# Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making

arXiv: https://arxiv.org/abs/2102.00625 · HTML: https://mlsanity.com/paper/2102.00625/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 119 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning](https://mlsanity.com/paper/1902.04783/) (2019)
- [The social dilemma of autonomous vehicles](https://mlsanity.com/paper/1510.03346/) (2015)
- [How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness](https://mlsanity.com/paper/1811.03654/) (2018)
- [Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences](https://mlsanity.com/paper/2001.09604/) (2020)
- [Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability](https://mlsanity.com/paper/1902.03245/) (2019)
- [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017)
- [Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction](https://mlsanity.com/paper/1802.09548/) (2018)
- [Dimensions of Diversity in Human Perceptions of Algorithmic Fairness](https://mlsanity.com/paper/2005.00808/) (2020)
- [Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda](https://mlsanity.com/paper/2002.09054/) (2020)
- [Demographics and discussion influence views on algorithmic fairness](https://mlsanity.com/paper/1712.09124/) (2017)
- [Responsible AI and Its Stakeholders](https://mlsanity.com/paper/2004.11434/) (2020)

## Built on by (most cited first, 23 of 23)

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- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey](https://mlsanity.com/paper/2407.08867/) (2024)
- [On Blame Attribution for Accountable Multi-Agent Sequential Decision Making](https://mlsanity.com/paper/2107.11927/) (2021)
- [The Conflict Between Explainable and Accountable Decision-Making Algorithms](https://mlsanity.com/paper/2205.05306/) (2022)
- [Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns](https://mlsanity.com/paper/2111.09478/) (2021)
- [Towards a Roadmap on Software Engineering for Responsible AI](https://mlsanity.com/paper/2203.08594/) (2022)
- [AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions](https://mlsanity.com/paper/2304.08804/) (2023)
- [Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions](https://mlsanity.com/paper/2503.00940/) (2025)
- [A Critical Examination of the Ethics of AI-Mediated Peer Review](https://mlsanity.com/paper/2309.12356/) (2023)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [Blaming Humans and Machines: What Shapes People's Reactions to Algorithmic Harm](https://mlsanity.com/paper/2304.02176/) (2023)
- [How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey](https://mlsanity.com/paper/2403.14496/) (2024)
- [TwIPS: A Large Language Model Powered Texting Application to Simplify Conversational Nuances for Autistic Users](https://mlsanity.com/paper/2407.17760/) (2024)
- [Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making](https://mlsanity.com/paper/2505.18066/) (2025)
- [Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment](https://mlsanity.com/paper/2509.04445/) (2025)
- [Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research](https://mlsanity.com/paper/2312.12119/) (2023)
- [AI Ethics and Social Norms: Exploring ChatGPT's Capabilities From What to How](https://mlsanity.com/paper/2504.18044/) (2025)
- [Does AI and Human Advice Mitigate Punishment for Selfish Behavior? An Experiment on AI ethics From a Psychological Perspective](https://mlsanity.com/paper/2507.19487/) (2025)
- [Confronting Label Indeterminacy in Automated Bail Decisions](https://mlsanity.com/paper/2605.04073/) (2026)
- [RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview](https://mlsanity.com/paper/2606.00011/) (2026)
- [Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models](https://mlsanity.com/paper/2606.13441/) (2026)
- [Method, Mind, and Morality: How People Make Sense of Artificial Intelligence](https://mlsanity.com/paper/2608.24748/) (2026)

## Abstract (arXiv)

How to attribute responsibility for autonomous artificial intelligence (AI) systems' actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people's perceptions of eight different notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful difference in how people perceived these agents' moral responsibility; human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these findings, such as the need for explainable AI in high-stakes scenarios.