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Dull, Dirty, Dangerous: Understanding the Past, Present, and Future of a Key Motivation for Robotics

Nozomi Nakajima, Pedro Reynolds-Cuéllar, Caitrin Lynch, Kate Darling

TL;DR

This paper tackles the mismatch between the widespread use of the dull, dirty, and dangerous (DDD) lens in robotics and the lack of formal definitions grounding its use. Through an empirical review of nearly 1,000 robotics publications (1980–2024) and a social science synthesis of DDD concepts, the authors show that only a small fraction of papers define DDD or provide concrete examples, highlighting a risk of ill-defined assumptions guiding automation. They introduce a sociotechnical DDD framework that foregrounds worker perspectives, context, and multiple data sources, and demonstrate its application to the waste and recycling sector to illuminate safety, autonomy, social interaction, and meaning tradeoffs in automation decisions. The work aims to steer robotics research toward more informed, ethically aware decisions that consider the downstream labor implications of automation, beyond purely technical capabilities.

Abstract

In robotics, the concept of "dull, dirty, and dangerous" (DDD) work has been used to motivate where robots might be useful. In this paper, we conduct an empirical analysis of robotics publications between 1980 and 2024 that mention DDD, and find that only 2.7% of publications define DDD and 8.7% of publications provide concrete examples of tasks or jobs that are DDD. We then review the social science literature on "dull," "dirty," and "dangerous" work to provide definitions and guidance on how to conceptualize DDD for robotics. Finally, we propose a framework that helps the robotics community consider the job context for our technology, encouraging a more informed perspective on how robotics may impact human labor.

Dull, Dirty, Dangerous: Understanding the Past, Present, and Future of a Key Motivation for Robotics

TL;DR

This paper tackles the mismatch between the widespread use of the dull, dirty, and dangerous (DDD) lens in robotics and the lack of formal definitions grounding its use. Through an empirical review of nearly 1,000 robotics publications (1980–2024) and a social science synthesis of DDD concepts, the authors show that only a small fraction of papers define DDD or provide concrete examples, highlighting a risk of ill-defined assumptions guiding automation. They introduce a sociotechnical DDD framework that foregrounds worker perspectives, context, and multiple data sources, and demonstrate its application to the waste and recycling sector to illuminate safety, autonomy, social interaction, and meaning tradeoffs in automation decisions. The work aims to steer robotics research toward more informed, ethically aware decisions that consider the downstream labor implications of automation, beyond purely technical capabilities.

Abstract

In robotics, the concept of "dull, dirty, and dangerous" (DDD) work has been used to motivate where robots might be useful. In this paper, we conduct an empirical analysis of robotics publications between 1980 and 2024 that mention DDD, and find that only 2.7% of publications define DDD and 8.7% of publications provide concrete examples of tasks or jobs that are DDD. We then review the social science literature on "dull," "dirty," and "dangerous" work to provide definitions and guidance on how to conceptualize DDD for robotics. Finally, we propose a framework that helps the robotics community consider the job context for our technology, encouraging a more informed perspective on how robotics may impact human labor.
Paper Structure (26 sections, 5 figures, 4 tables)

This paper contains 26 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Review of the use of DDD in Robotics Papers
  4. Methodology
  5. Data Collection: Information Sources and Search Strategies
  6. Data Collection: Screening and Data Extraction
  7. Data Analysis
  8. Results
  9. Prevalence of the DDD terms
  10. DDD definitions and contexts of use
  11. Review of Social Science Literature
  12. Methodology
  13. Dangerous Work
  14. Social science review
  15. Considerations for robotics
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Graph aggregating yearly counts of mentions of the DDD concept in papers across all three databases reviewed
  • Figure 2: A DDD Framework for Examining Jobs/Tasks
  • Figure S1: Absolute values and relative percentages of papers per field, papers offering citations supporting DDD concepts, and papers providing definitions of DDD concepts
  • Figure S2: Prisma flowchart diagram showing the process of selecting items for eligibility into the dataset
  • Figure S3: One-page worksheet to guide users through our DDD framework