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X-WR-CALNAME:LatentShift · Developing and Running AI Models in Critical Sy
 stems: How Do We Design Explainable\, Auditable\, and Reliable Models?
X-WR-TIMEZONE:Europe/Istanbul
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UID:ed01-kritik-sistemlerde-ai-modeli-gelistirmek@latentshift.ai
DTSTAMP:20261007T125617Z
DTSTART:20261017T093000Z
DTEND:20261017T095000Z
SUMMARY:LatentShift · Developing and Running AI Models in Critical Systems
 : How Do We Design Explainable\, Auditable\, and Reliable Models? · Fatih
  Bildirici
DESCRIPTION:Short talk · 20 min · Turkish\n\nFatih Bildirici · Senior Su
 bject Matter Expert\, ASELSAN\n\nTurkish Sign Language (TİD) interpretati
 on.\n\nThis talk is built around a fundamental lesson learned while deploy
 ing an AI model to production in a critical business process: a model prod
 ucing accurate predictions does not mean the system operates reliably. In 
 mission-critical / life-critical fields such as defense\, healthcare\, avi
 ation\, energy\, transportation\, and industrial control\, a model's outpu
 t becomes more than a score or a classification result. It becomes an oper
 ational signal that influences an alarm\, a human decision\, a procedure\,
  a risk level\, or fallback behavior.\n\nWithout sharing confidential deta
 ils about organizations\, data\, or systems\, the talk will describe the t
 echnical failure modes that can emerge when an AI model moves from a test 
 environment into a production decision chain. Initially\, the model's offl
 ine metrics appear acceptable: accuracy\, precision/recall\, latency\, and
  service response times meet their targets. But when real use begins\, the
  problem extends beyond model performance. The data distribution changes\,
  confidence scores become miscalibrated for some input segments\, explanat
 ions begin to differ for similar cases\, human intervention increases\, an
 d the system continues to process low-confidence outputs as candidates for
  decisions.\n\nBased on this experience\, the technical backbone of our ta
 lk will follow this flow:\n\ndata validation → feature pipeline → mode
 l inference → confidence / uncertainty estimation → explanation layer 
 → context check → risk gate → human review / fallback → monitoring
  → audit trail\n\nWithin this flow\, explainability will be treated as a
  control layer that conveys the model's limits to the system\, rather than
  a UI panel added later. An explanation will do more than answer "why did 
 the model produce this result?" It will also support the following signals
 : does the model recognize this data regime\, is its confidence truly reli
 able\, is the explanation stable\, is there drift\, are humans overriding 
 this output\, and when should the system give up on automated decision-mak
 ing?\n\nThe talk will examine these production lessons in technical detail
 : high confidence does not imply operational safety\, offline success and 
 production stability are different things\, explanation stability must be 
 monitored\, human-in-the-loop design cannot be just an approval screen\, a
 nd fallback/abstention mechanisms must be designed within the production d
 ecision chain\, rather than outside the model architecture.\n\nThe signals
  to monitor will go beyond accuracy\, precision\, recall\, p95 latency\, a
 nd cost/request. The talk will show why metrics such as expected calibrati
 on error\, confidence drift\, uncertainty distribution\, explanation drift
 \, explanation stability\, human override rate\, abstention rate\, fallbac
 k activation rate\, regression rate\, and audit completeness must also be 
 included.\n\nThe main lesson of the talk will be this: reliable AI in crit
 ical systems is more than a model that makes good predictions. Reliable AI
  is a system that knows when it is uncertain\, can explain its decisions\,
  controls risk\, hands over to a human when needed\, stops when needed\, a
 nd leaves a trace of every decision.\n\nhttps://latentshift.ai/schedule/kr
 itik-sistemlerde-ai-modeli-gelistirmek/
LOCATION:Bağlarbaşı Kongre ve Kültür Merkezi\, Boğaziçi Salonu\, Sel
 ami Ali Mah.\, Gazi Cad. No:22\, 34664 Üsküdar\, İstanbul
GEO:41.024366;29.033571
URL:https://latentshift.ai/schedule/kritik-sistemlerde-ai-modeli-gelistirme
 k/
CATEGORIES:LatentShift,Short talk
STATUS:CONFIRMED
TRANSP:OPAQUE
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DESCRIPTION:Starts in 10 minutes · Developing and Running AI Models in Cri
 tical Systems: How Do We Design Explainable\, Auditable\, and Reliable Mod
 els?
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