S
Shlomit Lir
Israeli Researcher
2
APPEARANCES
2
PODCASTS
012
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Oct 2, 2026
Who Gets to Write History? Wikibiases.org by Shlomit Aharoni Lir
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11:13Renee GarfinkelHOST
Tell us about the case of Iskandar 323, the lone editor as a source of knowledge bias.
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11:22Shlomit LirGUEST
So I thought one interesting way to really follow the line of bias and to understand where it springs from is to take one editor, who was very notorious as a biased editor.
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11:39Shlomit LirGUEST
This is an editor that really can shed light on other editors who work in a similar way.
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11:50Shlomit LirGUEST
at the beginning of the year, but until then, he edited for 12 years, close to 12 years, with 25,000 edits, most of them on the topics I mentioned, Israel, the conflict, Jewish history, and about the same amount of edits on talk pages.
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12:14Shlomit LirGUEST
So this is a veteran editor, very, from his writing, highly educated, very knowledgeable, and with a prominent place within the society, community, they call it, of editors.
15 MINS LATER
Who Gets to Write History? Wikibiases.org by Shlomit Aharoni Lir
R
10:38Renee GarfinkelHOST
Tell us about the case of Iskandar 323, the lone editor as a source of knowledge bias.
S
10:46Shlomit LirGUEST
So I thought one interesting way to really follow the line of bias and to understand where it springs from is to take one editor, who was very notorious as a biased editor.
S
11:03Shlomit LirGUEST
This is an editor that really can shed light on other editors who work in a similar way.
S
11:15Shlomit LirGUEST
at the beginning of the year, but until then, he edited for 12 years, close to 12 years, with 25,000 edits, most of them on the topics I mentioned, Israel, the conflict, Jewish history, and about the same amount of edits on talk pages.
S
11:38Shlomit LirGUEST
So this is a veteran editor, very, from his writing, highly educated, very knowledgeable, and with a prominent place within the society, community, they call it, of editors.
14 MINS LATER
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26:12Renee GarfinkelHOST
So what actions should Wikipedia take to reduce the problems you're talking about? And what about LLMs, large language models, AI? What should their policies be? I think