While most Americans are focusing on who will be the next President of the United States following Tuesday’s general election in the US, there are hundreds of other important political races to be decided as well. There are 33 US Senate seats and another 435 contests in the House of Representatives up for grabs.
Not to mention the hundreds of other races at the various state and county-levels of government that need to settled.
Perplexity Unveils First AI-Powered National Election Tracker
To keep track of the national contests, Perplexity announced the launch on Friday of the first publicly available AI-based election tracker. Beginning on Tuesday, the election information hub will be powered using data from the Associated Press as well as Democracy Works.
Users can visit the Perplexity link and enter their zip code. All the races and ballot measures that apply to that zip code will populate the screen, which the user can explore — the interface is pictured below.
“We want to make it as simple as possible to receive trusted, easy-to-understand information to inform your voting decisions. For each response, you can view the sources that informed an answer, allowing you to dive deeper and verify referenced materials. Whether you’re seeking to understand complex ballot measures, verify candidate positions, or simply find your polling place,” the company statement reads.
Perplexity Answered Some Lingering Questions
In an email exchange seeking answers about whether Perplexity was paying AP and Democracy Works; the company’s rationale behind the project and timing; as well as how Perplexity’s large language model might mitigate hallucination allegations of creating fake news by multiple media outlets earlier this year — a single blanket statement was received.
“We want to make it easier for people to make informed choices on all ballot items, including elected offices and ballot measures. We waited to release this to the public until we could conduct the appropriate testing,” wrote a company spokesperson.
“To clarify, Perplexity uses LLMs for summarizing content, but is designed to optimize for accuracy. We use a process called Retrieval-Augmented Generation to identify relevant information and summarize it in a way that’s tailored to a user’s query. Answers are not utilizing stored knowledge from a model’s training data, which makes us different from other AI chatbots. It’s also why we chose to work with organizations like the AP and Democracy Works to provide us with up-to-date information on ballot items and election results,” the spokesperson’s message concluded.
AI Experts Take Perplexity’s Election Hub For A Spin
In general, the AI influencers and experts I spoke with were impressed with the concept behind the election specific info hub. Kirk Borne, PhD is an internationally recognized thought leader and speaker within the AI and data space, as well as founder of the Data Leadership Group and he was named to a recent list of 100 Most Influential People in AI.
“I believe that this platform will be very helpful for many people: with “less talk, more data” and “fewer opinions, more analysis,” Dr. Borne wrote via text.
“The specificity, utility, currency, and accuracy—four key dimensions of all large language models and Generative AI—are 100% dependent on their data sources. Very broad LLMs that try to answer all possible end-user queries, utilizing massive datasets can be impossible to tame in all 4 key dimensions. Perplexity’s focus on a hyper-targeted and tightly constrained use case, with a limited spectrum of end-user queries, thus makes sense and is consistent with having such formalized data sources,” he explained.
Ahmed Banafa, PhD is a technology expert and Engineering Professor at San Jose State University. He thinks there could be benefits from the hub as well.
“Using AI to provide real-time updates on voting requirements, polling locations and candidate details—the platform aims to increase voter engagement and support informed choices. I checked it and I found the list of candidates in my area, it was accurate with good information about each candidate and each measure on the ballot. This approach reflects the growing trend of using AI to simplify access to critical election information, with a user-friendly design that makes it easy for voters to find what they need quickly,” Dr. Banafa wrote via email.
He also applauded Perplexity’s partnering with AP and Democracy Works for this initiative, which is intended to give the AI model authoritative, up-to-date election data. He noted that using such trusted data sources lends credibility of the provided information and reduces reliance on less dependable outlets or data suppliers.
“These collaborations are crucial for providing accurate and trustworthy content, especially during elections, when misinformation can have serious impacts. This will save the service the steps of verifications of the information as the AP/DW already did that work. These partnerships uphold high standards of election integrity, helping ensure users receive only thoroughly vetted, current information,” added Dr. Banafa.
Conor Grennan is chief AI Architect at NYU Stern School of Business as well as CEO and founder of the consultancy AI Mindset. In an email exchange, he wrote that he ran extensive queries to test the model and gained some interesting insights.
“Perplexity’s election hub addresses a crucial need by centralizing essential election information, from candidate profiles to voting logistics. While their factual information on voting procedures is reliable and serves a valuable public function, the platform faces challenges in presenting candidate information equitably,” he wrote.
“A comparison of candidate pages reveals inconsistencies in tone and framing — Harris’s page emphasizes historic achievements, while Trump’s page takes a markedly different editorial approach. This highlights a fundamental challenge with LLM-based platforms: maintaining consistent, unbiased presentation across variable content generation,” Grennan explained.
He also lauded the team-up with AP and DW stating that it should help ensure greater consistency in information delivery.
“This is particularly important for candidate information, where varying source material can lead to dramatically different presentations of the same individual. Having authorized data vendors helps establish a baseline for information quality,” noted Grennan.
Hallucination Risks May Still Remain For Perplexity
Despite the data collaborations, Grennan stated that they’re not a panacea for all the risks such as faulty article summations and hallucinations that can plague LLMs — even those that use RAG technology.
“While partnerships with established data providers like AP and Democracy Works should definitely help reduce technical hallucinations, they don’t fully address the challenge of perceived bias in presentation. Even factually accurate information can create different impressions based on framing and emphasis. The contrast in candidate biographies demonstrates how LLMs can inadvertently reflect existing biases in their training data, potentially affecting how information is contextualized and presented,” Grennan concluded.
Dr. Banafa echoed those sentiments writing that even if third-party data providers have reliable, rigorous fact-checking standards — the AI models sourcing those data can benefit from continual monitoring and refinement.
“While trusted sources lower the chances of misinformation, continuous monitoring and validation of AI outputs are still crucial to maintain reliability and trustworthiness,” he wrote. “It’s equally important for users to crosscheck critical details, given the potential consequences of even minor inaccuracies in election information.”
However, Dr. Borne was a bit more optimistic that the specific use case that Perplexity has developed should further curtail the incidence of hallucinations.
“The typical hallucinations arise in LLMs when the end user queries are essentially unconstrained on the vast historical knowledgebase of the world. Those LLMs cannot give a truly accurate and complete—and short—answer to a complex question any more than a physics professor can explain the fullness and the intricacies of quantum theory or general relativity 100% accurately in a few sentences to a general audience. I am optimistic that Perplexity will do better than the typical LLM track record,” wrote Dr. Borne.
What Might Perplexity’s Election Tracker Mean For The Future
Dr. Banafa believes that Perplexity’s unique model may hold promise for the future.
“But it’s essential to consider the broader challenges of using AI in election contexts. AI chatbots have previously provided incorrect or partially correct answers to election-related questions. This highlights the need for continuous evaluation and refinement of AI systems to meet the rigorous standards required for sharing accurate election information. Additionally, advancements in AI transparency and interpretability could further reduce errors, fostering more trust in AI-generated election information,” he noted.
While Dr. Borne described Perplexity’s election tracker platform as an experiment, that’s centered around the highly charged and personalized human sentiments and context-driven narratives associated with modern politics.
“We will see if this works well enough to be considered a success, or—if like any science-technology implementation—we learn from it and refine it for the next time. In this specific instance, I believe that the outcomes should be positive since there is more “technology implementation” than “scientific experimentation” involved, but the latter is definitely not 0%. Perplexity’s project is still ultimately an LLM after all,” he concluded.
