@article{leverag24,

title = {Leveraging Professional Ethics for Responsible AI: Applying AI techniques to journalism.},

author = {Nicholas Diakopoulos and Christoph Trattner and Dietmar Jannach and Irene Costera Meijer and Enrico Motta},

url = {https://mediafutures.no/3625252/https://mediafutures.no/3625252-1/},

year  = {2024},

date = {2024-01-23},

urldate = {2024-01-23},

journal = {Communications of the ACM},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Opdahl2023,

title = {Trustworthy Journalism Through AI},

author = {Andreas L. Opdahl and Bjørnar Tessem and Duc-Tien Dang-Nguyen and Enrico Motta and Vinay Setty and Eivind Throndsen and Are Tverberg and Christoph Trattner},

url = {https://mediafutures.no/1-s2-0-s0169023x23000423-main/},

year  = {2023},

date = {2023-04-29},

urldate = {2023-04-29},

journal = {Data & Knowledge Engineering (DKE), Elsevier},

abstract = {Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cristin2000622,

title = {Responsible media technology and AI: challenges and research directions},

author = {Christoph Trattner and Dietmar Jannach and Enrico Motta and Irene Costera Meijer and Nicholas Diakopoulos and Mehdi Elahi and Andreas L. Opdahl and Bjørnar Tessem and Njål Borch and Morten Fjeld and Lilja Øvrelid and Koenraad De Smedt and Hallvard Moe},

url = {https://app.cristin.no/results/show.jsf?id=2000622, Cristin

https://link.springer.com/content/pdf/10.1007/s43681-021-00126-4.pdf},

doi = {https://doi.org/10.1007/s43681-021-00126-4},

year  = {2021},

date = {2021-12-20},

urldate = {2021-12-20},

journal = {AI and Ethics},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Motta2020,

title = {Analysis and design of computational news angles},

author = {Enrico Motta and Enrico Daga and Andreas L. Opdahl and Bjørnar Tessem},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9127417},

doi = {10.1109/ACCESS.2020.3005513},

year  = {2020},

date = {2020-06-29},

urldate = {2020-06-29},

journal = {IEEE Access},

volume = {8},

pages = {120613-120626},

abstract = {A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chiatti2020,

title = {Towards a Framework for Visual Intelligence in Service Robotics: Epistemic Requirements and Gap Analysis},

author = {Agnese Chiatti and Enrico Motta and Enrico Daga},

url = {https://arxiv.org/ftp/arxiv/papers/2003/2003.06171.pdf},

year  = {2020},

date = {2020-03-13},

journal = {Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020)},

pages = {905–916},

abstract = {A key capability required by service robots operating in real-world, dynamic environments is that of Visual Intelligence, i.e., the ability to use their vision system, reasoning components and background knowledge to make sense of their environment. In this paper, we analyze the epistemic requirements for Visual Intelligence, both in a top-down fashion, using existing frameworks for human-like Visual Intelligence in the literature, and from the bottom up, based on the errors emerging from object recognition trials in a real-world robotic scenario. Finally, we use these requirements to evaluate current knowledge bases for Service Robotics and to identify gaps in the support they provide for Visual Intelligence. These gaps provide the basis of a research agenda for developing more effective knowledge representations for Visual Intelligence.},

note = {Pre SFI},

keywords = {},

pubstate = {published},

tppubtype = {article}

}