This article modifies an old archaeological adage-''excavation is destruction''-to demonstrate how advances in archaeological practice suggest a new iteration: ''excavation is digitization.'' Digitization, in a fully digital paradigm, refers to practices that leverage advances in onsite, image-based modeling and volumetric recording, integrated databases, and data sharing. Such practices were implemented in 2014 during the inaugural season of the Kaymakç ı Archaeological Project (KAP) in western Turkey. The KAP recording system, developed from inception before excavation as a digital workflow, increases accuracy and efficiency as well as simplicity and consistency. The system also encourages both practical and conceptual advances in archaeological practice. These involve benefits associated with thinking volumetrically, rather than in two dimensions, and a connectivity that allows for group decision-making regardless of group location. Additionally, it is hoped that the system's use of almost entirely ''off-the-shelf'' solutions will encourage its adoption or at least its imitation by other projects.

Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. They are modeled after impact assessments in other domains. Our study of the history of impact assessments shows that "impacts" are an evaluative construct that enable actors to identify and ameliorate harms experienced because of a policy decision or system. Every domain has different expectations and norms around what constitutes impacts and harms, how potential harms are rendered as impacts of a particular undertaking, who is responsible for conducting such assessments, and who has the authority to act on them to demand changes to that undertaking. By examining proposals for AIAs in relation to other domains, we find that there is a distinct risk of constructing algorithmic impacts as organizationally understandable metrics that are nonetheless inappropriately distant from the harms experienced by people, and which fall short of building the relationships required for effective accountability. As impact assessments become a commonplace process for evaluating harms, the FAccT community, in its efforts to address this challenge, should A) understand impacts as objects that are co-constructed accountability relationships, B) attempt to construct impacts as close as possible to actual harms, and C) recognize that accountability governance requires the input of various types of expertise and affected communities. We conclude with lessons for assembling cross-expertise consensus for the co-construction of impacts and building robust accountability relationships.

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The COVID-19 pandemic has, in a matter of a few short months, drastically reshaped society around the world. Because of the growing perception of machine learning as a technology capable of addressing large problems at scale, machine learning applications have been seen as desirable interventions in mitigating the risks of the pandemic disease. However, machine learning, like many tools of technocratic governance, is deeply implicated in the social production and distribution of risk and the role of machine learning in the production of risk must be considered as engineers and other technologists develop tools for the current crisis. This paper describes the coupling of machine learning and the social production of risk, generally, and in pandemic responses specifically. It goes on to describe the role of risk management in the effort to institutionalize ethics in the technology industry and how such efforts can benefit from a deeper understanding of the social production of risk through machine learning.

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