We risk a deluge of AI-written'science' pushing corporate interests - here's what to do about it Back in the 2000s, the American pharmaceutical firm Wyeth was sued by thousands of women who had developed breast cancer after taking its hormone replacement drugs. Court filings revealed the role of "dozens of ghostwritten reviews and commentaries published in medical journals and supplements being used to promote unproven benefits and downplay harms" related to the drugs. Wyeth, which was taken over by Pfizer in 2009, had paid a medical communications firm to produce these articles, which were published under the bylines of leading doctors in the field (with their consent). Any medical professionals reading these articles and relying on them for prescription advice would have had no idea that Wyeth was behind them. The pharmaceutical company insisted that everything written was scientifically accurate and - shockingly - that paying ghostwriters for such services was common in the industry.

There's good reason to fear that A.I. systems like ChatGPT and GPT4 will harm democracy. Public debate may be overwhelmed by industrial quantities of autogenerated argument. People might fall down political rabbit holes, taken in by superficially convincing bullshit, or obsessed by folies à deux relationships with machine personalities that don't really exist. These risks may be the fallout of a world where businesses deploy poorly tested A.I. systems in a battle for market share, each hoping to establish a monopoly. A.I. could advance the public good, not private profit, and bolster democracy instead of undermining it.

The word'collect' may be first word that springs to mind here, but it's not an accurate word. Tech companies have carefully chosen their words to frame personal data as something that we possess and "allow" them to collect, whereas the truth is that we give them'information' and they turn that information into'data', a physical, digital commodity which is often referenced as the oil of the 21st Century. What this means is that data -- the stuff that powers AI -- is a corporate commodity, created and owned by corporations, driven by the corporate interests of capitalism as opposed to ethical interests of the public. That model made sense once, when data was only narrowly useful in select corporate environments and we didn't all have internet-connected supercomputers in our pockets, but today that model is not only stunting growth in the emerging AI industry, it's downright dangerous to democracy in ways exemplified by Facebook and Emerdata (Cambridge Analytica) in recent months. I want to first take a look at what "data" is and its two primary uses: transporting information and combining information.

Since 1956, when computer science researchers gathered in the small town of Hanover, N.H. at Dartmouth College to talk about the field's nascent investigations into artificial intelligence, both government and industry in the U.S. have grappled with how to structure a systematic approach to research and development in the newly important field. From the government's perspective, this is increasingly important. With both federal research institutions and private companies pursuing artificial intelligence breakthroughs at breakneck speed, the federal government is frankly having a bit of an existential crisis about its role in research efforts and the priorities it has for what AI research should look like. To wit, in 2015 government spending on unclassified research and development in AI-related technologies was around 1.1 billion, according to one of the twin reports released today. But in the last five years alone, mergers and acquisitions among private companies vying for dominance in the AI market have far outstripped that figure, according to data from CB Insights.