Explainable AI

Explainability sounds academic until an exhibitor asks why they got thirty recommendations of the wrong buyer type, and the honest answer is "we don't know, the model decided." At events, AI outputs carry commercial and personal weight — who gets recommended, who appears in whose list, which accounts get retention attention — and unexplained decisions are hard to trust, debug, or defend. Explanation operates at two levels. For participants, "recommended because you both work in cold-chain logistics and they're sourcing what you sell" makes a suggestion credible and measurably more likely to be acted on than an unexplained name. For your team, explanation is diagnostic: when matches skew or a churn model flags your happiest exhibitor, you need to see which signals drove the output to fix the right thing. There's a regulatory layer too — GDPR gives people rights around significant automated decisions, and profiling at events edges into that territory. The common mistake is accepting "our algorithm is proprietary" as a complete answer during procurement; vendors can protect their models and still show which factors drove a given recommendation. One honest nuance: some explanations are decoration — plausible reasons generated after the fact rather than the model's actual logic. Ask whether the explanation reflects what the model computed, and expect some vendors to go quiet.