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.},
	number = {3},
	journal = {IEEE Internet Computing},
	author = {Dustdar, Schahram and Bhattacharya, Kamal},
	month = may,
	year = {2011},
	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},
	pages = {64--69},
	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}},
	shorttitle = {Trustworthy {AI}},
	url = {http://arxiv.org/abs/2107.06641},
	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.},
	urldate = {2021-11-03},
	journal = {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},
	month = aug,
	year = {2021},
	note = {arXiv: 2107.06641
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},
	shorttitle = {In {AI} {We} {Trust} {Incrementally}},
	url = {https://doi.org/10.1007/s13347-019-00378-3},
	doi = {10.1007/s13347-019-00378-3},
	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.},
	language = {en},
	number = {3},
	urldate = {2021-11-03},
	journal = {Philosophy \& Technology},
	author = {Ferrario, Andrea and Loi, Michele and Viganò, Eleonora},
	month = sep,
	year = {2020},
	pages = {523--539},
	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},
	copyright = {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 [...]},
	language = {en},
	number = {4},
	urldate = {2021-11-03},
	journal = {Applied Sciences},
	author = {Suh, Hyo-Joong and Son, Junggab and Kang, Kyungtae},
	month = jan,
	year = {2021},
	note = {Number: 4
Publisher: Multidisciplinary Digital Publishing Institute},
	keywords = {n/a},
	pages = {1676},
	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},
}