ID is an abductive theory

Charles Sanders Peirce (1839-1914)

In intelligent design detection (ID) the notion of plausibility is important. This is because ID has an abductive core.

Abduction is a cognitive procedure of accepting hypotheses. From Wikipedia:

Abduction is a form of logical inference that goes from data description of something to a hypothesis that accounts for the reliable data and seeks to explain relevant evidence. The term was first introduced by the American philosopher Charles Sanders Peirce (1839–1914) as "guessing". Peirce said that to abduce a hypothetical explanation a from an observed surprising circumstance b is to surmise that a may be true because then b would be a matter of course. Thus, to abduce a from b involves determining that a is sufficient (or nearly sufficient), but not necessary, for b. For example, the lawn is wet. But if it rained last night, then it would be unsurprising that the lawn is wet. Therefore, by abductive reasoning, the possibility that it rained last night is reasonable. (But note that Peirce did not remain convinced that a single logical form covers all abduction.) Peirce argues that good abductive reasoning from P to Q involves not simply a determination that, e.g., Q is sufficient for P, but also that Q is among the most economical explanations for P. Simplification and economy are what call for the 'leap' of abduction. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. Abductive reasoning can be understood as "inference to the best explanation".

And here goes my translation from another article on abduction here.

Peirce considered abduction together with induction and deduction, thinking that researchers by filtering out implausible hypotheses realise their abductive instinct that is key to scientific inquiry. According to Peirce, scientific methodology is an interaction between:

1. abduction which accepts plausible explanatory hypotheses; 
2. induction which empirically tests the formulated hypotheses; 
3. deduction that allows one to draw useful conclusions based on those hypotheses. 

So Peirce came up with a rough outline of a theory of reasoning which was later developed as part of Artificial intelligence where abduction plays a role in the automated generation of plausible explanations [Institute of Philosophy of Russian Academy of Sciences, Abduction].

Analysing the set H of possible hypotheses regarding the generation of a set К of configurations of matter in an arbitrary system, in line with abductive reasoning we should accept only those hypotheses which comply with a suitably defined plausibility criterion, e.g. [Abel 2009]. The abductive core of ID allows it to satisfy Occam's Rule. Indeed, in certain cases a set of plausible hypotheses H* ⊂ H includes only hypotheses based on intelligent purposive activity leading to the generation of particular configurations in К. This means that the set E of statements that constitute an abductive explanation of К is of minimum size. Put differently, under specific circumstances ID infers to purposive design as the best (i.e. most economical) explanation in a given context. Note that explanations involving the other types of causation (chance contingency and law-like necessity) are retained if they meet the plausibility criterion.

One may wonder why ID reasoning is weaker than what we usually call 'the law of nature' but is formulated only at the level of abduction to the most plausible explanation. I think this is because the start of protolife was a single event in the whole natural terrestrial history. We have no scientific means of knowing exactly how life started. We can at most make suppositions by abduction to the most plausible cause or causes of the appearance of the protocell.

The contribution of ID is two-fold:

• it demonstrates why the most plausible cause of life is intelligence, extraneous to once and for all initiated biological processes in biosystems, and
• it works out a concrete estimate of the functional information which, with as much statistical rigour as we can possibly get for something concerning deep past of natural terrestrial history, requires intelligent purposive intervention.

References

1. Institute of Philosophy, Russian Academy of Sciences.
2. David L. Abel (2009),  The Universal Plausibility Metric (UPM) & Principle (UPP). Theoretical Biology and Medical Modelling, 6:27.