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

Bahare Khajehpour

Education
M.Sc. in Science and Technology Policy from Sharif University of Technology
About
Passionate Ph.D. candidate at TU Delft who works on justice aspects of use of AI solutions for road safety. With an academic background in civil engineering and technology policy, enjoys working in multidisciplinary teams and projects aimed at developing fair and responsible emerging technologies.

Host: TUD & RHDHV

Objectives:

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.