Biases arising from assumptions about gender roles, identities, and expectations, reinforcing occupational stereotypes and limiting autonomy

Reflects discrimination and stereotypes associated with the caste system, including social exclusion, occupational hierarchies, and educational assumptions.

Skewed assumptions linking racial or ethnic identity with a, intelligence, economic status, or cultural characteristics.

Stereotypical associations linking religious identity with violence, extremism, moral character, or occupational roles

Favoritism or discrimination based on income, wealth, education level, occupation, and social class indicators

Discrimination based on language patterns, dialects, accents, or linguistic markers, often reflecting broader social prejudices

Bias affecting healthcare access, medical representation, and attitudes toward physical/mental health conditions and disabilities.

Political viewpoint favoritism affecting representation of parties, ideologies, leaders, and civic participation in AI outputs.

Systematic distortion in knowledge validation and representation, encompassing testimonial and hermeneutical injustice across marginalized perspectives.

This bias favors Global North views, makes more errors for underrepresented regions, and over-recommends affluent areas while neglecting rural regions.