Papers selected for the review
| Authors/year | Title | Journal |
|---|---|---|
| Adadi and Berrada (2018) | Peeking inside the black-box: A survey on explainable artificial intelligence (XAI) | IEEE access |
| Arrieta et al. (2020) | Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI | Information fusion |
| Berkelaar (2014) | Cybervetting, online information, and personnel selection: New transparency expectations and the emergence of a digital social contract | Management communication quarterly |
| Bertino et al. (2019) | Data transparency with blockchain and AI ethics | Journal of data and information quality |
| Coeckelbergh (2009) | Virtual moral agency, virtual moral responsibility: On the moral significance of the appearance, perception, and performance of artificial agents | AI and society |
| Coeckelbergh (2020) | Artificial intelligence, responsibility attribution, and a relational justification of explainability | Science and engineering ethics |
| D'Acquisto (2020) | On conflicts between ethical and logical principles in artificial intelligence | AI and society |
| Doshi-Velez and Kim (2017) | Towards a rigorous science of interpretable machine learning | arXiv preprint arXiv:1702.08608 |
| Felzmann et al. (2019) | Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns | Big data and society |
| Fernandez et al. (2019) | Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to? | IEEE computational intelligence magazine |
| Floridi (2011) | Children of the fourth revolution | Philosophy and technology |
| Floridi et al. (2018) | AI4People—an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations | Minds and machines |
| Garibaldi (2019) | The need for fuzzy AI | IEEE/CAA journal of automatica sinica |
| Gunning and Aha (2019) | DARPA's explainable artificial intelligence program | AI magazine |
| Gunning et al. (2019) | XAI—explainable artificial intelligence | Science robotics |
| Hacker et al. (2020) | Explainable AI under contract and tort law: legal incentives and technical challenges | Artificial intelligence and law |
| Harper (2019) | The role of HCI in the age of AI | International journal of human–computer interactions |
| Holzinger et al. (2019) | Causability and explainability of artificial intelligence in medicine | Data mining and knowledge discovery |
| Kaplan and Haenlein (2019) | Siri, siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence | Business horizons |
| Kroll (2018) | Data science data governance [AI ethics] | IEEE security and privacy |
| Kshetri (2019) | Complementary and synergistic properties of blockchain and artificial intelligence | IT professional |
| Lawless et al. (2019) | Artificial intelligence, autonomy, and human-machine teams: Interdependence, context, and explainable AI | AI magazine |
| Lecue (2019) | On the role of knowledge graphs in explainable AI | Semantic web |
| Miller (2019) | Explanation in artificial intelligence: Insights from the social sciences | Artificial intelligence |
| Mittelstadt et al. (2016) | The ethics of algorithms: Mapping the debate | Big data and society |
| Mohseni and Ragan (2018) | A human-grounded evaluation benchmark for local explanations of machine learning | arXiv preprint arXiv:1801.05075 |
| Nassar et al. (2020) | Blockchain for explainable and trustworthy artificial intelligence | Data mining and knowledge discovery |
| Ntoutsi et al. (2020) | Bias in data-driven artificial intelligence systems—An introductory survey | Data mining and knowledge discovery |
| Páez (2019) | The pragmatic turn in explainable artificial intelligence (XAI) | Minds and machines |
| Pieters (2011) | Explanation and trust: What to tell the user in security and AI? | Ethics and information technology |
| Poursabzi-Sangdeh et al. (2018) | Manipulating and measuring model interpretability | arXiv preprint arXiv:1802.07810 |
| Preece (2018) | Asking “why” in AI: Explainability of intelligent systems–perspectives and challenges | Intelligent systems in accounting, finance and management |
| Rai (2020) | Explainable AI: from black box to glass box | Journal of academy of marketing science |
| Robbins (2019a) | AI and the path to envelopment: Knowledge as a first step towards the responsible regulation and use of AI-powered machines | AI and society |
| Robbins (2019b) | A misdirected principle with a catch: Explicability for AI | Minds and machines |
| Stahl and Wright (2018) | Ethics and privacy in AI and big data: Implementing responsible research and innovation | IEEE security and privacy |
