Skip to main content

Protecting People, not profits

 


Protecting data. That's been the principle focus of tech regulation for decades. Protecting people hasn't. This should teach us a valuable lesson. Matt Clifford, an advisor to the UK Prime Minister, today stated in an interview: "We have got two years to get in place a framework that makes both controlling and regulating very large models much more possible than it is today." Earlier in the interview Clifford set out why this timescale is important, "if we don't start to think about safety then in about two years time we will be finding that we have systems that are very powerful indeed." But many have been thinking about safety in these systems, for a very long time. There are existing laws in the UK and most countries that could be enacted now, which are rarely enforced, around privacy of data as an example. 

Clifford though is misunderstanding the risks and the sector. The uncensored LLMs, that pose a national security risk, are in the Open Source arena, and can give one the formula for neurotoxins amongst many other dangerous chemical compounds. But so can books!  The challenge has been more about resources, about regulators having the people power, money and authority to intervene. The other challenge, a more significant one, is the concern of regulators to protect monopolistic concerns, to protect data, more than people, to enforce corporate rights rather than human rights.

Until this balance is reversed then there will be no effective protection that deals with AI. The other significant area of regulation has been around corporations, of course. But these fail constantly, largely as laws are there ultimately to aid larger corporations. Big Tech companies are constantly looking for ways to stay ahead of the competition. One of their most effective strategies is to invest in startups. Apple, for example, brought 50 different AI companies between 20/22.

Startups are often under the radar and largely unregulated. This gives Big Tech a chance to get in on the ground floor of new technologies and markets. Once a startup has developed a successful product or service, Big Tech can then acquire it or integrate it into their own business.

This strategy has allowed Big Tech to grow their market power and influence at an unprecedented rate. In recent years, Big Tech companies have come under increasing scrutiny for their monopolistic practices. However, they have so far been able to avoid significant regulation.

This is where you come in. As a consumer, you have the power to hold Big Tech accountable. You can choose to use products and services from companies that are committed to protecting your privacy and respecting your human rights. You can also support organisations that are fighting for stronger regulations on Big Tech. For those of you that are using ChatGPT without ever having read the terms of service, for example, which are pretty shocking, I'd urge you to read them, and seek to understand the implications of section 8 for instance.

Be highly skeptical about any company that requires a lot of your personal information, that wants to scan every eyeball in the world in order to enact it's own crypto currency, for example.

Rather than rushing to implement new forms of global governance around AI, insist on your legislators enact existing laws effectively first, to mitigate against the current dangers AI tools present now, which are many and varied. Then, when legislators consider AI regulation people have to come first, to be effective it should empower human rights.

Together, we can make sure that Big Tech doesn't use its power to exploit us. We can demand that they be held accountable for their actions and that they operate in a way that benefits all of us, not just their shareholders.

Comments

Popular posts from this blog

The Whispers in the Machine: Why Prompt Injection Remains a Persistent Threat to LLMs

 Large Language Models (LLMs) are rapidly transforming how we interact with technology, offering incredible potential for tasks ranging from content creation to complex analysis. However, as these powerful tools become more integrated into our lives, so too do the novel security challenges they present. Among these, prompt injection attacks stand out as a particularly persistent and evolving threat. These attacks, as one recent paper (Safety at Scale: A Comprehensive Survey of Large Model Safety https://arxiv.org/abs/2502.05206) highlights, involve subtly manipulating LLMs to deviate from their intended purpose, and the methods are becoming increasingly sophisticated. At its core, a prompt injection attack involves embedding a malicious instruction within an otherwise normal request, tricking the LLM into producing unintended – and potentially harmful – outputs. Think of it as slipping a secret, contradictory instruction into a seemingly harmless conversation. What makes prompt inj...

Podcast Soon Notice

I've been invited to make a podcast around the themes and ideas presented in this blog. More details will be announced soon. This is also your opportunity to be involved in the debate. If you have a response to any of the blog posts posted here, or consider an important issue in the debate around AGI is not being discussed, then please get in touch via the comments.  I look forward to hearing from you.

AI Agents and the Latest Silicon Valley Hype

In what appears to be yet another grandiose proclamation from the tech industry, Google has released a whitepaper extolling the virtues of what they're calling "Generative AI agents". (https://www.aibase.com/news/14498) Whilst the basic premise—distinguishing between AI models and agents—holds water, one must approach these sweeping claims with considerable caution. Let's begin with the fundamentals. Yes, AI models like Large Language Models do indeed process information and generate outputs. That much isn't controversial. However, the leap from these essentially sophisticated pattern-matching systems to autonomous "agents" requires rather more scrutiny than the tech evangelists would have us believe. The whitepaper's architectural approaches—with their rather grandiose names like "ReAct" and "Tree of Thought"—sound remarkably like repackaged versions of long-standing computer science concepts, dressed up in fashionable AI clot...