Michelle Ma

I recommend this post by Alexandra Smith on "total journalism reach" -- The 19th's new readership metric. It's full of insights and data wisdom. One takeaway: prioritize consistency across platforms without being overly rigid. For example, #Instagram views get counted because their posts basically republish an abridged version of the story -- an increasingly common publishing strategy -- not just because the machine needs more #data. Above all, this model is attentive to the question of *why* we measure #readership. I've always felt that digital interactions are metaphorical -- as summed up in that now quaint adage: "tweets, not endorsements." What does readership *represent*? Answering that question will help you collect quality, consistent data on #news consumption. And that will bring us closer to the readers we serve. https://lnkd.in/gxn4wmYy

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Congestion pricing was supposed to start today in New York City. It didn't after Gov. Kathy Hochul earlier this month announced an indefinite pause. My latest article in The Wall Street Journal traces the rise and pause of congestion pricing — a tale that involves 17 years and around $700 million. The abrupt reversal, which some attribute to Hochul’s reluctance to impose a new fee in an election year, leaves metro New York grappling with a historic missed opportunity and fiscal mess. There is no relief in sight for the city’s traffic congestion, which is the worst in the world, according to data published last week. The epic collapse in New York shows how a fear of dramatic change can give the status quo stubborn power over those trying to solve some of America’s most intractable challenges. That leaves policymakers nibbling at the edges of deeply rooted problems, even after investing huge sums of money and political capital. Blown up in a New York minute were plans for around $15 billion of planned improvements to the city’s ailing mass-transit system, the largest transportation network in North America. The reversal cast aside around $700 million in meticulous prep work, including a $555-million contract to install tolling cameras—which are already up and ready to go—and $33 million for a customer-service center with 100 employees who have already been brought on, officials said. Planners invested thousands of hours, including going to London and Stockholm to research their congestion-pricing programs, according to people familiar with the travel. What was supposed to be a transformative moment when New York led the way and boldly tackled traffic congestion, air pollution and transit funding, has instead turned into a surprising loss for a broad coalition that includes major employers, real-estate developers and subway riders. Surprised by the reversal were Hochul’s own lieutenants, including Janno Lieber, a fierce champion of congestion pricing and the chief executive of the Metropolitan Transportation Authority. The MTA—which carries around 5.5 million passengers each day in the New York metro area—now faces a $16.5 billion-financial hole from the loss of money from congestion pricing and federal matching funds. Just days after Hochul’s announcement, a beleaguered Lieber joined a conference call with advocates in which he said he was equally shocked and remained committed to the program, three people familiar with the exchange said. “This is devastating,” he said, according to a person on the call. Here's the latest from me and Joseph De Avila —>

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I would add: make sure you know what the reporter you're pitching to actually covers. Because in the credit team we write about any industry, we need to brace for pitches of stuff that doesn’t have to do with our regular beat very often. Sending an email saying "as a media representative covering topics related to urban tech" to someone covering credit-related matters just shows you either have no idea who you are addressing your message to or that you're sending it to a distribution list poorly built. Mention of honor to the most random pitches based on "studies" about things such as the sexiness of the characters of some tv drama.

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As someone passionate about the intersection of media and technology, I believe journalists must develop a firm grasp of artificial intelligence (AI) to leverage its potential thoughtfully and subject AI systems to public accountability. While there's been much hype around AI, ranging from overblown optimism to doomsday scenarios, what needs to be added to the capabilities and limitations of AI systems as they exist today is often missing. Djordje Padejski, in his insightful article "Time for Journalists to Investigate AI Systems," argues that the media has largely failed to thoroughly investigate modern AI technologies, partly due to a lack of AI competencies and the perception that journalists are ill-equipped to tackle the technical complexities involved. He's spot on. He notes, "AI systems are created by human design, engineers, executives, board members, and investors who should be held accountable for the outcomes of these products." It's critical to educate the public that AI is a tool shaped by the motives, biases, and choices of the humans and organizations wielding it. We must look beyond the technical dimensions to investigate the human context. Only by understanding the human forces behind AI can we paint a complete picture of its societal implications. While powerful, today's AI lacks human-like intelligence and contextual understanding. We need to move past "black box" framings and shed light on how these systems work, including their potential biases, privacy risks, and unintended consequences. The media must learn to audit algorithms and investigate AI systems, from conducting experiments to test AI's performance to employing investigative techniques like background research and critical questioning of technology companies. Only by developing AI competencies can we hold AI creators accountable. Last year's "Generative AI: Promises and Perils" panel moderated by Anil Ananthaswamy at the Heidelberg Laureate Forum Foundation's Forum brought together computer science experts to explore critical questions about the capabilities and implications of generative AI. The panel highlighted crucial questioning, like assessing the reasoning abilities of language models. It serves as an essential model for the kind of multidisciplinary examination of AI that journalism must undertake. #ArtificialIntelligence #AI #MediaLiteracy #AIAccountability #AIEthics #TechJournalism #EmergingTech #ResponsibleAI #FutureOfMedia #HLF2023

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