# An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research

arXiv: https://arxiv.org/abs/2105.06591 · HTML: https://mlsanity.com/paper/2105.06591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 10 of 10)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learning](https://mlsanity.com/paper/2004.11246/) (2020)
- [Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data](https://mlsanity.com/paper/1809.02519/) (2018)
- [Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective](https://mlsanity.com/paper/2101.03042/) (2021)
- [Leveraging Citation Networks to Visualize Scholarly Influence Over Time](https://mlsanity.com/paper/1611.07135/) (2016)
- [Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view](https://mlsanity.com/paper/2008.03397/) (2020)
- [Improving Fair Predictions Using Variational Inference In Causal Models](https://mlsanity.com/paper/2008.10880/) (2020)
- [Investigating the interplay between fundamentals of national research systems: performance, investments and international collaborations](https://mlsanity.com/paper/1507.08072/) (2015)

## Abstract (arXiv)

There is an increasing interest in ensuring machine learning (ML) frameworks behave in a socially responsible manner and are deemed trustworthy. Although considerable progress has been made in the field of Trustworthy ML (TwML) in the recent past, much of the current characterization of this progress is qualitative. Consequently, decisions about how to address issues of trustworthiness and future research goals are often left to the interested researcher. In this paper, we present the first quantitative approach to characterize the comprehension of TwML research. We build a co-occurrence network of words using a web-scraped corpus of more than 7,000 peer-reviewed recent ML papers -- consisting of papers both related and unrelated to TwML. We use community detection to obtain semantic clusters of words in this network that can infer relative positions of TwML topics. We propose an innovative fingerprinting algorithm to obtain probabilistic similarity scores for individual words, then combine them to give a paper-level relevance score. The outcomes of our analysis inform a number of interesting insights on advancing the field of TwML research.