ChatGPT listed as author on research papers: many scientists disapprove

The editors-in-chief of Nature and Science told Nature’s news team that ChatGPT doesn’t meet the standard for authorship. “An attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs,” says Magdalena Skipper, editor-in-chief of Nature in London. Authors using LLMs in any way while developing a paper should document their use in the methods or acknowledgements sections, if appropriate, she says.

We are beginning to see interesting ethical issues crop up regarding the new LLMs (Large Language Models) like ChatGPT. For example, Nature has an news article, ChatGPT listed as author on research papers: many scientists disapprove,.

It makes sense to document use, but why would we document use of ChatGPT and not, for example, use of a library or of a research tool like Google Scholar? What about the use of ChatGPT demands that it be acknowledged?

Why scientists are building AI avatars of the dead | WIRED Middle East

Advances in AI and humanoid robotics have brought us to the threshold of a new kind of capability: creating lifelike digital renditions of the deceased.

Wired Magazine has a nice article about Why scientists are building AI avatars of the dead. The article talks about digital twin technology designed to create an avatar of a particular person that could serve as a family companion. You could have your grandfather modelled so that you could talk to him and hear his stories after he has passed.

The article also talks about the importance of the body and ideas about modelling personas with bodies. Imagine wearing motion trackers and other sensors so that your bodily presence could be modelled. Then imagine your digital twin being instantiated in a robot.

Needless to say we aren’t anywhere close yet. See this spoof video of the robot Sophia on a date with Will Smith. There are nonetheless issues about the legalities and ethics of creating bots based on people. What if one didn’t have permission from the original? Is it ethical to create a bot modelled on a historical person? a living person?

We routinely animate other people in novels, dialogue (of the dead), and in conversation. Is impersonating someone so wrong? Should people be able to control their name and likeness under all circumstances?

Then there are the possibilities for the manipulation of a digital twin or through such a twin.

As for the issue of data breaches, digital resurrection opens up a whole new can of worms. “You may share all of your feelings, your intimate details,” Hickok says. “So there’s the prospect of malicious intent—if I had access to your bot and was able to talk to you through it, I could change your attitude about things or nudge you toward certain actions, say things your loved one never would have said.”

 

How AI image generators work, like DALL-E, Lensa and stable diffusion

Use our simulator to learn how AI generates images from “noise.”

The Washington Post has a nice explainer on how text to image generators work: How AI image generators work, like DALL-E, Lensa and stable diffusion. They let you play with the generator, though you have to stick with the predefined phrases. What I hadn’t realized was the role of static noise in the diffusion model. Not sure how it works, but it seems to train the AI to recognize and then generate in noisy images.

From Bitcoin to Stablecoin: Crypto’s history is a house of cards

The wild beginnings, crazy turns, colorful characters and multiple comebacks of the crypto world

The Washington Post has a nice illustrated history of crypto, From Bitcoin to Stablecoin: Crypto’s history is a house of cards. They use a deck of cards as a visual metaphor and a graph of the ups and downs of crypto. I can’t help thinking that crypto is going to go up again, but when and in what form?

For that matter, where is Ruja Ignatova?

EU Artificial Intelligence Act

With the introduction of the Artificial Intelligence Act, the European Union aims to create a legal framework for AI to promote trust and excellence. The AI Act would establish a risk-based framework to regulate AI applications, products and services. The rule of thumb: the higher the risk, the stricter the rule. But the proposal also raises important questions about fundamental rights and whether to simply prohibit certain AI applications, such as social scoring and mass surveillance, as UNESCO has recently urged in the Recommendation on AI Ethics, endorsed by 193 countries. Because of the significance of the proposed EU Act and the CAIDP’s goal to protect fundamental rights, democratic institutions and the rule of law, we have created this informational page to provide easy access to EU institutional documents, the relevant work of CAIDP and others, and to chart the important milestones as the proposal moves forward. We welcome your suggestions for additions. Please email us.

The Center for AI and Digital Policy (CAIDP) has a good page on the EU Artificial Intelligence Act with links to different resources. I’m trying to understand this Act the network of documents related to it, as the AI Act could have a profound impact on how AI is regulated, so I’ve put together some starting points.

First, the point about the potential influence of the AI Act is made in a slide by Giuliano Borter, a CAIDP Fellow. The slide deck is a great starting point that covers key points to know.

