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Resources

A collection of key books, papers and venues that are foundational to Responsible Robotics and AI - and serve as inspiration to our team.

Book recommendations

New Laws of Robotics, by Frank Pasquale
Design Justice, by Sasha Costanza-Chock
Race After Technology, by Ruha Benjamin
Artificial Whiteness, by Yarden Katz
The Age of Surveillance Capitalism, by Shoshana Zuboff
Atlas of AI, by Kate Crawford

Paper recommendations

  1. S. Serholt, S. Ljungblad, and Nı́ Bhroin Niamh, “Introduction: special issue—critical robotics research,” AI & SOCIETY, vol. 37, no. 2, pp. 417–423, 2022.
  2. A. Hundt, W. Agnew, V. Zeng, S. Kacianka, and M. Gombolay, “Robots enact malignant stereotypes,” in 2022 ACM Conference on Fairness, Accountability, and Transparency, 2022, pp. 743–756.
  3. M. Brandao, “Normative roboticists: the visions and values of technical robotics papers,” in IEEE International Conference on Robot and Human Interactive Communication, 2021, pp. 671–677. [PDF]
  4. M. R. Calo, “Robots and privacy,” in Machine Ethics and Robot Ethics, Routledge, 2020, pp. 491–505.
  5. Q. V. Liao, D. Gruen, and S. Miller, “Questioning the AI: informing design practices for explainable AI user experiences,” in CHI Conference on Human Factors in Computing Systems, 2020, pp. 1–15.
  6. R. Abebe, S. Barocas, J. Kleinberg, K. Levy, M. Raghavan, and D. G. Robinson, “Roles for computing in social change,” in Conference on Fairness, Accountability, and Transparency, 2020, pp. 252–260.
  7. B. Kulynych, R. Overdorf, C. Troncoso, and S. Gürses, “POTs: protective optimization technologies,” in Conference on Fairness, Accountability, and Transparency, 2020, pp. 177–188.
  8. M. Chromik, M. Eiband, S. T. Völkel, and D. Buschek, “Dark Patterns of Explainability, Transparency, and User Control for Intelligent Systems,” in IUI workshops, 2019, vol. 2327.
  9. B. Green and Y. Chen, “The principles and limits of algorithm-in-the-loop decision making,” Proceedings of the ACM on Human-Computer Interaction, vol. 3, no. CSCW, pp. 1–24, 2019.
  10. Z. C. Lipton, “The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.,” Queue, vol. 16, no. 3, pp. 31–57, 2018.
  11. S. Šabanović, “Robots in society, society in robots: Mutual shaping of society and technology as a framework for social robot design,” International Journal of Social Robotics, vol. 2, no. 4, pp. 439–450, 2010.

Conferences and workshops