concept drift
What It Means
Concept drift happens when the real-world patterns your AI system learned during training start changing over time, causing the system's predictions to become less accurate. It's like training a spam filter on 2020 emails and then using it in 2024 - the nature of spam has evolved, so the filter performs poorly on current threats.
Why Chief AI Officers Care
Concept drift directly impacts business ROI as AI systems silently degrade in performance without obvious warning signs, leading to poor decisions based on outdated models. It creates compliance risks in regulated industries where model performance standards must be maintained, and can damage customer trust when recommendations or predictions become irrelevant or biased over time.
Real-World Example
A credit scoring model trained on pre-pandemic data fails to accurately assess loan risk in 2023 because employment patterns, spending behaviors, and economic indicators have fundamentally shifted since COVID-19, resulting in either too many bad loans being approved or too many good customers being rejected.
Common Confusion
People often confuse concept drift with simple data quality issues or think it only happens with dramatic external events like pandemics. In reality, concept drift occurs gradually and continuously in most business domains as customer preferences, market conditions, and behavioral patterns naturally evolve.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, concept drift occurs when patient populations, disease patterns, or clinical practices evolve after mo...
Finance: In finance, concept drift occurs when market conditions, economic environments, or customer behaviors shift, causing AI ...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"Use of a system outside the planned domain of application, and a common cause of performance gaps between laboratory settings and the real world."Source: SP1270
"an online supervised learning scenario when the relation between the input data and the target variable changes over time."Source: Gama,_Joao
"Systems that classify or predict a concept (e.g., credit ratings or computer intrusion monitors) over time can suffer performance loss when the concept they are tracking changes. This is referred to as concept drift. This can either be a natural process that occurs without a reference to the system, or an active process, where others are reacting to the system (e.g., virus detection)."Source: Raynor
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