Poll-arized: Perspectives on our political climate

Newsrooms have been decimated. The younger generation doesn't closely follow the news. Attention spans have withered in the TikTok age. Can we count on journalism to serve its Fourth Estate function and deliver fair, accurate coverage of the election?

Mike McDevitt, a former editorial writer and reporter, isn't convinced the press has learned its lessons from the 2016 cycle, when outlets chased ratings and the appearance of impartiality over a commitment to craft that might have painted more accurate portraits of both candidates. High-quality reporting, he said, may mean less focus on finding scoops and more time sharing resources to chase impactful stories.

How can journalism be better?
"A lot of journalists might disagree with me, but I think news media should be less competitive among each other and find ways to collaborate, especially with the industry gutted. And the news can't lose sight of what's important by chasing clickable stories. Covering chaos and conflict is tempting, but journalism's interests in this respect do not always align with the security of democracy. While threats to democracy are real, amplifying chaos is not how news media should operate during an era of democratic backsliding."

After the 2016 election, Brian C. Keegan was searching for ways to use his interests in the computer and social sciences in service of democracy. That's driven his expertise in public-interest data science-how to make closed data more accessible to voters, journalists, activists and researchers. He looks at how campaigns can more effectively engage voters, understand important issues and form policies that address community needs.

You've called the 2012 election an "end of history" moment. Can you explain that in the context of what's happening in 2024?
"In 2012, we were coming out of the Arab Spring, and everyone was optimistic about social media. The idea that it could be a tool for bots and state information operations to influence elections would have seemed like science fiction. Twelve years later, we've finally learned these platforms are not neutral, have real risk and can be manipulated. And now, two years into the large language model moment, people are saying these are just neutral tools that can only be a force for good. That argument is already falling apart.

"You could actually roll the clock back even further, to the 1960s and '70s, when people were thinking about Silent Spring and Unsafe at Any Speed, and recognizing there are all these environmental, regulatory, economic and social things all connected through this lens of the environment. Like any computing system, when it comes to data, if you have garbage in, you get garbage out. The bias and misinformation we put into these A.I. systems are polluting our information ecosystem in ways that journalists, activists, researchers and others aren't equipped to handle."