Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions

King, Thomas C.; Aggarwal, Nikita; Taddeo, Mariarosaria; Floridi, Luciano

doi:10.1007/978-3-030-81907-1_13

Cited by 10 publications

(11 citation statements)

References 73 publications

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“…Stillwell, andGraepel 2013;Kosinski et al 2015). All in all, only a very small number of papers is published about the misuse of AI systems, even though they impressively show what massive damage can be done with those systems(Brundage et al 2018;King et al 2019;O'Neil 2016).…”

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confidence: 99%

The Ethics of AI Ethics: An Evaluation of Guidelines

2020

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Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems -and how the effectiveness in the demands of AI ethics can be improved.

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confidence: 99%

The Ethics of AI Ethics: An Evaluation of Guidelines

2020

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“…Thus, 'Why a particular decision was made' is of prime importance to improve the trust [77] of end-users including subject matter experts, developers, law-makers, and laypersons alike [78]- [80]. Fundamental explanations to classifier prediction is ever so important to stakeholders and government agencies to build trustability as we transition to a connected AI-driven socio-economic environment.…”

Section: A Why Is Research On Xai Important?mentioning

confidence: 99%

Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

Das,

Rad

2020

Preprint

176

187

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Nowadays, deep neural networks are widely used in mission critical systems such as healthcare, self-driving vehicles, and military which have direct impact on human lives. However, the black-box nature of deep neural networks challenges its use in mission critical applications, raising ethical and judicial concerns inducing lack of trust. Explainable Artificial Intelligence (XAI) is a field of Artificial Intelligence (AI) that promotes a set of tools, techniques, and algorithms that can generate high-quality interpretable, intuitive, human-understandable explanations of AI decisions. In addition to providing a holistic view of the current XAI landscape in deep learning, this paper provides mathematical summaries of seminal work. We start by proposing a taxonomy and categorizing the XAI techniques based on their scope of explanations, methodology behind the algorithms, and explanation level or usage which helps build trustworthy, interpretable, and self-explanatory deep learning models. We then describe the main principles used in XAI research and present the historical timeline for landmark studies in XAI from 2007 to 2020. After explaining each category of algorithms and approaches in detail, we then evaluate the explanation maps generated by eight XAI algorithms on image data, discuss the limitations of this approach, and provide potential future directions to improve XAI evaluation.

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“…This multistakeholder ethical framework contributes to the existing research, and it can serve as the platform around which rules, technical standards and best practices can be developed for specific sectors and industries. Moreover, these ethical principles play both an enabling role (facilitating the stakeholders in adopting and managing AI-augmented HRM) and a constraining one (regulation of AI technologies for the betterment of employees, organisation and society at large) to facilitates optimal and judicious use of AI in HRM (King et al. , 2020; Taddeo and Floridi, 2018).…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

A multi-stakeholder ethical framework for AI-augmented HRM

Prikshat

Patel

Varma

et al. 2022

IJM

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PurposeThis narrative review presents a multi-stakeholder ethical framework for AI-augmented HRM, based on extant research in the domains of ethical HRM and ethical AI. More specifically, the authors identify critical ethical issues pertaining to AI-augmented HRM functions and suggest ethical principles to address these issues by identifying the relevant stakeholders based on the responsibility ethics approach.Design/methodology/approachThis paper follows a narrative review approach by first identifying various ethical/codes/issues/dilemmas discussed in HRM and AI. The authors next discuss ethical issues concerning AI-augmented HRM, drawing from recent literature. Finally, the authors propose ethical principles for AI-augmented HRM and stakeholders responsible for managing those issues.FindingsThe paper summarises key findings of extant research in the ethical HRM and AI domain and provides a multi-stakeholder ethical framework for AI-augmented HRM functions.Originality/valueThis research's value lies in conceptualising a multi-stakeholder ethical framework for AI-augmented HRM functions comprising 11 ethical principles. The research also identifies the class of stakeholders responsible for identified ethical principles. The research also presents future research directions based on the proposed model.

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Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions

Cited by 10 publications

References 73 publications

The Ethics of AI Ethics: An Evaluation of Guidelines

The Ethics of AI Ethics: An Evaluation of Guidelines

Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

A multi-stakeholder ethical framework for AI-augmented HRM

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