# A Brief Overview of AI Governance for Responsible Machine Learning Systems

arXiv: https://arxiv.org/abs/2211.13130 · HTML: https://mlsanity.com/paper/2211.13130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 21 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [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)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Data Cards: Purposeful and Transparent Dataset Documentation for Responsible AI](https://mlsanity.com/paper/2204.01075/) (2022)
- [Principles alone cannot guarantee ethical AI](https://mlsanity.com/paper/1906.06668/) (2019)
- [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](https://mlsanity.com/paper/2204.14244/) (2022)
- [The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review](https://mlsanity.com/paper/1511.05263/) (2015)
- [Weakly-Supervised Classification and Detection of Bird Sounds in the Wild. A BirdCLEF 2021 Solution](https://mlsanity.com/paper/2107.04878/) (2021)
- [Recognizing bird species in diverse soundscapes under weak supervision](https://mlsanity.com/paper/2107.07728/) (2021)
- [General Image Descriptors for Open World Image Retrieval using ViT CLIP](https://mlsanity.com/paper/2210.11141/) (2022)
- [Backtesting the predictability of COVID-19](https://mlsanity.com/paper/2007.11411/) (2020)
- [Few-shot Long-Tailed Bird Audio Recognition](https://mlsanity.com/paper/2206.11260/) (2022)

## Abstract (arXiv)

Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies. Research has shown that these risks can range anywhere from regulatory, compliance, reputational, and user trust, to financial and even societal risks. Depending on the nature and size of the organization, AI technologies can pose a significant risk, if not used in a responsible way. This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI with the goal of preventing and mitigating risks. Having such a framework will not only manage risks but also gain maximum value out of AI projects and develop consistency for organization-wide adoption of AI.