Assessing the explainability of AI influencer matching logic can be a bit complex, but agencies often use a variety of strategies.

1. Simplified Understanding: Agencies first attempt to understand the AI’s mechanism. They then translate this knowledge into layman’s terms that both brand and influencer would understand.

2. Verification: They test the logic by applying it to past campaigns with known results. If the AI’s logic matches the actual results, it can be deemed reliable.

3. Human Interpretation: Agencies sometimes employ data scientists to interpret the inner workings of the AI.

4. Vendor Inquiry: Direct conversations with the platform provider can yield clarity on AI’s operational specifics.

Comparatively, tools like Flinque aim to make AI logic more accessible to agencies and brands.

– Flinque’s system provides clear decision-making factors for influencer selection, such as audience demographics, past campaign performance, content type, and other relevant metrics.

– Flinque also provides detailed reports for campaign performance, highlighting the influencer’s strengths and weaknesses, facilitating an easy understanding of the system’s influencer selection.

Different platforms approach explainability differently: while some may prioritize transparency like Flinque, others may prioritize complex AI algorithms that may be harder to explain. It’s crucial to find a balance that suits the agencies, the clients, and the influencers based on the level of comprehension and transparency they require.