Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI

Fjeld, Jessica; Achten, Nele; Hilligoss, Hannah; Nagy, Ádám; Srikumar, Madhu

doi:10.2139/ssrn.3518482

Cited by 463 publications

(506 citation statements)

References 1 publication

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“…A recent review of some of the main AI principles published since 2016 appears in [385]. In this work, the authors show a visual framework where different organizations are classified according to the following parameters:…”

Section: Principles Of Artificial Intelligencementioning

confidence: 99%

“…Even though these references apparently address an AI principle that appears to be independent of XAI, the literature shows that they are intertwined. For instance, the survey in [385] evinces that 26 out of the 28 AI principles that deal with XAI, also talk about fairness explicitly. This fact elucidates that organizations usually consider both aspects together when implementing Responsible AI.…”

Section: Fairness and Discriminationmentioning

confidence: 99%

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

et al. 2020

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In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

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Section: Principles Of Artificial Intelligencementioning

confidence: 99%

Section: Fairness and Discriminationmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

et al. 2020

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“…Seen from the perspective of the call formulated by Morley et al to go from "what to how", this focus on practices aims to provide guidance to AI engineers in identifying and addressing ethical challenges. In their paper, Morley et al examine the growing imbalance in the literature with regards to the attention that is paid to establishing principles and frameworks for AI (Floridi and Cowls 2019;Jobin et al 2019;Whittlestone et al 2019;Zeng et al 2019;Fjeld et al 2020) as opposed to operationalising these principles into practices and tools. In this paper, we share the authors' concerns and propose further steps towards bridging the gap between principles and practices by making explicit how AI engineers should address ethical challenges found in their practices.…”

Section: The Contribution To the Literaturementioning

confidence: 99%

Getting into the engine room: a blueprint to investigate the shadowy steps of AI ethics

Rochel

Evéquoz

2020

AI & Soc

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Enacting an AI system typically requires three iterative phases where AI engineers are in command: selection and preparation of the data, selection and configuration of algorithmic tools, and fine-tuning of the different parameters on the basis of intermediate results. Our main hypothesis is that these phases involve practices with ethical questions. This paper maps these ethical questions and proposes a way to address them in light of a neo-republican understanding of freedom, defined as absence of domination. We thereby identify different types of responsibility held by AI engineers and link them to concrete suggestions on how to improve professional practices. This paper contributes to the literature on AI and ethics by focusing on the work necessary to configure AI systems, thereby offering an input to better practices and an input for societal debates.

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“…Concerns over applications of machine-learning, algorithmic decision making and uses of data has led to the publication of "ethical AI" principles from a range of organizations around the world. A recent review compared 36 different sets of principles to help identify areas of potential convergence (Fjeld et al, 2020).…”

Section: Ensure Data Is Used Legally and Ethicallymentioning

confidence: 99%

A manifesto for increasing access to data in engineering

Dodds

L'Henaff

Maddison

et al. 2020

DCE

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This paper introduces a set of principles that articulate a shared vision for increasing access to data in the engineering and related sectors. The principles are intended to help guide progress toward a data ecosystem that provides sustainable access to data, in ways that will help a variety of stakeholders in maximizing its value while mitigating potential harms. In addition to being a manifesto for change, the principles can also be viewed as a means for understanding the alignment, overlaps and gaps between a range of existing research programs, policy initiatives, and related work on data governance and sharing. After providing background on the growing data economy and relevant recent policy initiatives in the United Kingdom and European Union, we then introduce the nine key principles of the manifesto. For each principle, we provide some additional rationale and links to related work. We invite feedback on the manifesto and endorsements from a range of stakeholders.

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Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI

Cited by 463 publications

References 1 publication

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Getting into the engine room: a blueprint to investigate the shadowy steps of AI ethics

A manifesto for increasing access to data in engineering

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