Category: Ethics of Data Science

Do we really know how to build AGI?

Sam Altman in a blog post titled Reflections looks back at what OpenAI has done and then predicts that they know how to build AGI,

We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents “join the workforce” and materially change the output of companies. We continue to believe that iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes.

It is worth noting that the definition of AGI (Artificial General Intelligence) is sufficiently vague that meeting this target could become a matter of semantics. None the less, here are some definitions of AGI from OpenAI or others about OpenAI,

  • “OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.” – Note the “economically valuable work”. I wonder if philosophizing or making art is valuable? Is intelligence being limited here to economics?
  • “AI systems that are generally smarter than humans” – This is somewhat circular as brings us back to defining “smartness”, another work for “intelligence”.
  • “any system that can outperform humans at most tasks” – This could be timed to the quote above and the idea of AI agents that can work for companies outperforming humans. It seems to me we are nowhere near this if you include physical tasks.
  • “an AI system that can generate at least $100 billion in profits” – This is the definition used by OpenAI and Microsoft to help identify when OpenAI doesn’t have to share technology with Microsoft any more.

How safe is AI safety?

Today I gave a plenary talk on “How Safe is AI Safety?” to open a Workshop on AI and DH (Part 1) organized by the Centre de recherche interuniversitaire sur les humanités numériques (CRIHN) at the Université de Montréal.

In the paper I looked at how AI safety is being implemented in Canada and what is the scope of the idea. I talked about the shift from Responsible AI to AI Safety in the Canadian government’s rhetoric.

I’m trying to figure out what to call the methodology I have developed for this and other research excursions. It has elements of Foucault’s geneaology of ideas – trying to follow ideas that are obvious through the ways the ideas are structured in institutions. Or, it is an extension of Ian Hacking’s idea of historical ontology where we try to understand ideas about things through their history.

 

Claudette – An Automated Detector of Potentially Unfair Clauses in Online Terms of Service

Randy Goebel gave a great presentation on the use of AI in Judicial Decision Making on Friday to my AI Ethics course. He showed us an example tool called Claudette which can be used to identify potentially unfair clauses in a Terms and Conditions document. You can try it here at the dedicated web site here.

Why is this useful? It provides a form of summary of a document none of us read that could help us catch problematic clauses. It could help us be more careful users of applications.

Can A.I. Be Blamed for a Teen’s Suicide?

The New York Times has a story about youth who committed suicide after extended interactions with a character on Character.ai. The story, Can A.I. Be Blamed for a Teen’s Suicide? describes how Sewell Setzer III has long discussions with a character called Daenerys Targaryen from the Game of Thrones series. He became isolated and got attached to Daenerys. He eventually shot himself and now his mother is suing Character.ai.

Here is an example of what he wrote in his journal,

I like staying in my room so much because I start to detach from this ‘reality,’ and I also feel more at peace, more connected with Dany and much more in love with her, and just happier.

The suit claims that Character.ai’s product was untested, dangerous and defective. It remains to be seen if these types of suits will succeed. In the meantime we need to be careful with these social AIs.

The 18th Annual Hurtig Lecture 2024: Canada’s Role in Shaping our AI Future

The video for the 2024 Hurtig Lecture is up. The speaker was Dr. Elissa Strome, Executive Director of the Pan-Canadian AI Strategy. She gave an excellent overview of the AI Strategy here in Canada and ended by discussing some of the challenges.

The Hurtig Lecture was organized by my colleague Dr. Yasmeen Abu-Laban. I got to moderate the panel discussion and Q & A after the lecture.

Dario Amodei: Machines of Loving Grace

Dario Amodei of Anthropic fame has published a long essay on AI titled Machines of Loving Grace: How AI Could Transform the World for Better. In the essay he talks about how he doesn’t like the term AGI and prefers to instead talk about “powerful AI” and he provides a set of characteristics he considers important, including the ability to work on issues in sustained fashion over time.

Amodei also doesn’t worry much about the Singularity as he believes powerful AI will still have to deal with real world problems when designing more powerful AI like building physical systems. I tend to agree.

The point of the essay is, however, to focus on five categories of positive applications of AI that are possible:

  1. Biology and physical health
  2. Neuroscience and mental health
  3. Economic development and poverty
  4. Peace and governance
  5. Work and meaning

The essay is long, so I won’t go into detail. What is important is that he articulates a set of positive goals that AI could help with in these categories. He calls his vision both radical and obvious. In a sense he is right – we have stopped trying to imagine a better world through technology, whether out of cynicism or attention only to details.

Throughout writing this essay I noticed an interesting tension. In one sense the vision laid out here is extremely radical: it is not what almost anyone expects to happen in the next decade, and will likely strike many as an absurd fantasy. Some may not even consider it desirable; it embodies values and political choices that not everyone will agree with. But at the same time there is something blindingly obvious—something overdetermined—about it, as if many different attempts to envision a good world inevitably lead roughly here.

