# Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems

arXiv: https://arxiv.org/abs/2102.04201 · HTML: https://mlsanity.com/paper/2102.04201/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 75 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020)
- [The Security Implications of Data Subject Rights](https://mlsanity.com/paper/1905.13005/) (2019)

## Built on by (most cited first, 30 of 31)

- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Understanding accountability in algorithmic supply chains](https://mlsanity.com/paper/2304.14749/) (2023)
- [Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits](https://mlsanity.com/paper/2205.06922/) (2022)
- [Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"](https://mlsanity.com/paper/2302.01448/) (2023)
- [The Conflict Between Explainable and Accountable Decision-Making Algorithms](https://mlsanity.com/paper/2205.05306/) (2022)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- [Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication](https://mlsanity.com/paper/2302.03460/) (2023)
- [Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice](https://mlsanity.com/paper/2506.09873/) (2025)
- [Accountability Capture: How Record-Keeping to Support AI Transparency and Accountability (Re)shapes Algorithmic Oversight](https://mlsanity.com/paper/2510.04609/) (2025)
- [Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis](https://mlsanity.com/paper/2205.07333/) (2022)
- [Human-AI Governance (HAIG): A Trust-Utility Approach](https://mlsanity.com/paper/2505.01651/) (2025)
- [Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse](https://mlsanity.com/paper/2309.04211/) (2023)
- [Towards Adaptive Categories: Dimensional Governance for Agentic AI](https://mlsanity.com/paper/2505.11579/) (2025)
- [Accountability in AI: From Principles to Industry-specific Accreditation](https://mlsanity.com/paper/2110.09232/) (2021)
- [Accountability Framework for Healthcare AI Systems: Towards Joint Accountability in Decision Making](https://mlsanity.com/paper/2509.03286/) (2025)
- [Towards a Praxis for Intercultural Ethics in Explainable AI](https://mlsanity.com/paper/2304.11861/) (2023)
- [Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions](https://mlsanity.com/paper/2305.11581/) (2023)
- [Towards Meaningful Transparency in Civic AI Systems](https://mlsanity.com/paper/2510.07889/) (2025)
- [Modeling Fairness in Recruitment AI via Information Flow](https://mlsanity.com/paper/2511.13793/) (2025)
- [Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction](https://mlsanity.com/paper/2602.17106/) (2026)
- [To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems](https://mlsanity.com/paper/2604.28053/) (2026)
- [Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure](https://mlsanity.com/paper/2605.01091/) (2026)
- [JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis](https://mlsanity.com/paper/2606.19407/) (2026)
- [Retrofitters, pragmatists and activists: Public interest litigation for accountable automated decision-making](https://mlsanity.com/paper/2511.03211/) (2025)
- [Compliance-by-Construction Argument Graphs: Using Generative AI to Produce Evidence-Linked Formal Arguments for Certification-Grade Accountability](https://mlsanity.com/paper/2604.04103/) (2026)
- [Governed Auditable Decisioning Under Uncertainty: Synthesis and Agentic Extension](https://mlsanity.com/paper/2604.19112/) (2026)
- [Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI](https://mlsanity.com/paper/2607.14353/) (2026)
- [The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems](https://mlsanity.com/paper/2607.19292/) (2026)
- [Governing Agentic AI in FinTech](https://mlsanity.com/paper/2608.11344/) (2026)
- [When Agents Act Unwatched: The Reduced-Supervision Paradox in Agentic AI](https://mlsanity.com/paper/2609.29547/) (2026)

## Abstract (arXiv)

This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an understanding of ADM as a socio-technical process involving both human and technical elements, beginning before a decision is made and extending beyond the decision itself. While explanations and other model-centric mechanisms may assist some accountability concerns, they often provide insufficient information of these broader ADM processes for regulatory oversight and assessments of legal compliance. Reviewability involves breaking down the ADM process into technical and organisational elements to provide a systematic framework for determining the contextually appropriate record-keeping mechanisms to facilitate meaningful review - both of individual decisions and of the process as a whole. We argue that a reviewability framework, drawing on administrative law's approach to reviewing human decision-making, offers a practical way forward towards more a more holistic and legally-relevant form of accountability for ADM.