trustworthy-ai/trustworthy-ai.bib

@article{dustdar_social_2011,
	title = {The Social Compute Unit},
	volume = {15},
	issn = {1941-0131},
	doi = {10.1109/MIC.2011.68},
	abstract = {Social computing is perceived mainly as a vehicle for establishing and maintaining private relationships and thus lacks mainstream adoption in enterprises. Collaborative computing, however, is firmly established, but no tight integration of the two approaches exists. Here, the authors look at how to integrate people, in the form of human-based computing, and software services into one composite system.},
	pages = {64--69},
	number = {3},
	journaltitle = {{IEEE} Internet Computing},
	author = {Dustdar, Schahram and Bhattacharya, Kamal},
	date = {2011-05},
	note = {Conference Name: {IEEE} Internet Computing},
	keywords = {Collaboration, Online services, Privacy, service-oriented computing, social compute power, social compute unit, social computing, Social network services, workflow},
	file = {IEEE Xplore Full Text PDF:/home/zenon/Zotero/storage/BRUJCIMC/Dustdar and Bhattacharya - 2011 - The Social Compute Unit.pdf:application/pdf;IEEE Xplore Abstract Record:/home/zenon/Zotero/storage/IB8NK88P/5755601.html:text/html},
}

@article{liu_trustworthy_2021,
	title = {Trustworthy {AI}: A Computational Perspective},
	url = {http://arxiv.org/abs/2107.06641},
	shorttitle = {Trustworthy {AI}},
	abstract = {In the past few decades, artificial intelligence ({AI}) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention of developing {AI} is to benefit humans, by reducing human labor, bringing everyday convenience to human lives, and promoting social good. However, recent research and {AI} applications show that {AI} can cause unintentional harm to humans, such as making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against one group. Thus, trustworthy {AI} has attracted immense attention recently, which requires careful consideration to avoid the adverse effects that {AI} may bring to humans, so that humans can fully trust and live in harmony with {AI} technologies. Recent years have witnessed a tremendous amount of research on trustworthy {AI}. In this survey, we present a comprehensive survey of trustworthy {AI} from a computational perspective, to help readers understand the latest technologies for achieving trustworthy {AI}. Trustworthy {AI} is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy {AI}: (i) Safety \& Robustness, (ii) Non-discrimination \& Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability \& Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy {AI} to investigate in the future.},
	journaltitle = {{arXiv}:2107.06641 [cs]},
	author = {Liu, Haochen and Wang, Yiqi and Fan, Wenqi and Liu, Xiaorui and Li, Yaxin and Jain, Shaili and Liu, Yunhao and Jain, Anil K. and Tang, Jiliang},
	urldate = {2021-11-03},
	date = {2021-08-18},
	eprinttype = {arxiv},
	eprint = {2107.06641},
	note = {version: 3},
	keywords = {Computer Science - Artificial Intelligence},
	file = {arXiv Fulltext PDF:/home/zenon/Zotero/storage/3SPRGW2M/Liu et al. - 2021 - Trustworthy AI A Computational Perspective.pdf:application/pdf;arXiv.org Snapshot:/home/zenon/Zotero/storage/8AUMUFD2/2107.html:text/html},
}

@article{ferrario_ai_2020,
	title = {In {AI} We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions},
	volume = {33},
	issn = {2210-5441},
	doi = {10.1007/s13347-019-00378-3},
	shorttitle = {In {AI} We Trust Incrementally},
	abstract = {Real engines of the artificial intelligence ({AI}) revolution, machine learning ({ML}) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of {AIs} to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around {AI} by proposing an incremental model of trust that can be applied to both human-human and human-{AI} interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-{AI} interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-{AI} interactions and with a discussion of kinds of normativity in trustworthiness of {AIs}.},
	pages = {523--539},
	number = {3},
	journaltitle = {Philosophy \& Technology},
	shortjournal = {Philos. Technol.},
	author = {Ferrario, Andrea and Loi, Michele and Viganò, Eleonora},
	date = {2020-09-01},
	langid = {english},
	file = {Springer Full Text PDF:/home/zenon/Zotero/storage/TKPD5797/Ferrario et al. - 2020 - In AI We Trust Incrementally a Multi-layer Model .pdf:application/pdf},
}

@article{suh_trustworthiness_2021,
	title = {Trustworthiness in Mobile Cyber-Physical Systems},
	volume = {11},
	rights = {http://creativecommons.org/licenses/by/3.0/},
	url = {https://www.mdpi.com/2076-3417/11/4/1676},
	doi = {10.3390/app11041676},
	abstract = {As they continue to become faster and cheaper, devices with enhanced computing and communication capabilities are increasingly incorporated into diverse objects and structures in the physical environment [...]},
	pages = {1676},
	number = {4},
	journaltitle = {Applied Sciences},
	author = {Suh, Hyo-Joong and Son, Junggab and Kang, Kyungtae},
	urldate = {2021-11-03},
	date = {2021-01},
	langid = {english},
	note = {Number: 4
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {n/a},
	file = {Full Text PDF:/home/zenon/Zotero/storage/EQDGFNC4/Suh et al. - 2021 - Trustworthiness in Mobile Cyber-Physical Systems.pdf:application/pdf;Snapshot:/home/zenon/Zotero/storage/798R34VM/1676.html:text/html},
}

@book{russell_artificial_2021,
	edition = {4},
	title = {Artificial Intelligence: A Modern Approach, Global Edition},
	isbn = {978-0-13-461099-3},
	shorttitle = {Artificial Intelligence},
	publisher = {Pearson},
	author = {Russell, Stuart J. and Norvig, Peter},
	date = {2021},
	file = {Russell and Norvig - 2021 - Artificial Intelligence A Modern Approach, Global.pdf:/home/zenon/Zotero/storage/LADUV26B/Russell and Norvig - 2021 - Artificial Intelligence A Modern Approach, Global.pdf:application/pdf},
}

@online{european_commission_ethics_nodate,
	title = {Ethics guidelines for trustworthy {AI} {\textbar} Shaping Europe’s digital future},
	url = {https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai},
	abstract = {On 8 April 2019, the High-Level Expert Group on {AI} presented Ethics Guidelines for Trustworthy Artificial Intelligence. This followed the publication of the guidelines' first draft in December 2018 on which more than 500 comments were received through an open consultation.},
	author = {European Commission},
	urldate = {2021-12-13},
	langid = {english},
	file = {Snapshot:/home/zenon/Zotero/storage/JG9TE5X8/ethics-guidelines-trustworthy-ai.html:text/html},
}