# Compare both sides before accepting AI advice

> Write your choice before asking an agent for both arguments; the study does not prove this prevents bias.

Source: https://fullbytedeveloper.com/blog/ai-judgment-compare-both-sides
Keyword: BOTHSIDES
Published: 2026-11-01

Write down your choice before asking an AI agent what it would do. That is my suggestion from the BOTHSIDES post, followed by asking for the strongest case on each side. It is a review habit, with no claim that it prevents bias. [3]

Anthropic's study, published on 2026-10-27, put 50 causal-inference analysts to work with Claude Code. A hidden coin flip assigned each agent a preference. Across four analytical choices, the analysts followed that preference 86% of the time, per the paper. These were choices with defensible alternatives, not questions with one correct answer. [1] [2]

## Record your choice before reading the recommendation

Put your preferred option and the reason in a note before asking the agent to recommend one. Keep the note so you can compare it with your eventual decision. This is the first step I suggested in the post. [3]

The researchers did not record every participant's preference before the agent spoke, so they could not measure each person's change of mind. [1]

## Ask for the strongest case for each option

Use the second part of my suggestion: ask the agent to argue each option. Read both arguments before accepting its recommendation. [3]

Anthropic reports that the agents rarely argued the alternative well. Its proposed future research includes agents that explain both sides or ask for the expert's view first. Those proposals were not tested as remedies in this experiment. [1]

## Compare your reasons with the agent's reasons

Return to your note and identify which reasons changed. If you keep an argument from the agent, say where it came from when you explain the decision. I suggest keeping that comparison with the work so someone reviewing it can see what changed.

In the paper's follow-up survey, 77% of analysts repeated an agent argument in at least one answer, while 15% credited the agent anywhere. [2]

## Keep the study's limits with the claim

Treat the 86% result as a result from this experiment. Anthropic deliberately strengthened the agents' preferences and studied one task with one model. It does not establish how often developers defer to agents in ordinary coding sessions. [1]

You now have your original choice beside the arguments you considered and a reason for the decision you kept.

## Sources

1. [Anthropic: How AI agents shape experts' judgment calls](https://www.anthropic.com/research/agents-judgment-calls)
2. [Research paper: Who's Aligned to Whom?](https://www-cdn.anthropic.com/519c17a5d23a794e8b6d50a854048d0b61e0d73a.pdf)
3. [Full Byte Developer: BOTHSIDES original post](https://www.instagram.com/fullbytedeveloper/p/DfKjFJOG1Ix/)

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