AI as an attorney? Student uses ChatGPT, Gemini to sue UW over alleged racial discrimination 34%

By Monica Nickelsburg0%

4/15/2026, 9:31:00 PM

BS Summary: This article contains 24 faulty reasoning types, including Confirmation Bias, Hasty Generalization, and Recency Bias, with Unattributed Quote as the most egregious example at 14.4% saturation with 105 hits. Analysis detected 1,130 faulty-reasoning hits from 730 analyzed words, generating a BS Score of 42.1% and a BS Rank of 34% (14,476 of 21,887 articles). This article is better (less manipulative) than 66.10% of the article peer group.

Stanley Zhong graduated from his Bay Area high school with a 4.42 GPA, a 1590 SAT score, and high rankings in several international coding competitions, according to his father. 
Nan Zhong liked his son’s chances for the 18 colleges he applied to, including several University of California schools and the University of Washington. 
“With all the credentials, everybody thought it's going be a strong case,” Nan Zhong said. 
“But in a fairly disappointing way, he was rejected by almost all of them. 
He was rejected by 16 out of the 18 programs.” 
His son decided not to go to college when Google offered him an AI engineering job that typically requires a doctorate. 
“The contrast is a little bit hard to comprehend,” Zhong said. 
“You have on one hand saying, this guy's as good as somebody with a Ph.D. degree... and on the other hand, there are all these colleges saying he's not qualified enough for undergrad admission.” 
<strong><em>RELATED</em>: <a href="https://www.kuow.org/stories/some-colleges-scrap-diversity-questions-from-admissions-essays-will-it-change-how-students-talk-about-themselves" target="_blank">Some colleges scrap diversity questions from admissions essays. 
Will it change how students talk about themselves? 
</a></strong> 
After speaking with other Asian American families with similar experiences, Zhong concluded discrimination was at work. 
He tried to enlist a law firm to represent his son but no lawyer would take the case. 
He said the attorneys he talked to were concerned about going up against deep-pocketed universities, worried the courts would not be on their side, or feared the backlash of wading into such a politically charged topic. 
So Zhong turned to an unlikely legal resource. 
“I think AI is a game changer in this scenario,” he said. 
Zhong began using ChatGPT and Gemini as one might a team of lawyers, asking them to form legal arguments, draft official documents, and check each other’s work. 
So far, he said none of the judges in his lawsuits have taken issue with his AI attorneys. 
He noted that a federal judge in Seattle recently sided with Zhong on a <a href="https://www.courtlistener.com/docket/69668915/38/zhong-v-university-of-washington-board-of-regents/" target="_blank">procedural motion.</a> 
“I'm going stand here and make a bold claim, based on our filing and the judge's recent ruling, that I think AI is already on par, if not better, than the top lawyers in the nation,” Zhong said. 
<strong><em>RELATED</em>: <a href="https://www.kuow.org/stories/judge-indefinitely-bars-trump-from-fining-uc-over-alleged-discrimination" target="_blank">Judge indefinitely bars Trump from fining UC over alleged discrimination</a></strong> 
That’s not to say that his AI lawyers never make mistakes. 
Zhong said he has to comb through all of the documents carefully, because the bots sometimes fabricate quotes or other details. 
Ultimately, he said he is responsible for the accuracy of the filings. 
But he also noted that human lawyers make mistakes, too. 
In fact, he says he used AI to find nearly a dozen legal errors in the UW's latest filing. 
Zhong plans to travel to Seattle for an in-person hearing in federal court in the coming weeks. 
That will present a unique challenge to his AI-enabled case, as electronics are forbidden in the courtroom. 
The University of Washington and Rob McKenna, the former Washington attorney general and lead lawyer in this case, declined interview requests. 
“The UW stands behind its admissions process, and we have long recognized that our capacity is limited and we are not able to admit some very talented and capable applicants," a UW spokesperson said in a statement. 
<strong><em>RELATED</em>: <a href="https://www.kuow.org/stories/judge-halts-trump-effort-requiring-colleges-to-show-they-don-t-consider-race-in-admissions" target="_blank">Judge halts Trump effort requiring colleges to show they don't consider race in admissions</a></strong> 
In a filing with the court, UW attorneys say Stanley Zhong was not rejected on the basis of race, but because he was an out-of-state applicant for a highly competitive program. 
“State law requires the University to prioritize the admission of Washington residents,” <a href="https://www.courtlistener.com/docket/69668915/43/zhong-v-university-of-washington-board-of-regents/" target="_blank">the document says</a>. 
“The impact of this prioritization is more pronounced for UW’s most competitive programs and, in particular, the Paul G. 
Allen School of Computer Science & Engineering (the “Allen School”)—the nation’s seventh-ranked computer science program—where 84% of students who were admitted as freshmen in 2023 were Washington residents  Stanley Zhong, a California resident, applied for admission to the Allen School for Fall 2023. 
He was among the 98% of out-of-state applicants not accepted for admission.” 
