Ethics and Politics of Algorithms

Status: concluded
Period: January 2019 – May 2021
Funding: in-kind
Funding organization: N/A
Person(s) in charge: Antonio Santangelo (Coordinator), Antonio Vetrò (Senior Researcher), Elena Beretta (Ph.D. Student)

The Nexa Center for Internet & Society has started a collaboration with the scholars and researchers of the University of Turin led by Prof. Maurizio Mori, who teaches Moral Philosophy and Bioethics. The aim is to strengthen the Nexa Center’s research line on philosophy and ethics of technology, with a particular – even if not exclusive – focus on AI.

Since 2017, with the launch of its Phd research project on data and algorithm ethics, in collaboration with Fondazione Bruno Kessler (FBK), the Nexa Center has focused its attention on the ethical consequences of the use of big data and AI.
Moreover, with SUSST, Nexa Center has decided to contribute to the opening of Politecnico of Turin to humanities, convinced of the necessity to study the social and cultural issues related to technology. At the same time, the research group on moral philosophy guided by Prof. Mori, that has been working for years on the relationship between technology and human life, especially in the field of bioethics, was looking for a partner to start a study on digital artifacts and their ethical implications.

This convergence of interests has led to draw a common research program on AI algorithms, with the aim of understanding how they should be projected to be fair. However, as fairness is a multifaceted concept which may derive from very different visions of justice and of our social life, the other aim of this collaboration is to imagine what kind of society we may give birth to, if we chose a certain criterion of fairness instead of another.
As this is a matter of politics, the objective of this part of the research is to help people understand what our democracies may become, if we let AI algorithms do something we as humans use to do, taking a partial or a total control of some of our social systems, as it is starting to happen in economy, law, medicine, etc.
Some of the topics of this research are now part of ERC project called Facets, in which Antonio Santangelo is involved at the University of Turin, where he works on deep fakes and on facial recognition.

The first results of this collaboration are three articles:
(i) Elena Beretta, Antonio Santangelo, Antonio Vetrò, Bruno Lepri, Juan Carlos De Martin. (2019) The Invisible Power of Fairness. How Machine Learning Shapes Democracy. In: Meurs MJ., Rudzicz F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science, vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_19.
(ii) Antonio Santangelo, I narcisi mascherati, gli eroi e noi, in M. Leone (eds), Volti virali, Facets digital press, 2020.
(iii) Antonio Santangelo, Equità degli algoritmi e democrazia, in DigitCult – Scientific Journal on Digital Cultures, May 2021.

@article{<LineBreak>	2318_1788795,

title = {Equità degli algoritmi e democrazia},

author = {Antonio Santangelo},

url = {https://nexa.polito.it/wp-content/uploads/2024/06/172-1-908-3-10-20210503.pdf},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {@ DIGITCULT},

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@inbook{<LineBreak>	2318_1764361,

title = {I narcisi mascherati, gli eroi e noi},

author = {Antonio Santangelo},

url = {https://nexa.polito.it/wp-content/uploads/2024/06/Articolo_Antonio_Volti-virali.pdf},

isbn = {979-12-200-6423-1},

year  = {2020},

date = {2020-01-01},

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booktitle = {Volti virali},

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@conference{<LineBreak>	11583_2735174,

title = {The Invisible Power of Fairness. How Machine Learning Shapes Democracy},

author = {E. Beretta and A. Santangelo and B. Lepri and A. Vetro and J. C. De Martin},

url = {https://www.springer.com/series/558},

doi = {10.1007/978-3-030-18305-9_19},

isbn = {978-3-030-18305-9},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {11489},

pages = {238–250},

publisher = {Springer Verlag},

abstract = {Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.},

keywords = {},

pubstate = {published},

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