Final answer:
Peter Galison's remarks on algorithmic unopenable black boxes in criminal justice parallel astronomers' use of the Drake Equation in SETI, which both address estimating unknowable factors.
Step-by-step explanation:
Peter Galison's discussion about algorithmic unopenable black boxes used in the criminal justice system hints at the complexities and potential biases embedded within AI algorithms. These concerns reflect the wider challenges of artificial intelligence and its transparency, particularly in critical applications like criminal justice where the outcome affects human lives. Galison's perspective is akin to the difficulties astronomers face with the Drake Equation, which organizes our ignorance about the probabilities of life elsewhere in the universe by breaking them down into factors that are best guesses rather than precise measures.
The Drake Equation, as a predictive tool, faces similar challenges; we must estimate factors involved in the development of life, intelligent life, and technological civilizations, which like the unopenable black boxes, involve uncertainties and are not directly observable. Thus, in criminal justice as in SETI, we attempt to quantify the unquantifiable, addressing abstract concepts like bias or the likelihood of extraterrestrial civilizations, using available data to inform our guesses. Galison's approach to AI in criminal justice suggests that we should critically assess and attempt to understand potential biases, in much the same way as astronomers use the Drake Equation to quantify aspects of life in the universe that are not yet fully understood.