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In this paper, we outline a new method for evaluating the human impact of machine-learning (ML) applications. In partnership with Underwriters Laboratories Inc., we have developed a framework to evaluate the impacts of a particular use of machine learning that is based on the goals and values of the domain in which that application is deployed. By examining the use of artificial intelligence (AI) in particular domains, such as journalism, criminal justice, or law, we can develop more nuanced and practically relevant understandings of key ethical guidelines for artificial intelligence. By decoupling the extraction of the facts of the matter from the evaluation of the impact of the resulting systems, we create a framework for the process of assessing impact that has two distinctly different phases.

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Limitations on an analysis of the data can be imposed by the nature of the diffraction pattern; background determination and the separation of peaks with severe overlap in complex patterns (J.I. Langford et aZ~ Powder Diff, 1986, l, 211) and a paucity of lines all affect the results of Profile refinement. Inadequacy of the function used to model neak shapes is often a source of systematic error (R.A. Young & D.B. Wiles, J Appl Cryst, 1982,15,430), as is the model used in any size-strain analySTs.

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