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Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development
16 references·1 citing·47 second hop
Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development (2021)Machine Learning Practices Outside Big Te…Where Responsible AI meets Reality (2021)Where Responsible AI meets Re…A Survey on Bias and Fairness in Machine Learning (2021)A Survey on Bias and Fairness…Closing the AI accountability gap (2020)Closing the AI accountability…Model Cards for Model Reporting (2019)Model Cards for Model Reporti…Improving Fairness in Machine Learning Systems (2019)Improving Fairness in Machine…Human-AI Collaboration in Data Science (2019)Human-AI Collaboration in Dat…How Data Science Workers Work with Data (2019)How Data Science Workers Work…Conducting in-depth interviews with and without voice recorders: a comparative analysis (2019)Conducting in-depth interview…Beyond Accuracy: The Role of Mental Models in Human-AI Team Performance (2019)Beyond Accuracy: The Role of …Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (2018)Data Statements for Natural L…European Union Regulations on Algorithmic Decision Making and a “Right to Explanation” (2017)European Union Regulations on…There is a blind spot in AI research (2016)There is a blind spot in AI r…Convenience Sampling, Random Sampling, and Snowball Sampling: How Does Sampling Affect the Validity of Research? (2015)Convenience Sampling, Random …Using thematic analysis in psychology (2006)Using thematic analysis in ps…The role of trust in automation reliance (2003)The role of trust in automati…A value for n -person games (1988)A value for n -person gamesTowards a Process Model to Enable Domain Experts to Become Citizen Data Scientists for Industrial Applications (2022)Towards a Process Model to En…Qualitative evaluation and research methods (1990)Qualitative Data Analysis: An Expanded Sourcebook (1994)Basics of qualitative research : techniques and procedures for developing grounded theory (1998)Older Adults' Reasons for Using Technology while Aging in Place (2015)Thematic Analysis (2017)Reflecting on reflexive thematic analysis (2019)The Handbook of Social Psychology (1969)Humans and Automation: Use, Misuse, Disuse, Abuse (1997)Situation Awareness, Mental Workload, and Trust in Automation: Viable, Empirically Supported Cognitive Engineering Constructs (2008)Automation bias: a systematic review of frequency, effect mediators, and mitigators (2011)Engineering Psychology and Human Performance (2015)A Meta-Analysis of Factors Influencing the Development of Trust in Automation (2016)Judgment under Uncertainty: Heuristics and Biases (1974)Judgment under Uncertainty: Heuristics and Biases (1975)Stanford Encyclopedia of Philosophy (2015)Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations (2018)From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019)Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019)Big Data's Disparate Impact (2014)Corporate digital responsibility (2019)The Society of Algorithms (2021)Social physics (2022)Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015)Data Feminism (2020)On the Opportunities and Risks of Foundation Models (2021)The Study of Second Language Acquisition (1996)CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials (2010)Publication Manual of the American Psychological Association (2011)Fairness in Machine Learning: A Survey (2023)Bias and Fairness in Large Language Models: A Survey (2024)Fairness through awareness (2012)Semantics derived automatically from language corpora contain human-like biases (2017)Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Algorithmic Bias in Education (2021)Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation (2023)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)PRODUCT DEVELOPMENT: PAST RESEARCH, PRESENT FINDINGS, AND FUTURE DIRECTIONS (1995)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Bridging the Gap Between Ethics and Practice (2020)Trustworthy AI: From Principles to Practices (2022)Trustworthy Artificial Intelligence: A Review (2022)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Algorithmic bias: review, synthesis, and future research directions (2021)Taxonomy of Risks posed by Language Models (2022)Cores of convex games (1971)Graphs and Cooperation in Games (1977)Property Rights and the Nature of the Firm (1990)
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  • 2006Using thematic analysis in psychology193,725 cited
  • 2021A Survey on Bias and Fairness in Machine Learning5,798 cited
  • 1988A value for n -person games4,956 cited
  • 2019Model Cards for Model Reporting2,653 cited
  • 2017European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”2,129 cited
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  • 2021Where Responsible AI meets Reality329 cited
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How Data Science Workers Work with Data
262 cited

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  • 2022Towards a Process Model to Enable Domain Experts to Become Citizen Data Scientists for Industrial Applications10 cited