2020
Klenk, Michael; Duijf, Hein
Ethics of Digital Contact Tracing and COVID-19: Who Is (Not) Free to Go? Miscellaneous
2020.
Abstract | Links | BibTeX | Tags: active responsibility, COVID-19, digital contact tracing, digital ethics, fairness, SARS-CoV-2, value:health, value:justice
@misc{Klenk2020,
title = {Ethics of Digital Contact Tracing and COVID-19: Who Is (Not) Free to Go?},
author = {Michael Klenk and Hein Duijf},
editor = {J. van den Hoven M. J. Dennis and Georgy Ishmaev},
url = {https://ssrn.com/abstract=3595394},
doi = {https://dx.doi.org/10.2139/ssrn.3595394},
year = {2020},
date = {2020-05-28},
urldate = {2020-05-28},
abstract = {Digital tracing technologies are heralded as an effective way of containing SARS-CoV-2 faster than it is spreading, thereby allowing the possibility of easing draconic measures of population-wide quarantine. But existing technological proposals risk addressing the wrong problem. The objective is not solely to maximise the ratio of people freed from quarantine but to also ensure that the composition of the freed group is fair. We identify several factors that pose a risk for fair group composition along with an analysis of general lessons for a philosophy of technology. Policymakers, epidemiologists, and developers can use these risk factors to benchmark proposal technologies, curb the pandemic, and keep public trust.},
keywords = {active responsibility, COVID-19, digital contact tracing, digital ethics, fairness, SARS-CoV-2, value:health, value:justice},
pubstate = {published},
tppubtype = {misc}
}
Digital tracing technologies are heralded as an effective way of containing SARS-CoV-2 faster than it is spreading, thereby allowing the possibility of easing draconic measures of population-wide quarantine. But existing technological proposals risk addressing the wrong problem. The objective is not solely to maximise the ratio of people freed from quarantine but to also ensure that the composition of the freed group is fair. We identify several factors that pose a risk for fair group composition along with an analysis of general lessons for a philosophy of technology. Policymakers, epidemiologists, and developers can use these risk factors to benchmark proposal technologies, curb the pandemic, and keep public trust.
2018
Bennati, Stefano; Dusparic, Ivana; Shinde, Rhythima; Jonker, Catholijn M
Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures Journal Article
In: Sensors, vol. 18, no. 11, 2018, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: artificial intelligence, big data, fairness, participatory sensing, privacy, public good, smart cities, value:justice
@article{s18113707,
title = {Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures},
author = {Stefano Bennati and Ivana Dusparic and Rhythima Shinde and Catholijn M Jonker},
url = {http://www.mdpi.com/1424-8220/18/11/3707},
doi = {10.3390/s18113707},
issn = {1424-8220},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Sensors},
volume = {18},
number = {11},
abstract = {Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.},
keywords = {artificial intelligence, big data, fairness, participatory sensing, privacy, public good, smart cities, value:justice},
pubstate = {published},
tppubtype = {article}
}
Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.