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Trust and transparency in AI: industry voices on data, ethics, and compliance
16 references·0 citing·46 second hopsampled
Trust and transparency in AI: industry voices on data, ethics, and compliance (2025)Trust and transparency in AI: industry vo…Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance (2023)Worldwide AI ethics: A review…Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Connecting the dots in trustw…Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice (2022)Human–AI Interactions in Publ…Toward a Sociology of Artificial Intelligence: A Call for Research on Inequalities and Structural Change (2021)Toward a Sociology of Artific…Governing AI safety through independent audits (2021)Governing AI safety through i…Data Quality for Machine Learning Tasks (2021)Data Quality for Machine Lear…Artificial Intelligence Regulation: a framework for governance (2021)Artificial Intelligence Regul…A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle (2021)A Framework of Potential Sour…“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021)“Everyone wants to do the mod…One size fits all? 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Comparing reflexive thematic analysis and other pattern‐based qualitative analytic approaches (2020)Toward good practice in thematic analysis: Avoiding common problems and be(com)ing a knowing researcher (2022)Supporting best practice in reflexive thematic analysis reporting in Palliative Medicine : A review of published research and introduction to the Reflexive Thematic Analysis Reporting Guidelines (RTARG) (2024)A General Inductive Approach for Analyzing Qualitative Evaluation Data (2006)AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations (2018)Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020)A Survey on Bias and Fairness in Machine Learning (2021)Advances, challenges and opportunities in creating data for trustworthy AI (2022)Power to the People? Opportunities and Challenges for Participatory AI (2022)Big Data's Disparate Impact (2014)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Towards a standard for identifying and managing bias in artificial intelligence (2022)Humans inherit artificial intelligence biases (2023)Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2023)Humans and Automation: Use, Misuse, Disuse, Abuse (1997)Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (2018)Exploring artificial intelligence adoption in public organizations: a comparative case study (2022)Gender bias perpetuation and mitigation in AI technologies: challenges and opportunities (2023)Exploring automation bias in human–AI collaboration: a review and implications for explainable AI (2025)Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (2017)AI Governance in a Complex and Rapidly Changing Regulatory Landscape: A Global Perspective (2024)Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework (2024)Assessing the nexus of Generative AI adoption, ethical considerations and organizational performance (2024)Saturation controversy in qualitative research: Complexities and underlying assumptions. 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References

  • 2005How Many Interviews Are Enough?20,704 cited
  • 2013Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research11,963 cited
  • 2020One size fits all? What counts as quality practice in (reflexive) thematic analysis?9,627 cited
  • 2019The global landscape of AI ethics guidelines6,172 cited
  • 2019To saturate or not to saturate? Questioning data saturation as a useful concept for thematic analysis and sample-size rationales4,749 cited
  • 2020A simple method to assess and report thematic saturation in qualitative research2,913 cited
  • 2023Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation795 cited
  • 2021“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI774 cited
  • 2021A Framework of Potential Sources of Harm Throughout the Machine Learning Life Cycle573 cited
  • 2022Human–AI Interactions in Public Sector Decision Making: “Automation Bias” and “Selective Adherence” to Algorithmic Advice462 cited
  • 2021Artificial Intelligence Regulation: a framework for governance395 cited
  • 2023Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance363 cited
  • 2021Governing AI safety through independent audits249 cited
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Toward a Sociology of Artificial Intelligence: A Call for Research on Inequalities and Structural Change
234 cited
  • 2020AI and Its New Winter: from Myths to Realities179 cited
  • 2021Data Quality for Machine Learning Tasks127 cited
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