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Can't we all just get along? Citizen scientists interacting with algorithms
2 references·1 citing·15 second hop
Can't we all just get along? Citizen scientists interacting with algorithms (2021)Can't we all just get along? Citizen scie…Participation Is not a Design Fix for Machine Learning (2022)Participation Is not a Design…Opportunities and Risks for Citizen Science in the Age of Artificial Intelligence (2019)Opportunities and Risks for C…Collaborating with Bots and Automation on OpenStreetMap (2024)Collaborating with Bots and A…A typology of reviews: an analysis of 14 review types and associated methodologies (2009)Business models and dynamic capabilities (2017)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Empowering local communities using artificial intelligence (2022)Fair and equitable AI in biomedical research and healthcare: Social science perspectives (2023)The critical role of HRM in AI-driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption (2024)CRITICAL QUESTIONS FOR BIG DATA (2012)Fairness and Abstraction in Sociotechnical Systems (2019)AI Ethics in the Public, Private, and NGO Sectors: A Review of a Global Document Collection (2021)Algorithmic Impact Assessments and Accountability (2021)Putting AI ethics to work: are the tools fit for purpose? (2021)Situated Learning (1991)OpenStreetMap: User-Generated Street Maps (2008)Thematic Analysis (2019)Collective Intelligence in Humanitarian Voluntary Geographic Information: The Case of the HOT Tasking Manager (2025)
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References

  • 2022Participation Is not a Design Fix for Machine Learning267 cited
  • 2019Opportunities and Risks for Citizen Science in the Age of Artificial Intelligence107 cited

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  • 2024Collaborating with Bots and Automation on OpenStreetMap5 cited

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