Media Party New York

The election of George Santos to Congress—despite his fraudulent background—raised serious questions about the media's inability to vet a new wave of political candidates with unknown backgrounds. This case inspired the creation of Sunlight Research Center, a non-partisan non-profit helping local newsrooms scrutinize 2024 candidates. In this keynote I will share the lessons Sunlight has learned about the real stories hiding behind the campaign biographies, the tech needed to enable deep investigation, and what hard-pressed newsrooms require in order to do this accountability reporting.

The 2023 elections in Nigeria experienced the first use of significantly new technology, directly connected to the election process itself but also coinciding with the proliferation of media-manipulating tools such as AI-generated deep fakes. Some examples include paid posts reportedly linking candidates to militant or separatist groups which filled social media platforms. The Nigeria presidential election was an instance of a country’s democratic process being impacted by such means. The scope of the problem was beyond the resources of local journalists to address. With wider access to more divergent technology, I believe the US can gain important risk and mitigation learnings from the critical role media tech played in Nigeria's 2023 elections. Learning: The session will explore how media innovation through the use of deeply insightful, investigative data stories and more effective fact-checking and deepfakes-exposing tools exposed these schemes and set a precedence for what might be improved voter understanding of media manipulation tricks and how to avoid it on an individual and community level.

What I have learned investigating AI impact on 2024 elections

Answering: How has the space evolved for both AI and hallucination detection. How GPTZero has grown and learned from both writers, journalists, and enterprise customers in adopting AI responsibly and keeping our information ecosystems safe.

In an industry set on rapid information flow and shortened attention spans, generative AI offers a powerful tool to enhance the value of journalism, particularly during the high-stakes environment of an election. By leveraging AI to delve into archives, we provide crucial historical context, draw meaningful connections between past and present events, and set a more relational tone for public discourse that people can make sense of. Moreover, the integration of generative AI with human-centric interactions, social listening, location data, sentiment analysis, and other indicators (economy, employment, health…) for each community, creates a multidimensional view of the election context is specific areas that makes it easy for the user to find familiar ground from where to absorb the information. This synthesis of information allows journalists to uncover patterns, anticipate trends, and provide more comprehensive coverage tailored to specific demographics or regions which also allows for richer, deeper and longer interactions. By harnessing these technological capabilities, we can take election journalism beyond the “we and them” horse-race reporting or some outlandish quote, offering citizens in-depth, contextualised information that they can relate and build upon from, so that they can make an informed decision at the ballot box.

Using the case on Zac Brettler's visual investigation as an example. This talk will delve into how data-driven visual storytelling, how we combined new evidence with intuitive visual storytelling to bring back to life Zac’s case and how a multidisciplinary team was key for the end result. By combining new evidence and data and fact checks with intuitive visual storytelling, this project aimed to take the user step by step on how that night unfolded allowing them to engage with the information on a personal level and where in the final result collaboration was key.

I want to highlight the inequality on Wikipedia. The Wiki community has been working hard to solve for this problem: one Wikiproject at a time.