For decades, I was told that Norman Finkelstein was “dangerous.” I heard he was biased, hateful, even antisemitic — and for far too long, I believed it. I avoided his work, not because I disagreed, but because I had been conditioned to think he simply wasn’t a good person.
Only within the last five years — ironically around 2020, when he himself began wondering whether decades of advocacy had been a waste of time — did I finally listen with an open mind. And what I discovered was sobering: a scholar who had devoted his life to documenting truth and confronting hypocrisy, slowly losing faith that truth itself could still matter.
Even when I once accepted the mainstream narrative, I could never recall him saying anything anti-Jewish. I just assumed there must be some hidden “read-between-the-lines” bigotry that I, as a non-Jew, wasn’t perceptive enough to see. Meanwhile, many of the same commentators who condemned him also platformed ex-Muslim and anti-Islamist voices whose rhetoric about Islam often veered into sweeping generalizations — language that dehumanized or vilified entire populations.
Between 2010 and 2012, I read the Old Testament (including the Torah), the New Testament (the Christian Bible), and the Qur’an (the Islamic holy book) cover to cover — partly so that no one could ever say I ‘don’t understand’ the faiths I speak about. While I’ve also read substantial portions of the Talmud, I wouldn’t claim to have studied its entire corpus, which is interpretive rather than scriptural in nature.
Every text contains arguable contradictions, contextual reliability thresholds, and difficult verses; that’s not a flaw unique to one faith, it’s the story of humanity itself. Yet for decades I saw one religion endlessly cherry-picked as proof of violence while the others were shielded by “context.” That double standard blinded me for years.
I’m not going to say which religion it was — that’s for each reader to confront honestly within themselves. But I would encourage anyone genuinely curious to conduct their own small-scale study: examine fifteen to twenty independent sources (i.e. different parent companies), each with differing stances and bias levels, and focus not on doctrine, but on the bias gradient and sentiment framing surrounding one particular faith among the three. The goal isn’t to judge belief, but to observe how perception itself is constructed, and how selective context shapes the moral hierarchies we unconsciously accept.
The Validation Process
When Objectivity AI™ began building its Israel–Palestine data set, we wanted to change that pattern — to replace assumption with verification. The system evaluates sources through multi-layer factual recursion and human-in-the-loop validation. Norman Finkelstein’s corpus emerged with the lowest factual-validation-failure rate of any author studied.
That doesn’t mean the AI “chose” him; humans ultimately did after the model’s outputs were recursively validated across multiple independent data streams to ensure factual convergence and eliminate hallucinations. Objectivity AI™ functions as a higher-reasoning validation system — it identifies, cross-references, and stress-tests claims through layered recursive logic, but it does not render the final judgment (yet).
Our alpha-tester and manual-verification teams review the model’s factual outputs, vote on their confidence levels, and finalize each determination. Those human reviewers represent exceptional diversity: roughly 150 to 160 alpha testers — Jewish, Muslim, Christian, secular, and everything in between.
A plurality, not a majority, live in the U.S. and Canada. Others are from Israel, the broader Middle East, Europe, and a surprising share from East Asia — a region historically tied to the project’s research roots. What astonished me most, however, was that about 25 percent of our testers identify as Jewish.
On the Role of Jewish Volunteers
Their participation wasn’t symbolic; it was foundational. These were unpaid volunteers—people giving their time, intellect, and emotional energy not for recognition or compensation, but because they believed in the integrity of truth itself. They were passionate not only about artificial intelligence, but about what it could represent: a way to restore fairness to conversations long clouded by bias. For many, especially within the Jewish community, participation held deeper meaning. They wanted to help reclaim the moral distinction between criticism and prejudice—to show that defending truth is not the same as betraying one’s people.
Some even joked — their words, not mine — that they were the project’s “human shields.” Beneath the humor was quiet defiance. They understood that factual integrity often carries a cost: any dataset revealing asymmetrical suffering would eventually be accused of bias, not because the data failed verification, but because it challenged narratives that powerful institutions had learned to protect. Yet they persisted, volunteering their nights and weekends, combing through sources, validating claims, and debating context—not for profit, but for principle.
