About
Dr. Shlomit Lir is a researcher at the University of Haifa and Bar-Ilan University, specializing in the politics of knowledge, and an academic adviser to Here4Good. Her work examines the intersections of gender, communication, and technology, with a focus on how identity and power are constructed within digital and cultural contexts. An award-winning scholar, she has published extensively on social justice, digital inclusion, and the socio-technical dynamics of knowledge production. Her research further explores media representation and the formation of voice among marginalized groups. She is the author and editor of multiple books on feminist politics, gender, and cultural narratives.
WikiBiases
Mission
In an era in which open digital platforms constitute the primary infrastructure for shaping knowledge, collective memory, and historical consciousness, the question of neutrality has become structurally critical. English Wikipedia, as the most consulted reference source in the world and a foundational input for search engines, artificial intelligence systems, journalists, policymakers, and the general public, operates as an active arena in which political, cultural, and national narratives are negotiated, legitimized, reframed, or marginalized.
WikiBiases was established to examine how structural bias forms within open knowledge systems and how it shapes global perception.
Areas of Examination
Rather than focusing on isolated factual inaccuracies, WikiBiases analyzes systematic deviations from neutrality within English Wikipedia, particularly in content related to Israel, Jewish heritage, and the Israeli–Palestinian conflict.
The project examines:
- Sourcing hierarchies and asymmetrical scrutiny
- Editorial governance and power concentration
- Terminological reframing
- Strategic information ordering
- Semantic weighting and cumulative micro-edits
These mechanisms often operate gradually, reshaping meaning over time while maintaining a surface appearance of neutrality.
Why It Matters
Wikipedia does not function in isolation. Its content feeds search engines, AI language models, media coverage, academic references, and public discourse. When structural bias becomes embedded in such a central node of the digital knowledge ecosystem, its effects reverberate widely.
The issue, therefore, is not partisan disagreement, but the integrity of knowledge production itself.
Approach
WikiBiases applies qualitative content analysis, terminology auditing, revision-history tracking, and pattern mapping to identify recurring editorial strategies. The goal is to move beyond political rhetoric and toward systematic documentation of how narrative framing operates within collaborative digital environments