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Artificial Intelligence (AI), particularly through the advent of large-scale generative AI (GenAI) models such as Large Language Models (LLMs), has become a transformative element in contemporary technology.
The chameleon of accountability: Forms and discourses
Amanda Sinclair · 1995
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Accountability in a computerized society
Helen Nissenbaum · 1996
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Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development
Jennifer Fereday and Eimear Muir-Cochrane · 2006
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Analysing and assessing accountability: A conceptual framework 1
Mark Bovens · 2007
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Systematic literature reviews in software engineering–a systematic literature review
Barbara Kitchenham, O Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen Linkman · 2009
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Software metrics: a rigorous and practical approach
Norman Fenton and James Bieman · 2014
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Accountability of ai under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O’Brien, Kate Scott, Stuart Schieber, James Waldo, David Weinberger, et al · 2017
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Transparent, explainable, and accountable ai for robotics
Sandra Wachter, Brent Mittelstadt, and Luciano Floridi · 2017
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The global landscape of ai ethics guidelines
Anna Jobin, Marcello Ienca, and Effy Vayena · 2019
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Guidelines for including grey literature and conducting multivocal literature reviews in software engineering
Vahid Garousi, Michael Felderer, and Mika V. Mäntylä · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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Factsheets: Increasing trust in ai services through supplier’s declarations of conformity
Matthew Arnold, Rachel KE Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilović, Ravi Nair, K Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, et al · 2019
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Closing the ai accountability gap: Defining an end-to-end framework for internal algorithmic auditing
Inioluwa Deborah Raji, Andrew Smart, Rebecca N White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes · 2020
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Assessment list for trustworthy artificial intelligence (altai) for self-assessment, 2020
European Commission’s High-Level Expert Group on Artificial Intelligence (AI HLEG) · 2020
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Patterns and anti-patterns, principles and pitfalls: accountability and transparency in ai
Jeanna Matthews · 2020
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From principles to practice. an interdisciplinary framework to operationalise ai ethics
Lajla Fetic, Torsten Fleischer, Paul Grünke, Thilo Hagendorf, Sebastian Hallensleben, Marc Hauer, Michael Herrmann, Rafaela Hillerbrand, Carla Hustedt, Christoph Hubig, et al · 2020
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Monitoring misuse for accountable’artificial intelligence as a service’
Seyyed Ahmad Javadi, Richard Cloete, Jennifer Cobbe, Michelle Seng Ah Lee, and Jatinder Singh · 2020
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Guidance on the ai auditing framework: Draft guidance for consultation, 2020
U.K. Information Commissioner’s Office (ICO) · 2020
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Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts, 2021
European Commission · 2021
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Situated accountability: Ethical principles, certification standards, and explanation methods in applied ai
Anne Henriksen, Simon Enni, and Anja Bechmann · 2021
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Ai cloud service compliance criteria catalogue (aic4)
German Federal Office for Information Security (BSI) · 2021
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Clinical ai: opacity, accountability, responsibility and liability
Helen Smith · 2021
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Accountable artificial intelligence: Holding algorithms to account
Madalina Busuioc · 2021
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Guidance on the ethical development and use of artificial intelligence, 2021
Hong Kong Privacy Commissioner for Personal Data · 2021
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Educating software and ai stakeholders about algorithmic fairness, accountability, transparency and ethics
Veronika Bogina, Alan Hartman, Tsvi Kuflik, and Avital Shulner-Tal · 2021
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The accountability fabric: A suite of semantic tools for managing ai system accountability and audit
Milan Markovic, Iman Naja, Pete Edwards, and Wei Pang · 2021
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Challenges and future directions for accountable machine learning
Agne Zainyte and Wei Pang · 2021
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Accuracy-efficiency trade-offs and accountability in distributed ml systems
A Feder Cooper, Karen Levy, and Christopher De Sa · 2021
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Towards accountability in the use of artificial intelligence for public administrations
Michele Loi and Matthias Spielkamp · 2021