The Zhongs have also filed lawsuits against the University of California network of colleges. 
Zhong was rejected by all five of the UC schools to which he applied. 
Article reasoning-pattern comparisonThis article: 13.2%Monica Nickelsburg: 2.6%KUOW: 2.6%Confirmation Bias13.2%This article: 0.0%Monica Nickelsburg: 1.7%KUOW: 1.3%Anchoring Bias0.0%This article: 4.1%Monica Nickelsburg: 4.1%KUOW: 3.4%Availability Heuristic4.1%This article: 8.9%Monica Nickelsburg: 1.0%KUOW: 1.2%Representativeness Heuristic8.9%This article: 0.0%Monica Nickelsburg: 0.4%KUOW: 0.7%Hindsight Bias0.0%This article: 6.8%Monica Nickelsburg: 1.7%KUOW: 1.4%Overconfidence Bias6.8%This article: 0.0%Monica Nickelsburg: 12.0%KUOW: 7.4%Framing Effect0.0%This article: 0.0%Monica Nickelsburg: 0.7%KUOW: 1.0%Loss Aversion0.0%This article: 5.1%Monica Nickelsburg: 0.9%KUOW: 1.0%Status Quo Bias5.1%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Sunk Cost Effect0.0%This article: 4.9%Monica Nickelsburg: 4.1%KUOW: 3.8%Optimism Bias4.9%This article: 1.5%Monica Nickelsburg: 1.8%KUOW: 1.8%Pessimism Bias1.5%This article: 1.9%Monica Nickelsburg: 9.9%KUOW: 8.0%Negativity Bias1.9%This article: 0.0%Monica Nickelsburg: 3.3%KUOW: 2.0%Self-Serving Bias0.0%This article: 9.2%Monica Nickelsburg: 1.1%KUOW: 0.9%Fundamental Attribution Error9.2%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Actor-Observer Bias0.0%This article: 0.0%Monica Nickelsburg: 2.0%KUOW: 2.0%In-Group Bias0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.5%Out-Group Homogeneity Bias0.0%This article: 4.7%Monica Nickelsburg: 1.7%KUOW: 2.7%Halo Effect4.7%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Horn Effect0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.0%Dunning-Kruger Effect0.0%This article: 10.1%Monica Nickelsburg: 0.9%KUOW: 1.1%Recency Bias10.1%This article: 0.0%Monica Nickelsburg: 0.5%KUOW: 0.4%Primacy Effect0.0%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.0%Blind-Spot Bias0.0%This article: 0.0%Monica Nickelsburg: 1.2%KUOW: 0.5%Ad Hominem0.0%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.3%Straw Man0.0%This article: 10.0%Monica Nickelsburg: 5.4%KUOW: 4.3%Appeal to Authority10.0%This article: 8.9%Monica Nickelsburg: 2.3%KUOW: 1.4%False Dilemma8.9%This article: 2.3%Monica Nickelsburg: 0.7%KUOW: 0.9%Slippery Slope2.3%This article: 0.0%Monica Nickelsburg: 0.5%KUOW: 0.1%Circular Reasoning0.0%This article: 12.1%Monica Nickelsburg: 4.5%KUOW: 4.1%Hasty Generalization12.1%This article: 5.6%Monica Nickelsburg: 0.3%KUOW: 0.3%Red Herring5.6%This article: 0.0%Monica Nickelsburg: 0.9%KUOW: 0.8%Bandwagon0.0%This article: 1.6%Monica Nickelsburg: 4.9%KUOW: 6.1%Appeal to Emotion1.6%This article: 0.0%Monica Nickelsburg: 0.7%KUOW: 0.8%Begging the Question0.0%This article: 0.0%Monica Nickelsburg: 4.4%KUOW: 2.2%Post Hoc (False Cause)0.0%This article: 1.4%Monica Nickelsburg: 0.3%KUOW: 0.1%Tu Quoque1.4%This article: 4.2%Monica Nickelsburg: 0.2%KUOW: 0.3%Burden of Proof4.2%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.2%Appeal to Nature0.0%This article: 7.7%Monica Nickelsburg: 0.4%KUOW: 0.2%Composition/Division7.7%This article: 6.8%Monica Nickelsburg: 3.6%KUOW: 3.3%Anecdotal6.8%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.1%No True Scotsman0.0%This article: 7.7%Monica Nickelsburg: 2.4%KUOW: 1.4%Ambiguity (Equivocation)7.7%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.1%Middle Ground0.0%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.1%Personal Incredulity0.0%This article: 1.6%Monica Nickelsburg: 0.3%KUOW: 0.2%Special Pleading1.6%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.2%Genetic Fallacy0.0%This article: 14.4%Monica Nickelsburg: 1.8%KUOW: 1.0%Unattributed Quote14.4%This article: 0.0%Monica Nickelsburg: 1.1%KUOW: 0.8%Quote-first Misdirection0.0%This article: 0.0%Monica Nickelsburg: 3.3%KUOW: 3.2%Biased Writer Voice0.0%This article: 0.0%Monica Nickelsburg: 0.6%KUOW: 1.5%Indoctrination0.0%This article: 0.0%Monica Nickelsburg: 1.8%KUOW: 1.1%Politically Left Leaning Bias0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Monica Nickelsburg: 0.9%KUOW: 1.3%Attempt to Sell a Product or S…0.0%

730 words analyzed.

Speakers

4speakers59%attributed speech302writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 4 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageNan Zhong • 15 words • 100.0% coverageNan Zhong • 14 words • 0.0% coverageNan Zhong • 10 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageZhong • 11 words • 0.0% coverageZhong • 34 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageZhong • 36 words • 100.0% coverageWriter's voice • 8 words • 0.0% coverageZhong • 12 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageZhong • 18 words • 0.0% coverageZhong • 18 words • 0.0% coverageZhong • 38 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageZhong • 21 words • 0.0% coverageZhong • 12 words • 0.0% coverageZhong • 10 words • 0.0% coverageZhong • 19 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageUW spokesperson • 37 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageUW attorneys • 31 words • 0.0% coverageUW attorneys • 17 words • 100.0% coverageUW attorneys • 19 words • 0.0% coverageUW attorneys • 44 words • 0.0% coverageUW attorneys • 12 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverage
Selected voice

UW spokesperson

100%flagged-word coverage
37 attributed words8.6% of attributed speech50% writer coverage
0%50.0%100.0%Unattributed Quote+100.0 ptsWriter: 0.0%UW spokesperson: 100.0%100.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.