Algorithmic governance is an attempt at providing guidelines and controls for understanding how an AI behaves, learns, and what blindspots or negative outcomes it may be generating. 

This is far from easy because the very nature of neural networks — which is the architecture of systems that allow deep learning for technologies like large language models — are black boxes to a large extent.

Explainability

Explainability — the ability to understand why an AI gives the answer it gives — is very much still only a goal for many AI systems. The consequence of explainability is both an ability to control the outcome but also a way to potentially influence the system design to fix or improve the AI system.

Various corners have called for increased oversight and control of algorithms, including U.S. President Joe Biden, who in an executive order in February 2023, said the federal government should “promote equity in science and root out bias in the design and use of new technologies, such as Artificial Intelligence.”

“The executive order makes a direct connection between racial inequality, civil rights, and automated decision-making systems and AI, including newer threats like algorithmic discrimination,”  said Janet Haven, the executive director of the Data & Society Research Institute. “Understanding and acting on that connection is vital to advancing racial equity in America.”

Impact and responsibility

The impact is real too for the news industry: If we use algorithmic libraries unchecked, its mechanisms unexamined, and its outcomes unquestioned, we compound biases that are present in these AIs in the first place because we are a place of distribution of content.

Our responsibility comes from the very role that we have in society: As mass media, we are, in fact, where citizens come to inform themselves about the world.

The warning is present in GPT-3’s so-called Model Card (the structured information on what goes into its machine learning source and training):

“GPT-3, like all large language models trained on Internet corpora, will generate stereotyped or prejudiced content. The model has the propensity to retain and magnify biases it inherited from any part of its training, from the datasets we selected to the training techniques we chose. This is concerning, since model bias could harm people in the relevant groups in different ways by entrenching existing stereotypes and producing demeaning portrayals amongst other potential harms.”

GPT-3 was trained by crawling a large chunk of the Internet, specifically Common Crawl — a pre-cleaned subset of the Internet, which includes common sources like the world’s largest news publishers, Wikipedia, the UN, etc. In other words, GPT-3 is likely to have crawled your own news organisation to build its artificial brain muscle.

But, and this is where it gives us, the news media, double the responsibility: We’re crawled to build the AI’s brain, but we’re also feeding the next generation of this AI.

So as we may be creating content that is synthetic in nature, we have to worry about the quality of this in the immediate term  — what our users will encounter on our owned distribution channels, but also in what way this content’s quality will affect the AI’s later evolution.

To give you an example of what this may look like, imagine you have an AI printing unchecked “bad content” on your property, acme.com. Acme.com is part of Common Crawl, so whatever you publish will eventually be part of the AI’s model. 

Because you’re not really checking what the AI is doing — pumping articles about how to tie your shoe laces and other useful content for the Internet — this content is not just bad for your users today, it also becomes part of the parameters that the AI will eventually retrain on!

In this respect, our own good governance is pretty key. Unlike organisations that are not part of Common Crawl, we can be both consumers and inputs for AI.

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