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Datasheets for datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford · 2021
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Data readiness report
Shazia Afzal, C Rajmohan, Manish Kesarwani, Sameep Mehta, and Hima Patel · 2021
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A semantic framework to support ai system accountability and audit
Iman Naja, Milan Markovic, Peter Edwards, and Caitlin Cottrill · 2021
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The subjects and stages of ai dataset development: A framework for dataset accountability
Mehtab Khan and Alex Hanna · 2022
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Accountability Principles for Artificial Intelligence (AP4AI) in the Internal Security Domain: AP4AI Framework Blueprint
B Akhgar, PS Bayerl, K Bailey, R Dennis, H Gibson, S Heyes, A Lyle, A Raven, and F Sampson · 2022
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capai-a procedure for conducting conformity assessment of ai systems in line with the eu artificial intelligence act
Luciano Floridi, Matthias Holweg, Mariarosaria Taddeo, Javier Amaya Silva, Jakob Mökander, and Yuni Wen · 2022
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Ai regulation is (not) all you need
Laura Lucaj, Patrick van der Smagt, and Djalel Benbouzid · 2023
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Accountability in artificial intelligence: what it is and how it works
Claudio Novelli, Mariarosaria Taddeo, and Luciano Floridi · 2023
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Understanding accountability in algorithmic supply chains
Jennifer Cobbe, Michael Veale, and Jatinder Singh · 2023
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Measuring responsible artificial intelligence (rai) in banking: a valid and reliable instrument
John Ratzan and Noushi Rahman · 2023
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AI Risk Management Framework (AI RMF 1.0), 2023
US National Institute of Standards and Technology (NIST) · 2023
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Artificial Intelligence Liability Directive, 2023
European Parliament · 2023
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Accountable ai for healthcare iot systems
Prachi Bagave, Marcus Westberg, Roel Dobbe, Marijn Janssen, and Aaron Yi Ding · 2022
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Ai documentation: A path to accountability
Florian Königstorfer and Stefan Thalmann · 2022
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Accountability in an algorithmic society: relationality, responsibility, and robustness in machine learning
A Feder Cooper, Emanuel Moss, Benjamin Laufer, and Helen Nissenbaum · 2022
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A systematic review of explainable artificial intelligence in terms of different application domains and tasks
Mir Riyanul Islam, Mobyen Uddin Ahmed, Shaibal Barua, and Shahina Begum · 2022
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A clarification of the nuances in the fairness metrics landscape
Alessandro Castelnovo, Riccardo Crupi, Greta Greco, Daniele Regoli, Ilaria Giuseppina Penco, and Andrea Claudio Cosentini · 2022
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An ontology for fairness metrics
Jade S Franklin, Karan Bhanot, Mohamed Ghalwash, Kristin P Bennett, Jamie McCusker, and Deborah L McGuinness · 2022
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NSW Artificial Intelligence Assurance Framework, 2022
Australia NSW Government · 2022
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What is AI Verify?, 2023
AI Verify Foundation · 2023
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Credo ai
Credo AI · 2023
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A taxonomy of trustworthiness for artificial intelligence
Center for Long-Term Cybersecurity (CLTC), University of California, Berkeley · 2023
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Catalogue of Tools & Metrics for Trustworthy AI
OECD.AI · 2023
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Algorithmic Impact Assessment tool , 2023
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Openai’s response to ntia request for comment: Artificial intelligence accountability, 2023
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Fairness, accountability, transparency, and ethics (fate) in artificial intelligence (ai), and higher education: A systematic review
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Microsoft response to ai accountability policy request for comment, ntia-2023–07776, 2023
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“i would like to design”: Black girls analyzing and ideating fair and accountable ai
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Log and monitor azure openai
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Voluntary Code of Conduct on the Responsible Development and Management of Advanced Generative AI Systems
Innovation, Science and Economic Development Canada · 2023
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Operationalising ai governance through ethics-based auditing: an industry case study
Jakob Mökander and Luciano Floridi · 2023
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Reward reports for reinforcement learning
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An empirical study on software bill of materials: Where we stand and the road ahead
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Trust in software supply chains: Blockchain-enabled sbom and the aibom future
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