# Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)

arXiv: https://arxiv.org/abs/2501.11705 · HTML: https://mlsanity.com/paper/2501.11705/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [Synthetic Data Generation with LLM for Improved Depression Prediction](https://mlsanity.com/paper/2411.17672/) (2024)
- [The Unseen AI Disruptions for Power Grids: LLM-Induced Transients](https://mlsanity.com/paper/2409.11416/) (2024)
- [Data Collection and Labeling Techniques for Machine Learning](https://mlsanity.com/paper/2407.12793/) (2024)
- [Comparison of Open-Source and Proprietary LLMs for Machine Reading Comprehension: A Practical Analysis for Industrial Applications](https://mlsanity.com/paper/2406.13713/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systems](https://mlsanity.com/paper/2512.05411/) (2025)

## Abstract (arXiv)

This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.