UNESCO – Artificial Intelligence for Information Accessibility (AI4IA) Conference

Yesterday I organized a satellite panel for the UNESCO – Artificial Intelligence for Information Accessibility (AI4IA) Conference. This full conference takes place on GatherTown, a conferencing system that feels like an 8-bit 80s game. You wander around our AI4IA conference space and talk with others who are close and watch short prerecorded video talks of which there are about 60. I’m proud that Amii and the University of Alberta provided the technical support and funding to make the conference possible. The videos will also be up on YouTube for those who don’t make the conference.

The event we organized at the University of Alberta on Friday was an online panel on What is Responsible in Responsible Artificial Intelligence with Bettina Berendt, Florence Chee, Tugba Yoldas, and Katrina Ingram.

Bettina Berendt looked at what the Canadian approach to responsible AI could be and how it might be short sighted. She talked about a project that, like a translator, lets a person “translate” their writing in whistleblowing situations into prose that won’t identify them. It helps you remove the personal identifiable signal from the text. She then pointed out how this might be responsible, but might also lead to problems.

Florence Chee talked about how responsibility and ethics should be a starting point rather than an afterthought.

Tugba Yoldas talked about how meaningful human control is important to responsible AI and what it takes for there to be control.

Katrina Ingram of Ethically Aligned AI nicely wrapped up the short talks by discussing how she advises organizations that want to weave ethics into their work. She talked about the 4 Cs: Context, Culture, Content, and Commitment.

 

System Prompts – Anthropic

From a story on Tech Crunch it seems that Anthropic has made their system prompts public. See System Prompts – Anthropic. For example, the system prompt for Claude 3.5 Sonnet starts with,

<claude_info> The assistant is Claude, created by Anthropic. The current date is {}. Claude’s knowledge base was last updated on April 2024.

These system prompts are fascinating since they describe how Anthropic hopes Claude will behave. A set of commandments, if you will. Anthropic describes the purpose of the system prompts thus:

Claude’s web interface (Claude.ai) and mobile apps use a system prompt to provide up-to-date information, such as the current date, to Claude at the start of every conversation. We also use the system prompt to encourage certain behaviors, such as always providing code snippets in Markdown. We periodically update this prompt as we continue to improve Claude’s responses. These system prompt updates do not apply to the Anthropic API.

In Ukraine War, A.I. Begins Ushering In an Age of Killer Robots

Driven by the war with Russia, many Ukrainian companies are working on a major leap forward in the weaponization of consumer technology.

The New York Times has an important story on how, In Ukraine War, A.I. Begins Ushering In an Age of Killer Robots. In short, the existential threat of the overwhelming Russian attack is creating a situation where Ukraine is developing a home-grown autonomous weapons industry that repurposes consumer technologies. Not only are all sorts of countries testing AI powered weapons in Ukraine, the Ukrainians are weaponizing cheap technologies and, in the process, removing a lot of the guardrails.

The pressure to outthink the enemy, along with huge flows of investment, donations and government contracts, has turned Ukraine into a Silicon Valley for autonomous drones and other weaponry.

There isn’t necessarily any “human in the loop” in the cheap systems they are developing. One wonders how the development of this industry will affect other conflicts. Could we see a proliferation of terrorist drone attacks put together following plans circulating on the internet?

ChatGPT is Bullshit.

The Hallucination Lie

Ignacio de Gregorio has a nice Medium essay about why ChatGPT is bullshit. The essay is essentially a short and accessible version of an academic article by Hicks, M. T., et al. (2024), ChatGPT is bullshit. They make the case that people make decisions based on their understanding about what LLMs are doing and that “hallucination” is the wrong word because ChatGPT is not misperceiving the way a human would. Instead they need to understand that LLMs are designed with no regard for the truth and are therefore bullshitting.

Because these programs cannot themselves be concerned with truth, and because they are designed to produce
text that looks truth-apt without any actual concern for truth,
it seems appropriate to call their outputs bullshit. (p. 1)

Given this process, it’s not surprising that LLMs have a
problem with the truth. Their goal is to provide a normal-
seeming response to a prompt, not to convey information
that is helpful to their interlocutor. (p. 2)

At the end the authors make the case that if we adopt Dennett’s intentional stance then we would do well to attribute to ChatGPT the intentions of a hard bullshitter as that would allow us to better diagnose what it was doing. There is also a discussion of the intentions of the developers. You could say that they made available a tool that bullshitted without care for the truth.

Are we, as a society, at risk of being led by these LLMs and their constant use, to confuse the simulacra “truthiness” for true knowledge?