Image credit: Christiaan Colen,“Rootkit code,” Flickr, CC BY-SA 2.0.

PILAC Briefing Report:
“War-Algorithm Accountability” 

In War-Algorithm Accountability (August 2016), we introduce a new concept—war algorithms—that elevates algorithmically-derived “choices” and “decisions” to a, and perhaps the, central concern regarding technical autonomy in war. We thereby aim to shed light on and recast the discussion regarding “autonomous weapon systems” (AWS).

We define “war algorithm” as any algorithm that is expressed in computer code, that is effectuated through a constructed system, and that is capable of operating in relation to armed conflict. In introducing this concept, our foundational technological concern is the capability of a constructed system, without further human intervention, to help make and effectuate a “decision” or “choice” of a war algorithm. Distilled, the two core ingredients are an algorithm expressed in computer code and a suitably capable constructed system.

Through that lens, we link international law and related accountability architectures to relevant technologies. We sketch a three-part (non-exhaustive) approach that highlights traditional and unconventional accountability avenues. We focus largely on international law because it is the only normative regime that purports—in key respects but with important caveats—to be both universal and uniform. In this way, international law is different from the myriad domestic legal systems, administrative rules, or industry codes that govern the development and use of technology in all other spheres. By not limiting our inquiry only to weapon systems, we take an expansive view, showing how the broad concept of war algorithms might be susceptible to regulation—and how those algorithms might already fit within the existing regulatory system established by international law. 

Report and Related Resources

  • Report landing page [link]
  • Full report including Bibliography and Appendices (244 pages) 
    • PDF: high quality for print [link] and compressed version [link] (the compressed version is also available on SSRN: link)
  • Standalone Executive Summary (16 pages) 
  • Standalone Bibliography (26 pages)
    • Web [link] and PDF [link] [NB: the PDF of the full report, above, also contains the Bibliography]
  • Standalone Appendices (98 pages)
    • PDF versions: High quality for print (but not text-searchable) [link] and lower quality but text-searchable [link] [NB: The PDF of the full report, above, also contains the Appendices; however, there the Appendices are not text-searchable]
  • Lawfare Blog post: “Accountability for Algorithmic Autonomy in War,” Lawfare, September 12, 2016 (Gabriella Blum, Dustin A. Lewis, and Naz K. Modirzadeh)
  • PHAP Expert Briefing question-and-answer:War algorithms and international law - Interview with Dustin A. Lewis,” October 10, 2016.
  • Report without Appendices (149 pages)
    • PDF [link]; Web version (immediately below)

Research Assistants

Learning Resources


  • Introduction to Algorithms (SMA 5503 at MIT, Fall 2005) [link]
  • Design and Analysis of Algorithms (6.046J / 18.410J at MIT, 2012) [link]

Artificial Intelligence

  • Artificial Intelligence (6.034 at MIT, Fall 2010) [link]

Machine Learning

  • Machine Learning (Coursera) [link]
  • Neural Networks for Machine Learning (Coursera) [link]
  • An introduction to machine learning with scikit-learn [link]

Thinking Computationally 

  • Simple Programming Problems (Adrian Neumann) [English link; Chinese link]  


[Write-up last updated: January 2017]