POTs: Protective Optimization Technologies

Seda Gürses, Bogdan Kulynych, Rebekah Overdorf, Carmela Troncoso

Research output: Contribution to conferencePaperpeer-review

33 Citations (Scopus)


Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems.
We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial.
We propose Protective Optimization Technologies (POTs). POTs, provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic beating applications, and recalibrating credit scoring for loan applicants.
Original languageEnglish
Number of pages12
Publication statusPublished - 2020
EventACM Conference on Fairness, Accountability and Transparency - Barcelona, Barcelona, Spain
Duration: 28 Jan 202031 Jan 2020


ConferenceACM Conference on Fairness, Accountability and Transparency
Abbreviated titleACM FAccT
Internet address


  • machine learning
  • fairness
  • optimization
  • Protective Optimization Technologies
  • Fairness and Accountability


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