This looks like an intriguing paper.
Abstract: It is often assumed by friends and foes alike of intelligent design that a likelihood approach to design inferences will require evidence regarding the specific motives and abilities of any hypothetical designer. Elliott Sober, like Venn before him, indicates that this information is unavailable when the designer is not human (or at least finite) and concludes that there is no good argument for design in biology. I argue that a knowledge of motives and abilities is not always necessary for obtaining a likelihood on design. In many cases, including the case of irreducibly complex objects, frequencies from known agents can supply the likelihood. I argue against the claim that data gathered from humans is inapplicable to non-human agents. Finally, I point out that a broadly Bayesian approach to design inferences, such as that advocated by Sober, is actually advantagous to design advocates in that it frees them from the Popperian requirement that they construct an overarching science which makes high-likelihood predictions.