Kansas City bus agency wants to use AI facial recognition. Critics say it'll make riders less safe 35%

By Savannah Hawley-Bates0%

6/25/2026, 9:00:00 AM

BS Summary: This article contains 35 faulty reasoning types, including Optimism Bias, Pessimism Bias, and Post Hoc (False Cause), with Negativity Bias as the most egregious example at 21.5% saturation with 220 hits. Analysis detected 2,037 faulty-reasoning hits from 1,025 analyzed words, generating a BS Score of 42.7% and a BS Rank of 35% (14,219 of 21,887 articles). This article is better (less manipulative) than 65.00% of the article peer group.

The Kansas City Area Transportation Authority could be one of the first in the nation to add AI-powered facial recognition cameras to its bus fleet this fall. 
National nonprofits and a KCATA commissioner are concerned about the privacy and security of riders. 
Kansas City bus riders may have to get used to artificial intelligence cameras scanning their faces when they get on the bus this fall. 
The Kansas City Area Transportation Authority plans to add AI-powered security cameras to some of its buses to help strengthen security and more quickly detect banned riders. 
It would be among the first in the nation to use the cameras, making this program a test for how surveillance technology is used in public spaces like transit. 
“I think it is an opportunity for us to be proactive in terms of our safety,” said Tyler Means, the chief strategy officer for the KCATA. 
Critics say riders should worry about their privacy and security with the technology, and say the KCATA runs a risk of misidentifying and punishing innocent riders. 
Adam Schwartz is the privacy litigation director for the Electronic Frontier Foundation, a national nonprofit that defends digital freedoms. 
He said the technology is dangerous and shouldn’t be used by public entities like the KCATA. 
“This is a really terrible idea,” Schwartz said. 
“You should be able to ride the bus to get to work, to take your kids to school, without the government subjecting you to biometric surveillance and without you being screened to see whether you're of interest to law enforcement.” 
The KCATA originally planned to install the AI facial recognition cameras on five buses ahead of the World Cup. 
The agency had hoped to use the technology to help identify missing persons throughout the tournament, which has been known to increase the threat of human trafficking. 
But the money that was supposed to fund the cameras  part of a grant from the U.S. 
Department of Justice  was stalled due to the partial government shutdown that began in February. 
The delayed money meant the KCATA didn’t install the facial recognition cameras ahead of the tournament. 
Now, it wants to expand the program to 30 of its buses in the fall. 
The Electronic Frontier Foundation and other national groups like the American Civil Liberties Union are against facial recognition software used for surveillance. 
Schwartz noted that police departments across the country have made more than a dozen false arrests because of facial recognition technology. 
Means told KCUR he doesn’t think riders will have major privacy concerns with the AI-powered cameras. 
He views the camera program, which would initially be put on a fraction of its buses, as a test. 
He said the KCATA is “intrigued to see if it even works” before expanding. 
“I honestly think that as this becomes more commonplace, the concerns will go away,” Means said. 
“Anytime anyone claims that this was not them, they could have the right to appeal and then verify that it wasn't them. 
I think that's definitely something that has to be considered and put in place.” 
Mans said the agency hopes the AI cameras will help increase safety on its buses. 
Dealing with unruly passengers has long been a complaint of KCATA drivers. 
The transit agency currently has security cameras on all its buses. 
Pictures of banned riders captured by the existing cameras are posted for drivers and staff to see and intervene if those people board the bus. 
Mans said the AI-powered cameras would work faster to identify the banned riders and notify the KCATA’s security team  private security officers and two armed police officers  to respond. 
Videos captured by the AI cameras would be stored by the KCATA for five years, as current video footage is now. 
Schwartz said that while both types of cameras would capture footage, the biometric data the AI-powered cameras collect is different from the current cameras that are used for surveillance. 
“This is an escalation of surveillance technology that, to my knowledge, is unprecedented in this country, in terms of getting on a bus,” Schwartz said. 
“It is one that the government simply should not be engaged in at all.” 
Chronic underfunding and years of feuds with area leaders has resulted in slow and spotty bus service around the Kansas City area. 
That means many of the agency’s riders are on the bus as a last resort. 
Johnathan Duncan, a Kansas City Council member who sits on the KCATA’s board of commissioners, said the “KCATA has a trust issue” and that the AI cameras will only make it worse and further decrease ridership. 
Before the KCATA can install the AI-powered cameras, it has to update the fleet’s Wi-Fi. 
It plans to do that in mid-July. 
Then the agency will undergo a procurement process to pick the company that will provide the cameras. 
Only then can the agency consider adding the cameras to buses. 
The KCATA plans to use the cameras only for bus security and to compare against its banned riders list. 
The agency would need a contract with local law enforcement agencies to use the footage for comparison against missing persons or wanted lists. 
So far, Means said that’s not part of the plan. 
Mans said he doesn’t believe this will pose a major issue to riders when the cameras are implemented. 
