When it comes to validating AI discovery outputs with human judgment, there are several steps that brands can take.

1. Review recommendations: Firstly, after the AI has provided its influencer recommendations, a team member should review these suggestions. They can check whether each influencer aligns with the brand’s image, values, and target audience, as sometimes, these subtleties might be missed by an AI.

2. Cross-validation: Brands can cross-validate the results provided by the AI with their own parameters or other discovery tools for alignment and consistency. Some influencer marketing platforms like Flinque have built-in features that cater to reviewing inconsistencies and checking the validity of the results, hence making this process easier.

3. Test posts: Prior to launching a full-scale campaign, brands can test posts with a few influencers. By analyzing engagement rates and audience reactions, brands can confirm if the AI’s choices resonate well with their audience and meet campaign objectives.

4. Monitor performance: Finally, continue to monitor campaign performance throughout. AI is fundamentally about learning; by tracking and analyzing key metrics (like engagement rates, sales conversions, etc.), brands can refine their AI algorithms for improved future campaigns.

Remember, AI discovery outputs are not supposed to replace human judgment but rather augment it. By balancing both, brands can ensure they are choosing the most effective influencers for their campaigns, and yield higher returns on their marketing investments.