Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.

Recent research has explored computational tools to manage workplace stress via personal sensing, a measurement paradigm in which behavioral data streams are collected from technologies including smartphones, wearables, and personal computers. As these tools develop, they invite inquiry into how they can be appropriately implemented towards improving workers' well-being. In this study, we explored this proposition through formative interviews followed by a design provocation centered around measuring burnout in a U.

Obligations to assess: Recent trends in AI accountability regulations.

Policymakers are increasingly turning toward assessments of social, economic, and ethical impacts as a governance model for automated decision systems in sensitive or regulated domains. In both the United States and the European Union, recently proposed legislation would require developers to assess the impacts of their systems for individuals, communities, and society, a notable step beyond the technical assessments that are familiar to the industry. This paper analyzes four examples of such legislation in order to illustrate how AI regulations are moving toward using accountability documentation to address common AI accountability concerns: identifying and documenting harms, public transparency, and anti-discrimination rules.

A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability.

In this perspective, we develop a matrix for auditing algorithmic decision-making systems (ADSs) used in the hiring domain. The tool is a socio-technical assessment of hiring ADSs that is aimed at surfacing the underlying assumptions that justify the use of an algorithmic tool and the forms of knowledge or insight they purport to produce. These underlying assumptions, it is argued, are crucial for assessing not only whether an ADS works "as intended," but also whether the intentions with which the tool was designed are well founded.

High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response.

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.

Contextual Analysis of Social Media: The Promise and Challenge of Eliciting Context in Social Media Posts with Natural Language Processing.

While natural language processing affords researchers an opportunity to automatically scan millions of social media posts, there is growing concern that automated computational tools lack the ability to understand context and nuance in human communication and language. This article introduces a critical systematic approach for extracting culture, context and nuance in social media data. The Contextual Analysis of Social Media (CASM) approach considers and critiques the gap between inadequacies in natural language processing tools and differences in geographic, cultural, and age-related variance of social media use and communication.