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Responsible AI: A Primer for the Legal Community
13 references·4 citing·36 second hop
Responsible AI: A Primer for the Legal Community (2020)Responsible AI: A Primer for the Legal Co…Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020)Toward Trustworthy AI Develop…Principles to Practices for Responsible AI: Closing the Gap (2020)Principles to Practices for R…Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI (2020)Principled Artificial Intelli…Fairlearn: A toolkit for assessing and improving fairness in AI (2020)Fairlearn: A toolkit for asse…The global landscape of AI ethics guidelines (2019)The global landscape of AI et…Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way (2019)Responsible Artificial Intell…Responsible Artificial Intelligence (2019)Responsible Artificial Intell…Model Cards for Model Reporting (2019)Model Cards for Model Reporti…AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias (2019)AI Fairness 360: An extensibl…Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018)Gender Shades: Intersectional…Fairness definitions explained (2018)Fairness definitions explainedMoral Responsibility and the Problem of Many Hands (2015)Moral Responsibility and the …Fairness through awareness (2012)Fairness through awarenessAI Guidelines and Ethical Readiness Inside SMEs: A Review and Recommendations (2024)AI Guidelines and Ethical Rea…Accountability and Transparency Ensuring Responsible AI Development (2024)Accountability and Transparen…A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms (2023)A Validity Perspective on Eva…AI and Ethical Issues (2022)AI and Ethical IssuesIncorporating Fairness into Game Theory and Economics (2011)How the machine ‘thinks’: Understanding opacity in machine learning algorithms (2016)Princeton University Press (2017)Semantics derived automatically from language corpora contain human-like biases (2017)A Survey on Bias and Fairness in Machine Learning (2021)Equality of Opportunity in Supervised Learning (2016)Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments (2017)Bias in data‐driven artificial intelligence systems—An introductory survey (2020)Algorithmic Bias in Education (2021)Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023)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)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)Principles alone cannot guarantee ethical AI (2019)Ethics of AI in Education: Towards a Community-Wide Framework (2021)Certifying and Removing Disparate Impact (2015)Mitigating Unwanted Biases with Adversarial Learning (2018)Bridging the Gap Between Ethics and Practice (2020)Addressing bias in big data and AI for health care: A call for open science (2021)Trustworthy Artificial Intelligence: A Review (2022)The Ethics of AI Ethics: An Evaluation of Guidelines (2020)ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope (2023)Framing governance for a contested emerging technology:insights from AI policy (2020)Responsible AI for Digital Health: a Synthesis and a Research Agenda (2021)How can we manage biases in artificial intelligence systems – A systematic literature review (2023)Artificial intelligence, systemic risks, and sustainability (2021)Responsible artificial intelligence governance: A review and research framework (2025)An Overview of Artificial Intelligence Ethics (2022)Thinking responsibly about responsible AI and ‘the dark side’ of AI (2022)Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making (2024)Ethics as a Service: A Pragmatic Operationalisation of AI Ethics (2021)Ethics-Based Auditing to Develop Trustworthy AI (2021)Auditing large language models: a three-layered approach (2023)
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References

  • 2019The global landscape of AI ethics guidelines6,172 cited
  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2012Fairness through awareness3,501 cited
  • 2019Model Cards for Model Reporting2,653 cited
  • 2018Fairness definitions explained1,125 cited
  • 2019Responsible Artificial Intelligence1,064 cited
  • 2020Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI998 cited
  • 2019AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias884 cited
  • 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims305 cited
  • 2019Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way262 cited
  • 2020Fairlearn: A toolkit for assessing and improving fairness in AI204 cited
  • 2015Moral Responsibility and the Problem of Many Hands152 cited
  • 2020Principles to Practices for Responsible AI: Closing the Gap65 cited

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  • 2023A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms34 cited
  • 2024Accountability and Transparency Ensuring Responsible AI Development

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34 cited
  • 2024AI Guidelines and Ethical Readiness Inside SMEs: A Review and Recommendations29 cited
  • 2022AI and Ethical Issues20 cited