One of the perennial problems we face in a system safety program is how to come up with a convincing proof for the proposition that a system is safe. Because it’s hard to prove a negative (in this case the absence of future accidents) the usual approach is to pursue a proof by contradiction, that is develop the negative proposition that the system is unsafe, then prove that this is not true, normally by showing that the set of identified specific propositions of `un-safety’ have been eliminated or controlled to an acceptable level. Enter the term `hazard’, which in this context is simply shorthand for a specific proposition about the unsafeness of a system. Now interestingly when we parse the set of definitions of hazard we find the recurring use of terms like, ‘condition’, ‘state’, ‘situation’ and ‘events’ that should they occur will inevitably lead to an ‘accident’ or ‘mishap’. So broadly speaking a hazard is a causal explanation, based on a defined set of phenomena, that argues that if they are present and given a relevant domain `law’ there will be an accident. All of which seems to indicate that hazards belong to a class of explanatory models called covering laws. As an explanatory class Covering laws models were developed by the logical positivist philosophers Hempel and Popper because of what they saw as problems with an over reliance on inductive arguments as to causality.
As a covering law explanation of unsafeness a hazard posits phenomenological facts (system states, human errors, hardware/software failures and so on) that confer what’s called nomic expectability on the accident (the thing being explained). That is, the phenomenological facts combined with some covering law (natural and logical), require the accident to happen, and this is what we call a hazard. We can see an archetypal example in the Source-Mechanism-Outcome model of Swallom , i.e. if we have all three elements in that model then we may expect an accident (Ericson 2005). While logical positivism had the last nails driven into it’s coffin by Kuhn and others in the 1960s, and while it’s true, as Kuhn and others pointed out, that covering model explanations have their fair share of problems so to do other methods (1). The one advantage that covering models do possess over other explanatory models is that they largely avoid the problems of causal arguments, as their makers intended. Which may well be why they persist in engineering arguments about safety.
1. Such as counterfactual, statistical relevance and causal explanations.
Ericson, C.A. Hazard Analysis Techniques for System Safety, page 93, John Wiley and Sons, Hoboken, New Jersey, 2005.