BS Summary: This article contains 28 faulty reasoning types, including Optimism Bias, Attempt to Sell a Product or Service, and Burden of Proof, with Negativity Bias as the most egregious example at 17.7% saturation with 174 hits. Analysis detected 1,668 faulty-reasoning hits from 983 analyzed words, generating a BS Score of 9.7% and a BS Rank of 3% (26,241 of 26,881 articles). This article is better (less manipulative) than 97.60% of the article peer group.

Portlanders love their pro women’s sports teams. 
The Thorns have enjoyed tremendous support since they began playing in 2013, and the Fire has hosted multiple sold-out games at the Moda Center this year—the team’s first season. 
Portland fans expect a kind of loyalty in return, and when they don’t like a decision a team is making, they’ll make that clear. 
Earlier this year, Thorns fans revolted against a partnership the team signed with home security company Ring, even going so far as to remove the company’s patch from their jerseys (the Thorns are still partnered with Ring). 
Last week, both teams announced a new partnership with Treasure AI, an agentic AI platform that helps marketers make decisions based on customer data. 
In the post, the Fire vaguely described the partnership as a new way to “build stronger connections with every fan.” 
According to the press release, the partnership also includes lots of Treasure AI branding, like sleeve patches for the Thorns, and logos on the Fire’s practice jerseys. 
Fans immediately revolted and flooded the Fire’s Instagram comments to voice their objections. 
As one person put it, “I love you, but this sucks. 
An AI ad about caring about people?” 
The Fire shared a statement from RAJ Sports (the brother-sister duo who own both teams) with the Mercury . 
“Our partnership with Treasure AI is focused on the fan experience: things like personalized offers, easier ticket access, and better communication around games and events,” the statement read. 
 [The partnership helps] the Fire and Thorns to create more personalized communications, respond more effectively, and deliver the experiences that make Portland sports special.” 
Treasure AI was originally solely a customer data platform, or CDP. 
A CDP is a marketing tool that pulls customer data from multiple sources to create individualized profiles that marketers use to make decisions. 
That changed recently. 
Now, the platform works as a form of agentic AI—which means the system can act on its own to “simplify how teams interact with customer data,” according to the company’s chief product and growth officer, Rafa Flores. 
However, while not the same, agentic AI often works hand-in-hand with generative AI to complete its tasks. 
As far as public-facing Treasure AI-backed experiences go, Flores acknowledged the company and teams are still in “early concepts,” and there are “so many different things” they could do together. 
“The first [activation] is the Thorns’ community partner program, where we get to choose to work with a local charity to bring some of those experiences to folks who maybe will never get to experience [games] on their own dime,” Flores explained, such as “families facing medical hardship,” according to the press release. 
Those kinds of values align with the Fire and Thorns, as well as the WNBA and the NWSL—at least on the surface. 
While values are easy to espouse, and equal access to games is an issue that should be addressed—a lot of fans still aren’t sold. 
This includes James Kerti, who will attend his first-ever Fire game this week. 
Kerti can recall watching WNBA games while on summer break; the idea of having a team in Portland he can see in-person is understandably exciting. 
Kerti saw the post that announced the new partnership and was surprised and “disappointed.” 
Though he is “no stranger to how brand partnerships work,” this one felt different. 
“Trying to spin your partnership with an AI company into a message about how you’re going to use your fans’ personal data to build a better community comes across as insulting,’ he added. 
That’s especially the case if rumors that the commercial was created with AI are true, he continued. 
For Kerti, the deal is lacking “human connection.” 
While speaking to the Mercury , Dr. 
Naveen Gudigantala, who is part of the School of Business at the University of Portland, agreed it’s possible Treasure AI could have underestimated, or not even considered, that the teams’ fans might be tired of being blatantly marketed to. 
Agentic AI gives companies the ability to “correlate different pieces of data” about customers “in a very advanced way,” Gudigantala said. 
“Instead of running a very advanced machine learning algorithm, I can simply use a Chatbot” to come up with deals tailored to an individual. 
Running those programs in the background can be part of an effort to improve the customer experience, but if the teams “are not explaining [that], I think that’s a problem.” 
There’s another problem, too: representatives for the teams haven’t fully explained what the partnership will mean for fans in detail. 
“In Europe there are a lot of restrictions around the privacy of data… companies have to get permission to use the pictures or data [of customers],” Gudigantala explained. 
In the United States, these protections “are lax.” 
So the Fire and Thorns could potentially use photos of fans to promote this partnership, something a lot of fans would be against. 
There’s no explicit indication that will happen, but no reassurance that it won’t. 
