# Filling gaps in trustworthy development of AI

arXiv: https://arxiv.org/abs/2112.07773 · HTML: https://mlsanity.com/paper/2112.07773/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 1 of 1)

- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)

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

- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://mlsanity.com/paper/2209.07858/) (2022)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Plex: Towards Reliability using Pretrained Large Model Extensions](https://mlsanity.com/paper/2207.07411/) (2022)
- [Responsible Reporting for Frontier AI Development](https://mlsanity.com/paper/2404.02675/) (2024)
- [Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios](https://mlsanity.com/paper/2407.19760/) (2024)
- [Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study](https://mlsanity.com/paper/2305.18307/) (2023)
- [Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting](https://mlsanity.com/paper/2501.14778/) (2025)
- [Lessons from the trenches on evaluating machine-learning systems in materials science](https://mlsanity.com/paper/2503.10837/) (2025)
- [Anecdoctoring: Automated Red-Teaming Across Language and Place](https://mlsanity.com/paper/2509.19143/) (2025)
- [Incorporating AI incident reporting into telecommunications law and policy: Insights from India](https://mlsanity.com/paper/2509.09508/) (2025)
- [Automating In-Network Machine Learning](https://mlsanity.com/paper/2205.08824/) (2022)
- [Assistive AI for Augmenting Human Decision-making](https://mlsanity.com/paper/2410.14353/) (2024)
- [FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection](https://mlsanity.com/paper/2502.02290/) (2025)
- [Emergent evaluation hubs in a decentralizing large language model ecosystem](https://mlsanity.com/paper/2510.01286/) (2025)

## Abstract (arXiv)

The range of application of artificial intelligence (AI) is vast, as is the potential for harm. Growing awareness of potential risks from AI systems has spurred action to address those risks, while eroding confidence in AI systems and the organizations that develop them. A 2019 study found over 80 organizations that published and adopted "AI ethics principles'', and more have joined since. But the principles often leave a gap between the "what" and the "how" of trustworthy AI development. Such gaps have enabled questionable or ethically dubious behavior, which casts doubts on the trustworthiness of specific organizations, and the field more broadly. There is thus an urgent need for concrete methods that both enable AI developers to prevent harm and allow them to demonstrate their trustworthiness through verifiable behavior. Below, we explore mechanisms (drawn from arXiv:2004.07213) for creating an ecosystem where AI developers can earn trust - if they are trustworthy. Better assessment of developer trustworthiness could inform user choice, employee actions, investment decisions, legal recourse, and emerging governance regimes.