He said security is a big issue for the KCATA and other options people have suggested, like putting a police officer on every bus, are not feasible. 
Duncan said as a commissioner he’ll “fight hard to ensure we never implement” the cameras. 
He cited the agency’s transit ambassador program as one noninvasive way to increase safety on the bus. 
“I challenge staff and the board to ask thoughtful questions about how we better understand riders without utilizing AI to capture their information or having privacy concerns and issues,” Duncan said. 
“Why are we, one, rolling out a system that's going to decrease ridership, and two, putting ourselves in a precarious situation where we could put at jeopardy riders' personal information?” 
Article reasoning-pattern comparisonThis article: 3.0%Savannah Hawley-Bates: 1.6%Columbia Missourian: 1.7%Confirmation Bias3.0%This article: 0.0%Savannah Hawley-Bates: 0.8%Columbia Missourian: 0.8%Anchoring Bias0.0%This article: 9.0%Savannah Hawley-Bates: 2.1%Columbia Missourian: 2.7%Availability Heuristic9.0%This article: 2.8%Savannah Hawley-Bates: 0.6%Columbia Missourian: 0.9%Representativeness Heuristic2.8%This article: 0.0%Savannah Hawley-Bates: 0.2%Columbia Missourian: 0.4%Hindsight Bias0.0%This article: 3.5%Savannah Hawley-Bates: 1.5%Columbia Missourian: 1.3%Overconfidence Bias3.5%This article: 6.4%Savannah Hawley-Bates: 7.2%Columbia Missourian: 5.5%Framing Effect6.4%This article: 3.9%Savannah Hawley-Bates: 1.2%Columbia Missourian: 1.0%Loss Aversion3.9%This article: 7.8%Savannah Hawley-Bates: 0.7%Columbia Missourian: 0.7%Status Quo Bias7.8%This article: 3.3%Savannah Hawley-Bates: 0.4%Columbia Missourian: 0.3%Sunk Cost Effect3.3%This article: 14.9%Savannah Hawley-Bates: 7.7%Columbia Missourian: 4.5%Optimism Bias14.9%This article: 11.7%Savannah Hawley-Bates: 1.1%Columbia Missourian: 1.6%Pessimism Bias11.7%This article: 21.5%Savannah Hawley-Bates: 6.9%Columbia Missourian: 5.4%Negativity Bias21.5%This article: 5.6%Savannah Hawley-Bates: 3.1%Columbia Missourian: 1.7%Self-Serving Bias5.6%This article: 2.6%Savannah Hawley-Bates: 0.8%Columbia Missourian: 0.6%Fundamental Attribution Error2.6%This article: 0.0%Savannah Hawley-Bates: 0.2%Columbia Missourian: 0.2%Actor-Observer Bias0.0%This article: 2.1%Savannah Hawley-Bates: 1.6%Columbia Missourian: 1.6%In-Group Bias2.1%This article: 0.0%Savannah Hawley-Bates: 0.2%Columbia Missourian: 0.4%Out-Group Homogeneity Bias0.0%This article: 4.3%Savannah Hawley-Bates: 1.7%Columbia Missourian: 2.4%Halo Effect4.3%This article: 0.0%Savannah Hawley-Bates: 0.2%Columbia Missourian: 0.0%Horn Effect0.0%This article: 0.0%Savannah Hawley-Bates: 0.0%Columbia Missourian: 0.0%Dunning-Kruger Effect0.0%This article: 3.0%Savannah Hawley-Bates: 0.6%Columbia Missourian: 0.9%Recency Bias3.0%This article: 4.5%Savannah Hawley-Bates: 0.3%Columbia Missourian: 0.3%Primacy Effect4.5%This article: 0.0%Savannah Hawley-Bates: 0.0%Columbia Missourian: 0.0%Blind-Spot Bias0.0%This article: 2.2%Savannah Hawley-Bates: 0.6%Columbia Missourian: 0.5%Ad Hominem2.2%This article: 0.0%Savannah Hawley-Bates: 0.1%Columbia Missourian: 0.2%Straw Man0.0%This article: 7.0%Savannah Hawley-Bates: 2.7%Columbia Missourian: 2.9%Appeal to Authority7.0%This article: 10.5%Savannah Hawley-Bates: 1.2%Columbia Missourian: 1.1%False Dilemma10.5%This article: 5.1%Savannah Hawley-Bates: 0.8%Columbia Missourian: 1.0%Slippery Slope5.1%This article: 0.0%Savannah Hawley-Bates: 0.1%Columbia Missourian: 0.1%Circular Reasoning0.0%This article: 8.5%Savannah Hawley-Bates: 3.0%Columbia Missourian: 3.6%Hasty Generalization8.5%This article: 0.0%Savannah Hawley-Bates: 0.2%Columbia Missourian: 0.2%Red Herring0.0%This article: 0.0%Savannah Hawley-Bates: 2.0%Columbia Missourian: 0.7%Bandwagon0.0%This article: 7.9%Savannah Hawley-Bates: 5.6%Columbia Missourian: 5.2%Appeal to Emotion7.9%This article: 10.1%Savannah Hawley-Bates: 0.8%Columbia Missourian: 0.6%Begging the Question10.1%This article: 10.7%Savannah Hawley-Bates: 2.1%Columbia Missourian: 2.0%Post Hoc (False Cause)10.7%This article: 0.0%Savannah Hawley-Bates: 0.0%Columbia Missourian: 0.0%Tu Quoque0.0%This article: 3.3%Savannah Hawley-Bates: 0.2%Columbia Missourian: 0.4%Burden of Proof3.3%This article: 1.7%Savannah Hawley-Bates: 0.1%Columbia Missourian: 0.2%Appeal to Nature1.7%This article: 0.0%Savannah Hawley-Bates: 0.1%Columbia Missourian: 0.2%Composition/Division0.0%This article: 3.2%Savannah Hawley-Bates: 1.8%Columbia Missourian: 2.6%Anecdotal3.2%This article: 0.0%Savannah Hawley-Bates: 0.0%Columbia Missourian: 0.1%No True Scotsman0.0%This article: 7.3%Savannah Hawley-Bates: 0.8%Columbia Missourian: 1.3%Ambiguity (Equivocation)7.3%This article: 0.0%Savannah Hawley-Bates: 0.0%Columbia Missourian: 0.0%Gambler’s Fallacy0.0%This article: 1.9%Savannah Hawley-Bates: 0.0%Columbia Missourian: 0.1%Middle Ground1.9%This article: 1.6%Savannah Hawley-Bates: 0.1%Columbia Missourian: 0.1%Personal Incredulity1.6%This article: 1.9%Savannah Hawley-Bates: 0.2%Columbia Missourian: 0.1%Special Pleading1.9%This article: 0.0%Savannah Hawley-Bates: 0.1%Columbia Missourian: 0.1%Genetic Fallacy0.0%This article: 2.0%Savannah Hawley-Bates: 0.7%Columbia Missourian: 0.8%Unattributed Quote2.0%This article: 0.7%Savannah Hawley-Bates: 0.3%Columbia Missourian: 0.7%Quote-first Misdirection0.7%This article: 0.0%Savannah Hawley-Bates: 1.8%Columbia Missourian: 2.9%Biased Writer Voice0.0%This article: 3.0%Savannah Hawley-Bates: 0.7%Columbia Missourian: 1.3%Indoctrination3.0%This article: 0.0%Savannah Hawley-Bates: 0.4%Columbia Missourian: 0.7%Politically Left Leaning Bias0.0%This article: 0.0%Savannah Hawley-Bates: 0.0%Columbia Missourian: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Savannah Hawley-Bates: 0.3%Columbia Missourian: 1.7%Attempt to Sell a Product or S…0.0%