“They probably have to think about the right way to communicate about how they’re using AI, and what exactly it means to them,” he suggested. 
Otherwise, “both parties” will continue to have a “misunderstanding about it.” 
Dr. 
Ashley Hass, assistant professor of marketing at the University of Portland, also pointed to a lack of transparency as a larger part of the problem. 
In addition to fans’ valid concerns about the use of AI, both Treasure AI and the teams “aren’t doing a great job” of communicating what the partnership is about, she told the Mercury . 
“I really do enjoy consumers pushing back, though,” Hass added. 
“I think that it’s super fair, and hopefully the teams are going to come out and say, ‘Whoa, whoa, whoa, this [part of the partnership] aligns with our values, not the other [aspects of AI] that are going on.” 
Article reasoning-pattern comparisonThis article: 4.3%Stephanie Kaloi: 1.4%Portland Mercury: 2.3%Confirmation Bias4.3%This article: 0.0%Stephanie Kaloi: 0.8%Portland Mercury: 0.4%Anchoring Bias0.0%This article: 1.3%Stephanie Kaloi: 2.5%Portland Mercury: 2.5%Availability Heuristic1.3%This article: 0.0%Stephanie Kaloi: 2.0%Portland Mercury: 0.7%Representativeness Heuristic0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.2%Hindsight Bias0.0%This article: 3.1%Stephanie Kaloi: 0.6%Portland Mercury: 0.9%Overconfidence Bias3.1%This article: 1.0%Stephanie Kaloi: 5.2%Portland Mercury: 4.0%Framing Effect1.0%This article: 3.1%Stephanie Kaloi: 0.0%Portland Mercury: 0.5%Loss Aversion3.1%This article: 0.0%Stephanie Kaloi: 0.3%Portland Mercury: 0.3%Status Quo Bias0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.2%Sunk Cost Effect0.0%This article: 15.7%Stephanie Kaloi: 1.1%Portland Mercury: 1.5%Optimism Bias15.7%This article: 6.7%Stephanie Kaloi: 0.7%Portland Mercury: 0.9%Pessimism Bias6.7%This article: 17.7%Stephanie Kaloi: 7.6%Portland Mercury: 5.9%Negativity Bias17.7%This article: 6.4%Stephanie Kaloi: 0.0%Portland Mercury: 1.1%Self-Serving Bias6.4%This article: 7.3%Stephanie Kaloi: 0.4%Portland Mercury: 0.7%Fundamental Attribution Error7.3%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.0%Actor-Observer Bias0.0%This article: 2.4%Stephanie Kaloi: 2.2%Portland Mercury: 3.3%In-Group Bias2.4%This article: 0.0%Stephanie Kaloi: 1.1%Portland Mercury: 0.8%Out-Group Homogeneity Bias0.0%This article: 2.2%Stephanie Kaloi: 1.0%Portland Mercury: 1.8%Halo Effect2.2%This article: 0.0%Stephanie Kaloi: 0.2%Portland Mercury: 0.2%Horn Effect0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.0%Dunning-Kruger Effect0.0%This article: 1.3%Stephanie Kaloi: 2.9%Portland Mercury: 1.0%Recency Bias1.3%This article: 3.0%Stephanie Kaloi: 0.3%Portland Mercury: 0.2%Primacy Effect3.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.1%Blind-Spot Bias0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 2.2%Ad Hominem0.0%This article: 0.0%Stephanie Kaloi: 0.2%Portland Mercury: 0.5%Straw Man0.0%This article: 8.4%Stephanie Kaloi: 2.4%Portland Mercury: 1.9%Appeal to Authority8.4%This article: 7.0%Stephanie Kaloi: 0.8%Portland Mercury: 1.0%False Dilemma7.0%This article: 3.5%Stephanie Kaloi: 0.3%Portland Mercury: 0.4%Slippery Slope3.5%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.1%Circular Reasoning0.0%This article: 9.1%Stephanie Kaloi: 3.8%Portland Mercury: 4.9%Hasty Generalization9.1%This article: 0.0%Stephanie Kaloi: 1.0%Portland Mercury: 0.4%Red Herring0.0%This article: 1.3%Stephanie Kaloi: 0.5%Portland Mercury: 0.6%Bandwagon1.3%This article: 7.0%Stephanie Kaloi: 2.9%Portland Mercury: 5.0%Appeal to Emotion7.0%This article: 1.7%Stephanie Kaloi: 0.2%Portland Mercury: 0.6%Begging the Question1.7%This article: 0.0%Stephanie Kaloi: 1.2%Portland Mercury: 0.8%Post Hoc (False Cause)0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.2%Tu Quoque0.0%This article: 11.1%Stephanie Kaloi: 0.2%Portland Mercury: 0.4%Burden of Proof11.1%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.1%Appeal to Nature0.0%This article: 5.4%Stephanie Kaloi: 0.0%Portland Mercury: 0.2%Composition/Division5.4%This article: 6.9%Stephanie Kaloi: 1.2%Portland Mercury: 1.3%Anecdotal6.9%This article: 0.0%Stephanie Kaloi: 0.4%Portland Mercury: 0.1%No True Scotsman0.0%This article: 9.8%Stephanie Kaloi: 4.2%Portland Mercury: 0.8%Ambiguity (Equivocation)9.8%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.0%Middle Ground0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.2%Personal Incredulity0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.0%Special Pleading0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.1%Genetic Fallacy0.0%This article: 0.0%Stephanie Kaloi: 2.6%Portland Mercury: 1.1%Unattributed Quote0.0%This article: 0.0%Stephanie Kaloi: 2.4%Portland Mercury: 0.7%Quote-first Misdirection0.0%This article: 3.1%Stephanie Kaloi: 7.0%Portland Mercury: 13.9%Biased Writer Voice3.1%This article: 5.4%Stephanie Kaloi: 3.7%Portland Mercury: 2.0%Indoctrination5.4%This article: 0.0%Stephanie Kaloi: 2.4%Portland Mercury: 2.5%Politically Left Leaning Bias0.0%This article: 0.0%Stephanie Kaloi: 0.0%Portland Mercury: 0.1%Politically Right Leaning Bias0.0%This article: 14.5%Stephanie Kaloi: 0.7%Portland Mercury: 5.1%Attempt to Sell a Product or S…14.5%

983 words analyzed.

Speakers

5speakers59%attributed speech407writer words
Selected voice

Rafa Flores

100%flagged-word coverage
120 attributed words21% of attributed speech61% writer coverage
0%37.5%75.0%Attempt to Sell a Product +75.0 ptsWriter: 0.0%Rafa Flores: 75.0%75.0%Indoctrination+44.2 ptsWriter: 0.0%Rafa Flores: 44.2%44.2%Biased Writer Voice-7.4 ptsWriter: 7.4%Rafa Flores: 0.0%0.0%

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.