Emanuel Moss

Orcid: 0000-0002-3850-2677

According to our database1, Emanuel Moss authored at least 23 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Tackling AI Hyping.
AI Ethics, August, 2024

Introducing v0.5 of the AI Safety Benchmark from MLCommons.
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CoRR, 2024

Materiality and Risk in the Age of Pervasive AI Sensors.
CoRR, 2024

Practicing Inclusivity in AI: Stakeholder Engagement Policy in Action.
Proceedings of the Companion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing, 2024

The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology.
Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

2023
Trust Is Not Enough: Accuracy, Error, Randomness, and Accountability in an Algorithmic Society.
Commun. ACM, June, 2023

Taking Algorithms to Courts: A Relational Approach to Algorithmic Accountability.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

2022
A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability.
Patterns, 2022

Obligations to assess: Recent trends in AI accountability regulations.
Patterns, 2022

Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians.
Proc. ACM Hum. Comput. Interact., 2022

The Objective Function: Science and Society in the Age of Machine Intelligence.
CoRR, 2022

A relationship and not a thing: A relational approach to algorithmic accountability and assessment documentation.
CoRR, 2022

Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Participation Is not a Design Fix for Machine Learning.
Proceedings of the Equity and Access in Algorithms, Mechanisms, and Optimization, 2022

2021
Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research.
Big Data Soc., July, 2021

The Objective Function: Science and Society in the Age of Machine Intelligence.
PhD thesis, 2021

Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts.
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Governing Algorithmic Systems with Impact Assessments: Six Observations.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020
High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response.
Patterns, 2020

AI reflections in 2019.
Nat. Mach. Intell., 2020

Positionality-aware machine learning: translation tutorial.
Proceedings of the FAT* '20: Conference on Fairness, 2020

Contextual Analysis of Social Media: The Promise and Challenge of Eliciting Context in Social Media Posts with Natural Language Processing.
Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020

2019
AI's social sciences deficit.
Nat. Mach. Intell., 2019


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