1025 words analyzed.

Speakers

4speakers50%attributed speech515writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 0.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageTyler Means • 26 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageAdam Schwartz • 16 words • 0.0% coverageAdam Schwartz • 8 words • 0.0% coverageAdam Schwartz • 40 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageAdam Schwartz • 21 words • 100.0% coverageTyler Means • 16 words • 0.0% coverageTyler Means • 19 words • 0.0% coverageTyler Means • 14 words • 0.0% coverageTyler Means • 16 words • 0.0% coverageTyler Means • 22 words • 0.0% coverageTyler Means • 14 words • 0.0% coverageMans • 15 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageMans • 31 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageAdam Schwartz • 29 words • 0.0% coverageAdam Schwartz • 25 words • 0.0% coverageAdam Schwartz • 14 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageJohnathan Duncan • 36 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageTyler Means • 10 words • 0.0% coverageMans • 18 words • 0.0% coverageMans • 27 words • 0.0% coverageJohnathan Duncan • 15 words • 0.0% coverageJohnathan Duncan • 17 words • 0.0% coverageJohnathan Duncan • 31 words • 100.0% coverageJohnathan Duncan • 30 words • 0.0% coverage
Selected voice

Adam Schwartz

100%flagged-word coverage
153 attributed words30% of attributed speech88% writer coverage
0%7.5%15.0%Unattributed Quote+13.7 ptsWriter: 0.0%Adam Schwartz: 13.7%13.7%Quote-first Misdirection-1.4 ptsWriter: 1.4%Adam Schwartz: 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.