Key Point #1 – EU Shapes Global Digital Policy

• Unlike OECD AI Principles, EU AI legislation will have legal force with consequences for businesses and consumers

• EU has enormous influence on global digital policy (e.g. GDPR)

• EU AI regulation could have similar impact

Borter goes on to point out that the Proposal is based on a “risk-based approach” where the higher the risk the more (strict) regulation. This approach is supposed to provide legal room for innovative businesses not working on risky projects while controlling problematic (riskier) uses. Borter’s slides suggest that an unresolved issue is mass surveillance. I can imagine that there is the danger that data collected or inferred by smaller (or less risky) services is aggregated into something with a different level of risk. There are also issues around biometrics (from face recognition on) and AI weapons that might not be covered.

The Act is at the moment only a proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) – the Proposal was launched in April of 2021 and all sorts of entities, including the CAIDP are suggesting amendments.

What was the reason for this AI Act? In the Reasons and Objective opening to the Proposal they write that “The proposal is based on EU values and fundamental rights and aims to give people and other users the confidence to embrace AI-based solutions, while encouraging businesses to develop them.” (p. 1) You can see the balancing of values, trust and business.

But I think it is really the economic/business side of the issue that is driving the Act. This can be seen in the Explanatory Statement at the end of the Report on artificial intelligence in a digital age (PDF) from the European Parliament Special Committee on Artificial Intelligence in a Digital Age (AIDA).

Within the global competition, the EU has already fallen behind. Significant parts of AI innovation and even more the commercialisation of AI technologies take place outside of Europe. We neither take the lead in development, research or investment in AI. If we do not set clear standards for the human-centred approach to AI that is based on our core European ethical standards and democratic values, they will be determined elsewhere. The consequences of falling further behind do not only threaten our economic prosperity but also lead to an application of AI that threatens our security, including surveillance, disinformation and social scoring. In fact, to be a global power means to be a leader in AI. (p. 61)

The AI Act may be seen as way to catch up. AIDA makes the supporting case that “Instead of focusing on threats, a human-centric approach to AI based on our values will use AI for its benefits and give us the competitive edge to frame AI regulation on the global stage.” (p. 61) The idea seems to be that a values based proposal that enables regulated responsible AI will not only avoid risky uses, but create the legal space to encourage low-risk innovation. In particular I sense that there is a linkage to the Green Deal – ie. that AI is being a promising technology that could help reduce energy use through smart systems.

Access Now also has a page on the AI Act. They have a nice clear set of amendments that show where some of the weaknesses in the AI Act could be.

Colorado artist used artificial intelligence program Midjourney to win first place

When Jason Allen submitted his “Théâtre D’opéra Spatial” into the Colorado State Fair’s fine arts competition last week, the sumptuous print was an immediate hit. It also marked a new milestone in the growth of artificial intelligence.

There has been a lot of comment about how a Colorado artist used artificial intelligence program Midjourney to win first place. This is seen as historic, but, as is pointed out in the Washington Post piece, people weren’t sure photography is an art. You could say that in both cases the art is in selection, not the image making that is taken over by a machine.

I can’t help thinking that an important part of art is the making. When I make art things they are amateurish and wouldn’t win any prizes, but I enjoy the making and improving at making. Having played with Midjourney it does have some of the pleasures of creating, but now the creation is through iteratively trying different combinations of words.

The New York Times has story about the win too, An A.I.-Generated Picture Won an Art Prize. Artists Aren’t Happy.

I Wrote a Book with GPT-3 AI in 24 Hours — And Got It Published

“How do we know that you haven’t written the poems you claim were authored by GPT-3?” the publisher asked in one of our calls.

That was what my life had become. Convincing a publisher that AI really had written the poems. No, I hadn’t done any editing. Yes, all the poems were generated in 24 hours and translated to Finnish with Google Translate — within those same 24 hours. Yes, I own the copyright, not OpenAI. Yes, they know and they said yes.

From Humanist I learned about the Medium post by Jukka Aalho I Wrote a Book with GPT-3 AI in 24 Hours. Aalho wrote a short book of poetry in 24 hours using GPT-3. I’ve bought the book and I’m not impressed by the poems. Perhaps they were better in Finnish. Here is one of the better ones (where the bold was the prompt),

why do we need to be happy, really
wouldn’t it be enough if we just don’t suffer

but everyone seems to be so very consumed by it
as if it’s the most important thing they can do
is try to get a grip on this
and I guess if that’s what they want
then I suppose I don’t have to be the one telling them
that they are mistaken
that they are making a big mistake
but how could they know that if no one tells them?
I don’t know

I’m just rambling

I’m rambling again

I’m sorry

What’s more impressive is that he got it published, but that may be because people like me would want to check it out.