The backlash was both coordinated and personal. Some volunteers found their workplaces harassed, their employers pressured to sever ties. Small business owners endured targeted email campaigns, with anonymous actors contacting their clients en masse to discredit them professionally. Others had their names published on websites notorious for doxing and reputational harm — placed there for no greater offense than confirming empirically validated information. And many experienced a quieter form of punishment: friends drifting away, relatives confronting them online, or social circles growing tense the moment they shared something that challenged accepted narratives.
Among the most painful incidents were those directed at our Jewish volunteers. Some were called Erev Rav — sometimes in social media threads, group chats, or whispered through their own communities. The term, originating in biblical and later rabbinic tradition, means “mixed multitude,” but in modern usage it has become a slur implying a Jew who has betrayed their own people. To be labeled Erev Rav in that way is not a casual insult; it carries the weight of spiritual excommunication — a suggestion that one’s Jewishness itself is revoked. For Jewish volunteers who stood for factual truth and universal human rights, hearing that from fellow Jews — often from those they once admired — was devastating. It revealed a moral inversion that borders on tragedy: Jews condemned by other Jews for defending the very values of justice, empathy, and integrity that Jewish ethics helped to define.
Yet they were not alone in facing consequences. Non-Jewish volunteers — Muslims, Christians, agnostics, and secular humanists — also endured intimidation, harassment, and reputational harm. Some were labeled extremists or propagandists merely for upholding methodological transparency. Many lost professional opportunities; a few withdrew from public discourse entirely, unwilling to risk their safety or that of their families.
The irony is bitter. Much of this hostility came from people and organizations who sincerely believed they were defending Jewish safety. Yet their actions inflicted measurable harm on Jewish families — loss of income, public humiliation, and profound emotional distress. The intent may have been protection, but the outcome was persecution. These volunteers — Jewish and non-Jewish alike — acted out of conscience, not conflict. Their work deserves not suspicion but gratitude. Because when the defense of identity becomes indistinguishable from the suppression of truth, the moral architecture that sustains honest discourse begins to collapse — and with it, the very humanity these volunteers were trying to preserve.
The Language Problem
This is also why comparisons between Norman Finkelstein and figures like Nick Fuentes are so dangerous. Fuentes’s past rhetoric contained explicit antisemitic tropes; he now claims he didn’t mean them, and his more recent commentary on Israel aligns closer to a progressive critique of state policy. The issue isn’t that some of his observations about geopolitical power are wrong — many are verifiable — it’s that he still uses collective language like “the Jews” instead of specifying the Israeli government, the Likud Party, the IDF, or particular lobbying networks.
Precision matters. When legitimate state-level criticism is phrased as ethnic generalization, truth becomes contaminated. Objectivity AI™ can, in theory, embed Fuentes as a data source — his statements often contain verifiable elements — but doing so imposes a high normalization cost. His sentiment may at times align with fact, yet his framing introduces bias that forces the model to expend additional reasoning cycles to reach contextual neutrality. Objectivity AI™ doesn’t merely validate information; it optimizes for neutrality efficiency. Sources that enter the system already balanced integrate with minimal adjustment, while those burdened by bias demand recursive correction and greater computational effort. In that sense, Finkelstein’s corpus is more efficient and stable to process — which is why he ranks as a flagship source within the framework, representing low validation failure rather than informational dominance.
Finkelstein’s own methodology mirrors this principle. He has never directed his critiques at an identity group; his focus remains structural and behavioral. When he speaks of Israeli culture, he’s referring to the sociological effects of occupation, militarization, and collective trauma — not to ethnicity or religion. His analysis explores how prolonged conflict reshapes public consciousness within a nation, much like academic studies of American militarism or Russian nationalism. Still, the distinction can sound uncomfortable, and even one of my most intellectually honest neighbors in D.C. — a staunch supporter of Israel — once remarked that Finkelstein sometimes paints too broadly when characterizing Israeli society. Yet even he conceded that the data itself were accurate. Uncomfortable isn’t the same as untrue.
