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

## TL;DR

This study investigates whether AI assistance with explanations can improve human ability to assess bias and reliability in news articles, revealing varied effectiveness among different user groups.

## Contribution

It provides empirical evidence that AI explanations can enhance perception accuracy, highlighting differences based on user news consumption habits.

## Key findings

- AI assistance improves perception accuracy
- Frequent social media users are less accurate in bias detection
- Political familiarity correlates with better news evaluation

## Abstract

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.