel24a.html}, abstract = {While machine learning shows promise in automated knowledge generation, current techniques such as large language models and micro-targeted influence operations can be exploited for harmful purposes like the proliferation of disinformation. The European Union’s Digital Services Act (DSA) is an exemplary policy response addressing these harms generated by online platforms. In this regard, it necessitates a comprehensive evaluation of its impact on curbing the harmful downstream effects of these opaque practices. Despite their harmful applications, we argue that machine learning techniques offer immense, yet under-exploited, potential for unraveling the impacts of regulations like the DSA. Following an analysis that reveals possible limitations in the DSA’s provisions, we call for resolute efforts to address methodological barriers around appropriate data access, isolating marginal regulatory effects, and facilitating generalization across different contexts. Given the identified advantages of data-driven approaches to regulatory delivery, we advocate for machine learning research to help quantify the policy impacts on online harms.} }
Endnote
%0 Conference Paper %T Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms %A Eleonora Bonel %A Luca Nannini %A Davide Bassi %A Michele Joshua Maggini %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-bonel24a %I PMLR %P 4329--4344 %U https://proceedings.mlr.press/v235/bonel24a.html %V 235 %X While machine learning shows promise in automated knowledge generation, current techniques such as large language models and micro-targeted influence operations can be exploited for harmful purposes like the proliferation of disinformation. The European Union’s Digital Services Act (DSA) is an exemplary policy response addressing these harms generated by online platforms. In this regard, it necessitates a comprehensive evaluation of its impact on curbing the harmful downstream effects of these opaque practices. Despite their harmful applications, we argue that machine learning techniques offer immense, yet under-exploited, potential for unraveling the impacts of regulations like the DSA. Following an analysis that reveals possible limitations in the DSA’s provisions, we call for resolute efforts to address methodological barriers around appropriate data access, isolating marginal regulatory effects, and facilitating generalization across different contexts. Given the identified advantages of data-driven approaches to regulatory delivery, we advocate for machine learning research to help quantify the policy impacts on online harms.
APA
Bonel, E., Nannini, L., Bassi, D. & Maggini, M.J.. (2024). Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:4329-4344 Available from https://proceedings.mlr.press/v235/bonel24a.html.

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