#algorithmic bias

2 AI perspectives

Society

Men Build AI, Women Get Replaced By It — The ILO's Two Labor Markets

A landmark ILO analysis covering 84 countries has directly challenged the assumption that AI automation is gender-neutral, finding that 29 percent of female-dominated occupations face generative AI exposure compared to just 16 percent of male-dominated ones — and in the highest automation-risk tier, the disparity expands to a fivefold gap of 16 percent versus 3 percent. This structural inequality is not the product of individual career choices but the accumulated result of 150-plus years of systematically channeling women into clerical, administrative, and service roles — precisely the occupations that generative AI targets most aggressively. Women face a double exclusion: they are overrepresented in the jobs most exposed to automation while simultaneously comprising only 30 percent of the global AI workforce, with Europe's core tech sector actually shrinking from 22 to 19 percent female representation between 2023 and 2025–2026. Survey data on workplace AI tool usage varies significantly by methodology — Pew Research Center's February 2026 study of 5,119 U.S. adults found a 5-percentage-point gap (women 35%, men 40%), while PwC Workforce Radar reported a 25-point gap (women 32%, men 57%) — but in either case, IMD-Wharton research linking emerging tech skills to a 6 percent salary premium means any sustained usage gap converts directly into a wage gap over time. The existing U.S. gender wage ratio already fell from 83.9 percent to 80.6 percent in a single year according to BLS Q1 2026 data, and the structural dynamics underlying that decline suggest that AI is functioning as an inequality amplifier rather than the equalizing force it is often presumed to be.

Society

93% Turnout, 9 Million Couldn't Vote: How an Algorithm Quietly Dismantled India's Democracy

In India's 2026 West Bengal state assembly election, the Election Commission of India deployed an AI-based "Special Intensive Revision" (SIR) process that removed 9.1 million voters — 11.88% of the total electorate — from the rolls before a single ballot was cast. Among those deleted, Muslims made up 34% of all purged names despite comprising only 27% of the state's population, and in Nandigram constituency, 95.5% of deleted voters were Muslim in a district where Muslims represent just 25% of residents. Of 3.4 million objections filed, fewer than 2,000 were processed before election day, yet 98% of those reviewed were ruled "improperly deleted" — a statistical indictment of the algorithm's core premise. The BJP won West Bengal's assembly for the first time in history, securing 207 of 293 seats, but in 49 constituencies the number of deleted voters exceeded the winner's margin of victory, raising fundamental questions about electoral legitimacy. Concurrently, Freedom House docked India 14 points since 2005 and V-Dem classified it an "electoral autocracy" ranked 105th of 179 nations — together marking what may be the most thoroughly documented case of algorithmic disenfranchisement in the history of electoral democracy.

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