Category: Surveillance

The Ends of Safety

The AI future is not going to be won by hand-wringing about safety. (Vance)

At the Paris Summit, Vice President JD Vance gave a speech indicating a shift in US policy towards AI including a move away from “safety” as a common ethical ground. He also discussed how:

  • The US intends to dominate in AI, including chip manufacturing, though other countries can join them.
  • They want to focus on opportunity not guardrails.
  • They need reliable energy to power AI, which I take to mean non-green energy.
  • AI should be free of “ideology” by which he probably means “woke” language.

He apparently didn’t stay to hear the response from the Europeans. Nor did the USA or the UK sign the summit Statement on Inclusive and Sustainable Artificial Intelligence for People and the Planet that included language saying,

Harnessing the benefits of AI technologies to support our economies and societies depends on advancing Trust and Safety. We commend the role of the Bletchley Park AI Safety Summit and Seoul Summits that have been essential in progressing international cooperation on AI safety and we note the voluntary commitments launched there. We will keep addressing the risks of AI to information integrity and continue the work on AI transparency.

This marks a shift from the consensus that emerged from the Bletchley Declaration from 2023 which led to the formation of the UK AI Safety Institute and the US AI Safety Institute and the Canadian AI Safety Institute (etc.).

With Vance signalling the shift away from safety the UK has renamed its AISI the AI Security Institute (note how they substituted “security” for “safety”). The renamed unit is going to focus more on cybersecurity. It looks like the UK, unlike Europe, is going to try to stay ahead of a Trump ideological turn.

The US AI Safety Institute (AISI), which was set up with a small budget by NIST. is likely to also be affected (or have its acronym changed.)

Metaculus on AGI Outcomes

Listening to Jacob Steinhardt on The Hinton Lectures™ I learned about Metaculus, which is a forecasting service which is a public benefit company. It has a focus area on AI Progress with lots of AI related forecasts, (which seems to be a huge area of interest.) This service coordinates human forecasts and builds infrastructure to facilitate others in forecasting.

Neat!

UN launches recommendations for urgent action to curb harm from spread of mis and disinformation and hate speech Global Principles for Information Integrity address risks posed by advances in AI

United Nations, New York, 24 June 2024 – The world must respond to the harm caused by the spread of online hate and lies while robustly upholding human rights, United Nations Secretary- General António Guterres said today at the launch of the United Nations Global Principles for Information Integrity.

The UN has issued a press release announcing that the UN launches recommendations for urgent action to curb harm from spread of mis and disinformation and hate speech Global Principles for Information Integrity address risks posed by advances in AI. This press release marks the launch of the United Nations Global Principles for Information Integrity.

The recommendations in the press release include:

Tech companies should ensure safety and privacy by design in all products, alongside consistent application of policies and resources across countries and languages, with particular attention to the needs of those groups often targeted online. They should elevate crisis response and take measures to support information integrity around elections.

Tech companies should scope business models that do not rely on programmatic advertising and do not prioritize engagement above human rights, privacy, and safety, allowing users greater choice and control over their online experience and personal data.

Advertisers should demand transparency in digital advertising processes from the tech sector to help ensure that ad budgets do not inadvertently fund disinformation or hate or undermine human rights.

Tech companies and AI developers should ensure meaningful transparency and allow researchers and academics access to data while respecting user privacy, commission publicly available independent audits and co-develop industry accountability frameworks.

 

‘New York Times’ considers legal action against OpenAI as copyright tensions swirl : NPR

The news publisher and maker of ChatGPT have held tense negotiations over striking a licensing deal for the use of the paper’s articles to train the chatbot. Now, legal action is being considered.

Finally we are seeing a serious challenge to the way AI companies are exploiting written resources on the web as the New York Times engaged Open AI,  ‘New York Times’ considers legal action against OpenAI as copyright tensions swirl.

A top concern for the Times is that ChatGPT is, in a sense, becoming a direct competitor with the paper by creating text that answers questions based on the original reporting and writing of the paper’s staff.

It remains to be seen what the legalities are. Does using a text in order to train a model constitute the making of a copy in violation of copyright? Does the model contain something equivalent to a copy of the original? These issues are being explored in the AI image generating space where Stability AI is being sued by Getty Images. I hope the New York Times doesn’t just settle quietly before there is a public airing of the issues around the exploitation/ownership of written work. I also note that the Author’s Guild is starting to advocate on behalf of authors,

“It says it’s not fair to use our stuff in your AI without permission or payment,” said Mary Rasenberger, CEO of The Author’s Guild. The non-profit writers’ advocacy organization created the letter, and sent it out to the AI companies on Monday. “So please start compensating us and talking to us.”

This could also have repercussions in academia as many of us scrape the web and social media when studying contemporary issues. For that matter what do we think about the use of our work? One could say that our work, supported as it is by the public, should be fair game from gathering, training and innovative reuse. Aren’t we supported for the public good? Perhaps we should assert that academic prose is available for training models?

What are our ethics?

Worldcoin ignored initial order to stop iris scans in Kenya, records show

The Office of the Data Protection Commissioner in Kenya first instructed Worldcoin to stop collecting personal data in May.

I don’t know what to think about Worldcoin. Is it one more crypto project doomed to disappear or could it be a nasty exploitive project designed to corner identity by starting starting in Kenya. Imagine having to get orbed just to use local government services online! Fortunately Kenya is now ordering them to stop their exploitation; see the TechCrunch story, Worldcoin ignored initial order to stop iris scans in Kenya, records show.

The Illusion Of AI’s Existential Risk

In sum, AI acting on its own cannot induce human extinction in any of the ways that extinctions have happened in the past. Appeals to the competitive nature of evolution or previous instances of a more intelligent species causing the extinction of a less intelligent species reflect a common mischaracterization of evolution by natural selection.