Aalhu’s reflections on how such AIs might change creativity and editing are, however, quite interesting.

Zampolli Prize Awarded to Voyant Tools

Spyral Notebook Detail (showing code cell and stacked graphs)

Yesterday I gave the triennial Zampolli Prize lecture that honoured Voyant. The lecture is given at the annual ADHO Digital Humanities conference which this year is being hosted by the University of Tokyo. The award notice is here Zampolli Prize Awarded to Voyant Tools. Some of the things I touched on in the talk included:

  • The genius of of Stéfan Sinclair who passed in August 2020. Voyant was his vision from the time of his dissertation for which he develop HyperPo.
  • The global team of people involved in Voyant including many graduate research assistants at the U of Alberta. See the About page of Voyant.
  • How Voyant built on ideas Stéfan and I developed in Hermeneutica about collaborative research as opposed to the inherited solitary paradigm.
  • How we have now developed an extension to Voyant called Spyral. Spyral is a notebook programming environment built on JavaScript. It allows you to document your Voyant explorations, save parameters for corpora and tools, preprocess texts, postprocess results, and create new visualizations. It is, in short, a full data analysis and visualization environment built into Voyant so you can easily call up and explore results in Voyant’s already rich tool set.
  • In the image above you can see a Spyral code cell that outputs two stacked graphs where the same pattern of words is graphed over two different, but synchronized, corpora. You can thus compare the use of the pattern over time between the two datasets.
  • Replication as a practice for recovering an understanding of innovative technologies now taken for granted like tokenization or the KWIC. I talked about how Stéfan and I have been replicating important text processing technologies as a way of understanding the history of computing and the digital humanities. Spyral was the environment we developed for documenting our replications.
  • I then backed up and talked about the epistemological questions about knowledge and knowledge things in the digital age that grew out of and then inspired our experiments in replication. These go back to attempts to think-through tools as knowledge things that bear knowledge in ways that discourse doesn’t. In this context I talked about the DIKW pyramid (data, information, knowledge, wisdom) that captures current views about the relationships between data and knowledge.
  • Finally I called for help to maintain and extend Voyant/Spyral. I announced the creation of a consortium to bring us together to sustain Voyant.

It was an honour to be able to give the Zampolli lecture on behalf of all the people who have made Voyant such a useful tool.

Axon Pauses Plans for Taser Drone as Ethics Board Members Resign – The New York Times

After Axon announced plans for a Taser-equipped drone that it said could prevent mass shootings, nine members of the company’s ethics board stepped down.

Ethics boards can make a difference as a story from The New York Times shows, Axon Pauses Plans for Taser Drone as Ethics Board Members ResignThe problem is that board members had to resign.

The background is that Axon, after the school shootings, announced an early-stage concept for a TASER drone. The idea was to combine two emerging technologies, drones and non-lethal energy weapons. The proposal said they wanted a discussion and laws. “We cannot introduce anything like non-lethal drones into schools without rigorous debate and laws that govern their use.” The proposal went on to discuss CEO Rick Smith’s 3 Laws of Non-Lethal Robotics: A New Approach to Reduce Shootings. The 2021 video of Smith talking about his 3 laws spells out a scenario where a remote (police?) operator could guide a prepositioned drone in a school to incapacitate a threat. The 3 laws are:

  1. Non-lethal drones should be used to save lives, not take them.
  2. Humans must own use-of-force decisions and take moral and legal responsibility.
  3. Agencies must provide rigorous oversight and transparency to ensure acceptable use.

The ethics board, which had reviewed a limited internal proposal and rejected it, then resigned when Axon went ahead with the proposal and issued a statement on Twitter on June 2nd, 2022.

Rick Smith, CEO of Axon soon issued a statement pausing work on the idea. He described the early announcement as intended to start a conversation,

Our announcement was intended to initiate a conversation on this as a potential solution, and it did lead to considerable public discussion that has provided us with a deeper appreciation of the complex and important considerations relating to this matter. I acknowledge that our passion for finding new solutions to stop mass shootings led us to move quickly to share our ideas.

This resignation illustrates a number of points. First, we see Axon struggling with ethics in the face of opportunity. Second, we see an example of an ethics board working, even if it led to resignations. These deliberations are usually hidden. Third, we see differences on the issue of autonomous weapons. Axon wants to get social license for a close alternative to AI-driven drones. They are trying to find an acceptable window for their business. Finally, it is interesting how Smith echoes Asimov’s 3 Laws of Robotics as he tries to reassure us that good system design would mitigate the dangers of experimenting with weaponized drones in our schools.

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 debacleThis 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.