# Rating Reliability and Bias in News Articles: Does AI Assistance Help Everyone?

arXiv: https://arxiv.org/abs/1904.01531 · HTML: https://mlsanity.com/paper/1904.01531/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 8 of 8)

- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making](https://mlsanity.com/paper/2101.05303/) (2021)
- [In Search of Verifiability: Explanations Rarely Enable Complementary Performance in AI-Advised Decision Making](https://mlsanity.com/paper/2305.07722/) (2023)
- [A Decision Theoretic Framework for Measuring AI Reliance](https://mlsanity.com/paper/2401.15356/) (2024)
- [Ask to Know More: Generating Counterfactual Explanations for Fake Claims](https://mlsanity.com/paper/2206.04869/) (2022)
- [Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild](https://mlsanity.com/paper/2206.02661/) (2022)
- [Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term](https://mlsanity.com/paper/2310.17711/) (2023)
- [Iffy-Or-Not: Extending the Web to Support the Critical Evaluation of Fallacious Texts](https://mlsanity.com/paper/2503.14412/) (2025)

## Abstract (arXiv)

With the spread of false and misleading information in current news, many algorithmic tools have been introduced with the aim of assessing bias and reliability in written content. However, there has been little work exploring how effective these tools are at changing human perceptions of content. To this end, we conduct a study with 654 participants to understand if algorithmic assistance improves the accuracy of reliability and bias perceptions, and whether there is a difference in the effectiveness of the AI assistance for different types of news consumers. We find that AI assistance with feature-based explanations improves the accuracy of news perceptions. However, some consumers are helped more than others. Specifically, we find that participants who read and share news often on social media are worse at recognizing bias and reliability issues in news articles than those who do not, while frequent news readers and those familiar with politics perform much better. We discuss these differences and their implication to offer insights for future research.