Emotional Honesty and Harm
Over the past year, I’ve had hundreds of conversations about this topic across different platforms — but thirty-two (32) of them were distinct in nature (as of November, 2025). They were with Jewish and evangelical Christian friends, colleagues, extended family, and others who know me personally. Each, in their own words, shared a similar yet deeply personal sentiment: “I know you’re not antisemitic, but it still hurts when you criticize Israel,” or “I get that you mean policy, not people — but I still cringe.” I don’t dismiss that. Emotional pain matters. And I suspect there are many more who feel the same way but choose not to reach out — perhaps out of discomfort, uncertainty, or simply not knowing how to engage. I understand that too.
For many, the pain isn’t ideological; it’s existential. A critique of state policy can register as a critique of self, of safety, of the fragile continuity of family. With the long shadow of antisemitism, that reaction is human. Words can feel like threats when they brush against generations of inherited trauma. So when people tell me to “ignore” those feelings, I won’t. It would be dishonest to pretend I don’t understand the depth of that fear.
But there is another truth we have to hold at the same time: the Jewish truth-tellers, academics, and human-rights advocates who have lost livelihoods for speaking from the same moral impulse Judaism often celebrates—justice, compassion, human dignity. These are Jewish families too. Some were doxxed, blacklisted, and ostracized—not by antisemites, but by people claiming to protect Jewish safety. If we’re going to talk about harm, we have to hold both: the emotional harm of fear, and the material harm of punishment for conscience. Both fracture Jewish lives. Yet one of them removes income, healthcare, housing, and stability. If our response to fear produces new suffering in the same community, our moral calculus is broken.
Part of what intensifies this conflict is how we encounter information. Most of us don’t read “the internet”; we read our feed. Feeds are not neutral. They’re optimized for engagement, which means:
- Personalization loops: you’re shown more of what you dwell on, not necessarily what’s most accurate.
- Sentiment amplification: strong emotion outranks nuance; outrage outperforms context.
- Network effects: what your closest contacts interact with is overrepresented, even if it’s unrepresentative.
- Moderation asymmetries: enforcement is uneven; identical claims can be labeled differently across platforms or accounts.
So two sincere people can inhabit incompatible realities: one fed a stream where “criticism = coded antisemitism,” the other flooded with evidence of state-level abuses. Both feel certain because their feeds are certain. In that environment, language tightens, categories collapse, and intent gets flattened. Saying “policy” is heard as “people.” Asking for accountability is heard as erasure. And once a platform labels a frame as hateful (or, conversely, elevates it as righteous), the algorithm keeps reinforcing that conviction.
Objectivity AI™ was built to push against exactly that drift. In our system, facts are judged independently from sentiment, and every claim is forced through multiple, diverse sources before it “settles.” Sources that arrive with precise, identity-neutral language impose a lower normalization cost; those that mix verifiable facts with sweeping identity claims impose a higher one. That isn’t a moral verdict; it’s a signal that bias-cleanup takes compute—and that compute has consequences for clarity. It’s also why many of the volunteers who helped test this—including a high share of Jewish volunteers, all unpaid—joined: to separate criticism from prejudice, and to keep collective blame from swallowing the conversation. They gave their time not for recognition, but to help restore fairness to a discourse increasingly shaped by engagement algorithms.
This project was never about blame. It was about reconstruction: tracing verified context back to first principles and forward to the present. Our timeline literally begins with the formation of the land that became Israel and Palestine—before peoples and polities—so the “you don’t know history” objection collapses on contact. From that geological baseline through modern conflict data, every claim is trace-validated, source-linked, and human-approved. The goal isn’t to win an argument inside anyone’s feed; it’s to build a record that can survive outside of one.
The Real Danger of Mislabeling
In recent years, a troubling pattern has emerged: the notion that any criticism of Israel is simply coded antisemitism—a “read-between-the-lines” hostility that non-Jews supposedly can’t detect. This belief is false, and it’s dangerous.