Could artificial intelligence (AI) soon get to the point where it could enslave us? An Amii colleague sent me to this sensible article, The Illusion Of AI’s Existential Risk that argues that it is extremely unlikely that an AI could evolve to the point where it could manipulate us and prevent us from turning it off. One of the points they make is that the situation is completely different from past extinctions.

Our safety is the topic of Brian Christian’s excellent The Alignment Problem book which talks about different approaches to developing AIs so they are aligned with our values. An important point made by Stuart Russell and quoted in the book is that we don’t want AIs to have the same values as us, we want them to value our having values and to pay attention to our values.

This raises the question of how an AI might know what we value. One approach is Constitutional AI where we train ethical AIs on a constitution that captures our values and then use it to model others.

One of the problems, however, with ethics is that human ethics isn’t simple and may not be something one can capture in a constitution. For this reason another approach is Inverse Reinforcement Learning (IRL) where were ask an AI to infer our values from a mass of evidence of ethical discourse and behaviour.

My guess is that this is what they are trying at OpenAI in their Superalignment project. Imagine an ethical surveillance project that uses IRL to develop a (black) moral box which can be used to train AIs to be aligned. Imagine if it could be tuned to different community ethics?

Lessons from the Robodebt debacle

How to avoid algorithmic decision-making mistakes: lessons from the Robodebt debacle

The University of Queensland has a research alliance looking at Trust, Ethics and Governance and one of the teams has recently published an interesting summary of How to avoid algorithmic decision-making mistakes: lessons from the Robodebt debacle. This is based on an open paper Algorithmic decision-making and system destructiveness: A case of automatic debt recovery. The web summary article is a good discussion of the Australian 2016 robodebt scandal where an unsupervised algorithm issued nasty debt collection letters to a large number of welfare recipients without adequate testing, accountability, or oversight. It is a classic case of a simplistic and poorly tested algorithm being rushed into service and having dramatic consequences (470,000 incorrectly issued debt notices). There is, as the article points out, also a political angle.

UQ’s experts argue that the government decision-makers responsible for rolling out the program exhibited tunnel vision. They framed welfare non-compliance as a major societal problem and saw welfare recipients as suspects of intentional fraud. Balancing the budget by cracking down on the alleged fraud had been one of the ruling party’s central campaign promises.

As such, there was a strong focus on meeting financial targets with little concern over the main mission of the welfare agency and potentially detrimental effects on individual citizens. This tunnel vision resulted in politicians’ and Centrelink management’s inability or unwillingness to critically evaluate and foresee the program’s impact, despite warnings. And there were warnings.

What I find even more disturbing is a point they make about how the system shifted the responsibility for establishing the existence of the debt from the government agency to the individual. The system essentially made speculative determinations and then issued bills. It was up to the individual to figure out whether or not they had really been overpaid or there was a miscalculation. Imagine if the police used predictive algorithms to fine people for possible speeding infractions who then had to prove they were innocent or pay the fine.

One can see the attractiveness of such a “fine first then ask” approach. It reduces government costs by shifting the onerous task of establishing the facts to the citizen. There is a good chance that many who were incorrectly billed will pay anyway as they are intimidated and don’t have the resources to contest the fine.

It should be noted that this was not the case of an AI gone bad. It was, from what I have read, a fairly simple system.

A New Way to Inoculate People Against Misinformation

A new set of online games holds promise for helping identify and prevent harmful misinformation from going viral.

Instead of fighting misinformation after it’s already spread, some researchers have shifted their strategy: they’re trying to prevent it from going viral in the first place, an approach known as “prebunking.” Prebunking attempts to explain how people can resist persuasion by misinformation. Grounded in inoculation theory, the approach uses the analogy of biological immunization. Just as weakened exposure to a pathogen triggers antibody production, inoculation theory posits that pre-emptively exposing people to a weakened persuasive argument builds people’s resistance against future manipulation.

Prebunking is being touted as A New Way to Inoculate People Against Misinformation. The idea is that one can inoculate people against the manipulation of misinformation. This strikes me as similar to how we were taught to “read” advertising in order to inoculate us to corporate manipulation. Did it work?

The Cambridge Social Decision-Making Lab has developed some games like the Bad News Game to build psychological resistance to misinformation.

That viruses and inoculation can be metaphors for patterns of psychological influence is worrisome. It suggests a lack of agency or reflection among people. How are memes not like viruses?

The Lab has been collaborating with Google’s Jigsaw on Inoculation Science which has developed the games and videos to explain misinformation.

Pentagon believes its precognitive AI can predict events ‘days in advance’

The US military is testing AI that helps predict events days in advance, helping it make proactive decisions..

Endgadget has a story on how the Pentagon believes its precognitive AI can predict events ‘days in advance’. It is clear that for most the value in AI and surveillance is prediction and yet there are some fundamental contradictions. As Hume pointed out centuries ago, all prediction is based on extrapolation from past behaviour. We simply don’t know the future; the best we can do is select features of past behaviour that seemed to do a good job predicting (retrospectively) and hope they will work in the future. Alas, we get seduced by the effectiveness of retrospective work. As Smith and Cordes put it in The Phantom Pattern Problem:

How, in this modern era of big data and powerful computers, can experts be so foolish? Ironically, big data and powerful computers are part of the problem. We have all been bred to be fooled—to be attracted to shiny patterns and glittery correlations. (p. 11)

What if machine learning and big data were really best suited for suited for studying the past and not predicting the future? Would there be the hype? the investment?

When the next AI winter comes we in the humanities could pick up the pieces and use these techniques to try to explain the past, but I’m getting ahead of myself and predicting another winter.