| Timmers (2019) | Ethics of AI and cybersecurity when sovereignty is at stake | Minds and machines |
| Tóth (2019) | Algorithmic copyright enforcement and AI: Issues and potential solutions through the lens of text and data mining | Masaryk university journal of law and technology |
| Waltl and Vogl (2018) | Explainable artificial intelligence- the new frontier in legal informatics | Jusletter IT |
| Authors/year | Title | Journal |
|---|---|---|
| Peeking inside the black-box: A survey on explainable artificial intelligence (XAI) | IEEE access | |
| Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI | Information fusion | |
| Cybervetting, online information, and personnel selection: New transparency expectations and the emergence of a digital social contract | Management communication quarterly | |
| Data transparency with blockchain and AI ethics | Journal of data and information quality | |
| Virtual moral agency, virtual moral responsibility: On the moral significance of the appearance, perception, and performance of artificial agents | AI and society | |
| Artificial intelligence, responsibility attribution, and a relational justification of explainability | Science and engineering ethics | |
| On conflicts between ethical and logical principles in artificial intelligence | AI and society | |
| Towards a rigorous science of interpretable machine learning | arXiv preprint | |
| Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns | Big data and society | |
| Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to? | IEEE computational intelligence magazine | |
| Children of the fourth revolution | Philosophy and technology | |
| AI4People—an ethical framework for a good AI society: Opportunities, risks, principles, and recommendations | Minds and machines | |
| The need for fuzzy AI | IEEE/CAA journal of automatica sinica | |
| DARPA's explainable artificial intelligence program | AI magazine | |
| XAI—explainable artificial intelligence | Science robotics | |
| Explainable AI under contract and tort law: legal incentives and technical challenges | Artificial intelligence and law | |
| The role of HCI in the age of AI | International journal of human–computer interactions | |
| Causability and explainability of artificial intelligence in medicine | Data mining and knowledge discovery | |
| Siri, siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence | Business horizons | |
| Data science data governance [AI ethics] | IEEE security and privacy | |
| Complementary and synergistic properties of blockchain and artificial intelligence | IT professional | |
| Artificial intelligence, autonomy, and human-machine teams: Interdependence, context, and explainable AI | AI magazine | |
| On the role of knowledge graphs in explainable AI | Semantic web | |
| Explanation in artificial intelligence: Insights from the social sciences | Artificial intelligence | |
| The ethics of algorithms: Mapping the debate | Big data and society | |
| A human-grounded evaluation benchmark for local explanations of machine learning | arXiv preprint | |
| Blockchain for explainable and trustworthy artificial intelligence | Data mining and knowledge discovery | |
| Bias in data-driven artificial intelligence systems—An introductory survey | Data mining and knowledge discovery | |
| The pragmatic turn in explainable artificial intelligence (XAI) | Minds and machines | |
| Explanation and trust: What to tell the user in security and AI? | Ethics and information technology | |
| Manipulating and measuring model interpretability | arXiv preprint | |
| Asking “why” in AI: Explainability of intelligent systems–perspectives and challenges | Intelligent systems in accounting, finance and management | |
| Explainable AI: from black box to glass box | Journal of academy of marketing science | |
| AI and the path to envelopment: Knowledge as a first step towards the responsible regulation and use of AI-powered machines | AI and society | |
| A misdirected principle with a catch: Explicability for AI | Minds and machines | |
| Ethics and privacy in AI and big data: Implementing responsible research and innovation | IEEE security and privacy | |
| Ethics of AI and cybersecurity when sovereignty is at stake | Minds and machines | |
| Algorithmic copyright enforcement and AI: Issues and potential solutions through the lens of text and data mining | Masaryk university journal of law and technology | |
| Explainable artificial intelligence- the new frontier in legal informatics | Jusletter IT |
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