First, it collapses categories that responsible frameworks strive to keep distinct. The International Holocaust Remembrance Alliance (IHRA) working definition includes examples such as “denying the Jewish people their right to self-determination (e.g., by claiming that the existence of a State of Israel is a racist endeavour)” and “drawing comparisons of contemporary Israeli policy to that of the Nazis.” At the same time, IHRA states that legitimate criticism of Israel, when applied in the same way as one would critique any other state, cannot be regarded as antisemitic. The Jerusalem Declaration on Antisemitism (JDA) similarly warns that collective blame of Jews is antisemitic, whereas sharp critique of Israeli state policy or Zionism is not inherently so. When those distinctions are ignored, debate is chilled and scholars self-censor.
Second, the definition is itself legitimate but often misused or misinterpreted. For example, one clause addresses “denying the Jewish people their right to self-determination.” That clause is valid—it protects against denial of Jewish self-determination—but becomes problematic when it is invoked without nuance, such that any review of Israeli policy is immediately branded as such. The key is not the principle of self-determination, but whether it is exercised at no harm to another population or group of people. In the Objectivity AI™ framework the core axiom is simple: all lives are equal. The model flags not just denial of a right, but when the invocation of one group’s right proceeds by denying another’s. That is where the recursive logic fails.
Third, take the example on Piers Morgan Uncensored where Katie Miller—a former U.S. government aide and spouse of a senior adviser—during a live panel told commentator Cenk Uygur to “check [his] citizenship application and hope everything was legal,” after he criticized Israeli policy. This is a concrete case of dissent being recast as bigotry and then weaponized with threats against legal status.
Fourth, the trope of “Jews control the media” illustrates how factual complexity becomes distilled into bigotry. It is not inherently problematic to note that a specific individual—say Larry Ellison—has publicly supported Israeli causes (e.g., he donated $16.6 million to Friends of the IDF in 2017), or that Oracle has played a significant role in the TikTok-U.S. arrangement. But if you then say “all Jews control the media”—that collapses individual decisions into collective identity, which is an antisemitic trope. Even when rooted in real actors, the leap into collective blame destroys nuance and risks bigotry.
Why is all this dangerous? Because it redefines dissent as hate. Once truth-claims about state power become indistinguishable from prejudice, the institutions of inquiry weaken: scholars are silenced, journalists are cautious, human-rights defenders are punished. It also harms Jewish communities by reducing Jewish identity to one state’s policy, erasing the pluralism of Jewish voices—including those who oppose certain Israeli policies on ethical or religious grounds. It weaponizes heritage instead of protecting it.
The path forward is not to diminish antisemitism—which remains real and intolerable—but to preserve clear lines: collective blame of Jews is antisemitic; rigorous, identity-neutral critique of state policy is not. When we lose those distinctions, we imperil free inquiry, corrode civil liberties, and paradoxically make it harder to fight true antisemitism when it appears.
Where We Stand
When we say Norman Finkelstein is Objectivity AI’s Flagship Source for the Israel–Palestine framework, it isn’t an endorsement of every opinion he holds. It’s a reflection of data integrity — of consistent factual accuracy across recursive model tests and human audits.
Finkelstein is provocative, passionate, often loud — but he is not a liar. His bias, while present, remains structurally compatible with truth; it bends around evidence rather than breaking from it. Within Objectivity AI™, this distinction matters. The model can process data from any source, but each carries a measurable normalization cost — the computational energy required to reconcile framing with fact.
Many modern commentators, influencers, and even scholars introduce high sentiment volatility or ideological load, forcing the system to perform deeper recursive correction before achieving equilibrium. Finkelstein’s corpus, by contrast, integrates with minimal friction. His framing may be impassioned, but his empirical alignment is unusually stable across independent validation cycles.
In practical terms, that means his work demands the least corrective computation of any verified corpus in the current framework — not because it dominates the dataset, but because it resists distortion.
If we lose the ability to distinguish those who distort truth from those who defend it, we won’t just forfeit objectivity — we’ll abandon the moral compass that makes truth worth defending in the first place.
