@conference{Soe2020,

title = {Circumvention by design - dark patterns in cookie consents for online news outlets},

author = {Than Htut Soe and Oda Elise Nordberg and Frode Guribye and Marija Slavkovik},

url = {https://dl.acm.org/doi/pdf/10.1145/3419249.3420132},

doi = {10.1145/3419249.3420132},

year  = {2020},

date = {2020-06-24},

booktitle = {Proceedings of the 11th Nordic Conference on Human-Computer Interaction},

abstract = {To ensure that users of online services understand what data are collected and how they are used in algorithmic decision-making, the European Union's General Data Protection Regulation (GDPR) specifies informed consent as a minimal requirement. For online news outlets consent is commonly elicited through interface design elements in the form of a pop-up. We have manually analyzed 300 data collection consent notices from news outlets that are built to ensure compliance with GDPR. The analysis uncovered a variety of strategies or dark patterns that circumvent the intent of GDPR by design. We further study the presence and variety of these dark patterns in these "cookie consents" and use our observations to specify the concept of dark pattern in the context of consent elicitation.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{deHaan2019,

title = {Answer set programming for judgment aggregation},

author = {Ronald de Haan and Marija Slavkovik},

url = {https://www.ijcai.org/Proceedings/2019/0231.pdf},

year  = {2019},

date = {2019-08-10},

booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence},

pages = {1668-1674},

publisher = {International Joint Conferences on Artificial Intelligence},

abstract = {Judgment aggregation (JA) studies how to aggregate truth valuations on logically related issues. Computing the outcome of aggregation procedures is notoriously computationally hard, which is the likely reason that no implementation of them exists as of yet. However, even hard problems sometimes need to be solved. The worst-case computational complexity of answer set programming (ASP) matches that of most problems in judgment aggregation. We take advantage of this and propose a natural and modular encoding of various judgment aggregation procedures and related problems in JA into ASP. With these encodings, we achieve two results: (1) paving the way towards constructing a wide range of new benchmark instances (from JA) for answer set solving algorithms; and (2) providing an automated tool for researchers in the area of judgment aggregation.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Liao2019,

title = {Building Jiminy Cricket: An architecture for moral agreements among stakeholders},

author = {Beishui Liao and Marija Slavkovik and Leendert van der Torre},

url = {https://arxiv.org/pdf/1812.04741.pdf},

year  = {2019},

date = {2019-03-07},

booktitle = {Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society},

pages = {147–153},

abstract = {An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and is interacting with end-users. We address the

challenge of how the moral values and views of all stakeholders can be integrated

and reflected in the moral behaviour of the autonomous system. We propose an artificial moral agent architecture that uses techniques from normative systems and

formal argumentation to reach moral agreements among stakeholders. We show

how our architecture can be used not only for ethical practical reasoning and collaborative decision-making, but also for the explanation of such moral behavior.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Dyrkolbotn2018,

title = {On the distinction between implicit and explicit ethical agency},

author = {Sjur Dyrkolbotn and Truls Pedersen and Marija Slavkovik},

url = {https://dl.acm.org/doi/pdf/10.1145/3278721.3278769},

doi = {https://doi.org/10.1145/3278721.3278769},

year  = {2018},

date = {2018-12-01},

booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (AIES '18)},

pages = {74–80},

abstract = {With recent advances in artificial intelligence and the rapidly increasing importance of autonomous intelligent systems in society, it is becoming clear that artificial agents will have to be designed to comply with complex ethical standards. As we work to develop moral machines, we also push the boundaries of existing legal categories. The most pressing question is what kind of ethical decision-making our machines are actually able to engage in. Both in law and in ethics, the concept of agency forms a basis for further legal and ethical categorisations, pertaining to decision-making ability. Hence, without a cross-disciplinary understanding of what we mean by ethical agency in machines, the question of responsibility and liability cannot be clearly addressed. Here we make first steps towards a comprehensive definition, by suggesting ways to distinguish between implicit and explicit forms of ethical agency.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}