DC1. How to implement justice in AI for road safety?



  • To investigate the conditions to achieve both safety and justice on the road through AI
  • To embed non-discrimination and inclusiveness in the design of AI technologies for road safety
  • To disentangle the role of explainability for justice in AI from the user and the regulatory agency and policy-maker perspectives

Explored from a theoretical perspective within the recruitment at TUD and from a practical perspective (operationalisation on existing tools e.g., the Strategic Road Safety Policy tool, the Flowtrack smart traffic lights system) within the recruitment at RHDHV.

Expected results:

  • A new theoretical framework to assess justice on the road, through data justice and attention to VRUs
  • A set of recommendations for promoting justice by design, e.g., achieving a better inclusivity of vulnerable or excluded groups (e.g., pedestrians, cyclists, LMICs, women, and low-income households)
  • A set of guidelines to design fair data protocols (e.g., data protection, user profiling) and algorithms (e.g., explainability, goal-oriented planning) in the operation stage of AI in road safety

Planned secondment(s):

  • TNO-NL, Purpose: to explore the existing experience of TNO on goal-oriented planning for ethical and safe AI in autonomous systems on the road and in other domains.
  • RHDHV-RSA, Purpose: international exposure and explore ethical issues of AI in a non-European LMIC (Republic of South Africa). Preliminary agreement with the RHDHV-RSA office.