A5012389632- Ethics and Social Impacts of AI
- Artificial Intelligence in Healthcare and Education
- Machine Learning in Healthcare
- Innovative Human-Technology Interaction
- Sepsis Diagnosis and Treatment
- Information Systems Theories and Implementation
- Ethics in Clinical Research
- Clinical Reasoning and Diagnostic Skills
- Digital Games and Media
- Neuroethics, Human Enhancement, Biomedical Innovations
- Human-Automation Interaction and Safety
- Military and Defense Studies
- Innovation, Sustainability, Human-Machine Systems
- Generative Adversarial Networks and Image Synthesis
- Corporate Social Responsibility Reporting
- Global Security and Public Health
- Geographies of human-animal interactions
- University-Industry-Government Innovation Models
- Artificial Intelligence in Law
- Smart Cities and Technologies
- Robotic Path Planning Algorithms
- Technology, Environment, Urban Planning
- Multimodal Machine Learning Applications
- Anthropology: Ethics, History, Culture
- Social Robot Interaction and HRI
Google (United Kingdom)
2024
Google (United States)
2015-2022
Data & Society Research Institute
2017-2021
Internet Society
2015-2021
University of Oxford
2015-2021
Harvard University Press
2019
Columbia University
2010-2017
"Big Data" and "artificial intelligence" have captured the public imagination are profoundly shaping social, economic, political spheres. Through an interrogation of histories, perceptions, practices that shape these technologies, we problematize myths animate supposed "magic" systems. In face increasingly widespread blind faith in data-driven argue for grounding machine learning-based untethering them from hype fear cycles. One path forward is to develop a rich methodological framework...
As debates about the policy and ethical implications of AI systems grow, it will be increasingly important to accurately locate who is responsible when agency distributed in a system control over an action mediated through time space. Analyzing several high-profile accidents involving complex automated socio-technical media coverage that surrounded them, I introduce concept moral crumple zone describe how responsibility for may misattributed human actor had limited behavior or autonomous...
Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less focus real world implementation and the associated challenges to fairness, transparency, accountability that come from actual, situated use. Serious questions remain underexamined regarding how ethically build interpret explain model output, recognize account biases, minimize disruptions professional expertise work...
Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum: the increased attention comes partly with view that participation opens gateway an inclusive, equitable, robust, responsible trustworthy AI. Among other benefits, participatory essential understanding adequately representing needs, desires perspectives of historically marginalized communities. However, there currently exists lack clarity on what meaningful entails it is expected do. In...
Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption poorly characterized in the literature.This study aims report a quality improvement effort integrate deep sepsis detection management platform, Sepsis Watch, care.In 2016, multidisciplinary team consisting statisticians, data scientists, engineers, clinicians was assembled by leadership an academic health system radically improve treatment sepsis. This follows framework...
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. They modeled after in other domains. Our study the history shows "impacts" evaluative construct enable actors to identify ameliorate harms experienced because a policy decision or system. Every domain has different expectations norms around what constitutes impacts harms, how potential rendered as particular undertaking, who is responsible...
The Algorithmic Impact Assessment is a new concept for regulating algorithmic systems and protecting the public interest. Assembling Accountability: Public Interest report that maps challenges of constructing impact assessments (AIAs) provides framework evaluating effectiveness current proposed AIA regimes. This practical tool regulators, advocates, public-interest technologists, technology companies, critical scholars who are identifying, assessing, acting upon harms.First, authors Emanuel...
The wide‐spread deployment of machine learning tools within healthcare is on the horizon. However, hype around “AI” tends to divert attention toward spectacular, and away from more mundane ground‐level aspects new technologies that shape technological adoption integration. This paper examines development a learning‐driven sepsis risk detection tool in hospital Emergency Department order interrogate contingent deeply contextual ways which AI are likely be adopted healthcare. In particular,...
As debates about the policy and ethical implications of AI systems grow, it will be increasingly important to accurately locate who is responsible when agency distributed in a system control over an action mediated through time space. Analyzing several high-profile accidents involving complex automated socio-technical media coverage that surrounded them, I introduce concept moral crumple zone describe how responsibility for may misattributed human actor had limited behavior or autonomous...
This article analyzes US drone operations through a historical and ethnographic analysis of the remote split paradigm used by Air Force. Remote refers to globally distributed command control entails network human operators analysts in Middle East, Europe, Southeast Asia as well continental United States. Though often viewed teleological progression “unmanned” warfare, this paper argues that historically specific technopolitical logics establish conditions possibility for work war be...
What will happen to current regimes of liability when driverless cars become commercially available? happens there is no human actor — only a computational agent responsible for an accident? This white paper addresses these questions by examining the historical emergence and response technologies autopilot cruise control. Through examination technical, social legal histories, we observe counter-intuitive focus on responsibility even while action increasingly replaced automation. We argue...
Machine learning technologies are increasingly developed for use in healthcare. While research communities have focused on creating state-of-the-art models, there has been less focus real world implementation and the associated challenges to accuracy, fairness, accountability, transparency that come from actual, situated use. Serious questions remain under examined regarding how ethically build interpret explain model output, recognize account biases, minimize disruptions professional...
Algorithmic decision-making and decision-support systems (ADS) are gaining influence over how society distributes resources, administers justice, provides access to opportunities. Yet collectively we do not adequately study these affect people or document the actual potential harms resulting from their integration with important social functions. This is a significant challenge for computational justice efforts of measuring governing AI systems. Impact assessments often used as instruments...
Algorithmic impact assessments (AIA) are increasingly being proposed as a mechanism for algorithmic accountability. These seen potentially useful anticipating, avoiding, and mitigating the negative consequences of decision-making systems (ADS). At same time, what an AIA would entail remains under-specified. While promising, AIAs raise many questions they answer. Choices about methods, scope, purpose structure possible governance outcomes. Decisions type effects count impact, when impacts...
This paper examines the under-explored role of video demos as a consequential form communicating tangible computing research. Examining production demos, in particular two videos from MIT's Media Lab, I explore ethical complications producing demo by bringing to surface tensions inherent representations research, understood social studies technology perspective. In doing so, this hopes generate discussion about challenges and responsibilities creating accurate compelling narratives around
Algorithmically mediated systems and tools are used by workers across the globe. Many of these in low-power positions, where they have little leverage to make demands around transparency, explanation, or terms use, yet, at same time rely deeply on for many aspects their jobs. This tension between power high reliance drives production intensive algorithmic imaginaries, engage meaning-making construct understandings systems. Yet, there has been attention paid diversity ingenuity crafted...
<sec> <title>BACKGROUND</title> Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption poorly characterized in the literature. </sec> <title>OBJECTIVE</title> This study aims report a quality improvement effort integrate deep sepsis detection management platform, Sepsis Watch, care. <title>METHODS</title> In 2016, multidisciplinary team consisting statisticians, data scientists, engineers, clinicians was assembled by...