AI platforms rely on various reasoning methods to process information and generate responses. Among them are three ‘ductives — deductive, inductive, and abductive reasoning. Each draws conclusions from facts, observations, or hypotheses in different ways.[i] While AI excels at deductive and inductive reasoning, it often struggles with abductive reasoning, a form of logical thinking involving inferences based on incomplete information. This human-like ability to draw plausible conclusions from uncertain evidence is crucial for tasks like understanding context, making creative leaps, and solving complex problems.

Consider this: When prompted with, 'If I left the hose out last night and the grass is wet, what is the most likely cause?', an AI might simply respond, 'The hose was left on.' However, a human might also consider other possibilities, such as heavy dew, a sprinkler system, or rain overnight. This ability to explore multiple explanations is a hallmark of abductive reasoning.

Abductive reasoning, for better or worse, powers human intuition, our common sense, our superstitions, and our ability to second-guess ourselves. What we may deem common sense, AI struggles to discern.[ii] While AI can sometimes mimic abductive reasoning, even common sense, we should not rely on it, nor should we give up on our intuition, our suspicions that it may be wrong, or our ability to second-guess its responses.

[i] Farnam Street Blog. “Deductive vs Inductive Reasoning: Make Smarter Arguments, Better Decisions, and Stronger Conclusions” [https://fs.blog/deductive-inductive-reasoning/]

[ii] The Curious Case of Commonsense Intelligence, Daedalus MIT Press Direct [https://direct.mit.edu/daed/article/151/2/139/110627/The-Curious-Case-of-Commonsense-Intelligence]