________________________________________________________________________________________ Artificial Intelligence
■ Too costly to be wrong - In high-stakes situations , the cost of being wrong is too high - financially , for human safety , and for environmental impact . Probabilistic models ( all correlationbased AI models ) do not provide the needed dependability in these cases .
■ Varying delay between cause and effect - Correlation-based AI models work best when the cause and effect are very closely tied together temporally . This breaks down in situations where aging and stress develop over time , or corrosive micro-organisms lie dormant for extended periods .
■ Lack of explainability - Even when AI produces an accurate result , it does not provide insight into how that answer was determined , which lowers engineers ’ confidence in AI ’ s predictions .
Causal AI as an alternative
Given these challenges , it ’ s crucial to develop AI models that offer more than just correlations . Causation-based AI , also known as causal inference or causal AI , is a sophisticated alternative . It identifies cause and effect relationships , enhancing reliability and explainability . Understanding root causes enables better anticipation of future effects .
Unique additions to causal AI : SME insight
A recent method of expanding causal AI models ’ capability is to include subject matter experts ’ ( SMEs ) knowledge and intuition . SME insights , derived from personally analyzing extreme situations and developing hypotheses based on experience , can be translated into code
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