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Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model
16 references·0 citing·33 second hop
Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model (2021)Principle-based recommendations for big d…The Ethics of AI Ethics: An Evaluation of Guidelines (2020)The Ethics of AI Ethics: An E…The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation (2020)The Chinese approach to artif…Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI (2020)Principled Artificial Intelli…How to Design AI for Social Good: Seven Essential Factors (2020)How to Design AI for Social G…The global landscape of AI ethics guidelines (2019)The global landscape of AI et…From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019)From What to How: An Initial …How AI can be a force for good (2018)How AI can be a force for goodFairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making (2018)Fairness and Accountability D…Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation (2017)Why a Right to Explanation of…What is data ethics? (2016)What is data ethics?The ethics of algorithms: Mapping the debate (2016)The ethics of algorithms: Map…The Trouble with Algorithmic Decisions (2015)The Trouble with Algorithmic …The Black Box Society (2015)The Black Box SocietyMedia Technologies (2014)Media TechnologiesBig Data's Disparate Impact (2014)Big Data's Disparate Impact8. The Ethics of Information (2010)8. The Ethics of InformationTruth and Method (1960)Why Most Published Research Findings Are False (2005)Stanford Encyclopedia of Philosophy (2015)Artificial Intelligence and the Public Sector—Applications and Challenges (2018)Principles alone cannot guarantee ethical AI (2019)Setting the future of digital and social media marketing research: Perspectives and research propositions (2020)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (2009)Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement (2009)Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement. (2009)Ethics of AI in Education: Towards a Community-Wide Framework (2021)The structural transformation of the public sphere: an inquiry into a category of bourgeois society (1990)Cybernetics or Control and Communication in the Animal and the Machine (2019)Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies (2021)Ethical framework for Artificial Intelligence and Digital technologies (2021)AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2022)Experimental evidence of massive-scale emotional contagion through social networks (2014)AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations (2018)Emerging challenges in AI and the need for AI ethics education (2020)Ethical principles for artificial intelligence in education (2022)Trustworthy Artificial Intelligence: A Review (2022)Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions (2019)A Unified Framework of Five Principles for AI in Society (2022)Governing artificial intelligence: ethical, legal and technical opportunities and challenges (2018)Recommender Systems and their Ethical Challenges (2019)Bridging the Gap Between Ethics and Practice (2020)Artificial intelligence, systemic risks, and sustainability (2021)Responsible artificial intelligence governance: A review and research framework (2025)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Dissecting racial bias in an algorithm used to manage the health of populations (2019)'It's Reducing a Human Being to a Percentage' (2018)Algorithms at Work: The New Contested Terrain of Control (2019)When data is capital: Datafication, accumulation, and extraction (2019)What Do Platforms Do? Understanding the Gig Economy (2020)
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References

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  • 2015The Trouble with Algorithmic Decisions422 cited
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