# A Mulching Proposal

arXiv: https://arxiv.org/abs/1908.06166 · HTML: https://mlsanity.com/paper/1908.06166/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2019.

## Built on by (most cited first, 14 of 14)

- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [NLPositionality: Characterizing Design Biases of Datasets and Models](https://mlsanity.com/paper/2306.01943/) (2023)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits](https://mlsanity.com/paper/2102.04256/) (2021)
- [Towards a multi-stakeholder value-based assessment framework for algorithmic systems](https://mlsanity.com/paper/2205.04525/) (2022)
- [Auditing of AI: Legal, Ethical and Technical Approaches](https://mlsanity.com/paper/2407.06235/) (2024)
- ["I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI](https://mlsanity.com/paper/2410.08526/) (2024)
- [Responsible AI Research Needs Impact Statements Too](https://mlsanity.com/paper/2311.11776/) (2023)
- [Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor](https://mlsanity.com/paper/2506.14652/) (2025)
- [Axes for Sociotechnical Inquiry in AI Research](https://mlsanity.com/paper/2105.06551/) (2021)
- [How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India](https://mlsanity.com/paper/2205.05661/) (2022)
- [The Ethics of AI Value Chains](https://mlsanity.com/paper/2307.16787/) (2023)
- [Slurry-as-a-Service: A Modest Proposal on Scalable Pluralistic Alignment for Nutrient Optimization](https://mlsanity.com/paper/2603.02420/) (2026)
- [Moral Attitudes of Sentient ASI towards Humanity and Implications for AGI Development](https://mlsanity.com/paper/2607.14998/) (2026)

## Abstract (arXiv)

he ethical implications of algorithmic systems have been much discussed in both HCI and the broader community of those interested in technology design, development and policy. In this paper, we explore the application of one prominent ethical framework - Fairness, Accountability, and Transparency - to a proposed algorithm that resolves various societal issues around food security and population ageing. Using various standardised forms of algorithmic audit and evaluation, we drastically increase the algorithm's adherence to the FAT framework, resulting in a more ethical and beneficent system. We discuss how this might serve as a guide to other researchers or practitioners looking to ensure better ethical outcomes from algorithmic systems in their line of work.