Skip to content

Approximating Accessibility of Regions from Incomplete Volunteered Data

Author: Asghari, H., Stolberg-Larsen, J., & Züger, T.
Published in: CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, 1-6
Year: 2022
Type: Academic articles
DOI: 10.1145/3491101.3519706

Being informed about the accessibility of neighborhoods, cities, and regions can help persons with disabilities in making travel and daily decisions. This information can also be useful and a pushing factor for supportive public policies. While accessibility mapping initiatives, such as, have enjoyed tremendous success and scale, they are still far from exhaustive, and their coverage contains biases stemming from volunteer practices. With the aid of the framework of causal statistics, we suggest approaches to adjust for these biases, with the end goal of providing helpful approximations of overall accessibility in different European geographical regions.

Visit publication
Download Publication


Connected HIIG researchers

Jakob Stolberg-Larsen

Former Researcher: AI & Society Lab

Hadi Asghari, Dr.

Researcher: AI & Society Lab

Theresa Züger, Dr.

Research Group Lead: Public Interest AI | AI & Society Lab

  • Open Access
  • Peer Reviewed

Related projects

Research issue in focus

Du siehst Eisenbahnschienen. Die vielen verschiedenen Abzweigungen symbolisieren die Entscheidungsmöglichkeiten von Künstlicher Intelligenz in der Gesellschaft. Manche gehen nach oben, unten, rechts. Manche enden auch in Sackgassen. Englisch: You see railway tracks. The many different branches symbolise the decision-making possibilities of artificial intelligence and society. Some go up, down, to the right. Some also end in dead ends.

Artificial intelligence and society

The future of artificial Intelligence and society operates in diverse societal contexts. What can we learn from its political, social and cultural facets?