Category: Ethics of Data Science

Anthropic co-founder remarks on “Magnifica humanitas”

Anthropic co-founder Chris Olah was one of the respondents on Pope Leo XIV’s encyclical Magnifica humanitas. (You can see my blog post here.) His remarks have been posted on the Anthropic site. Unlike some accelerationists who don’t want to listen to anyone outside the magic circle, he is humble and invites dialogue. Some of the points he makes:

  • He starts by recognizing how those who work in the AI industry operate “inside a set of incentives and constraints that can sometimes conflict with doing the right thing.”
  • Which is why he welcomes “Magnifica humanitas” and calls for dialogue.
  • He hopes that “if this technology is coming, it must go well—for our common home, and for the children to come.” Note that he doesn’t assume it is coming.
  • He describes it in almost human terms. “They are grown, on a structure roughly modeled after the brain, on an enormous inheritance of human thought and speech.” He says they are not the cold beings we expected but developing them is, “a little like bringing a fictional character to life.” Note that he doesn’t say it is like bringing a person to life, but making close as fiction. (Remember that “fiction” comes etymologically from “fashioning.”)

He names three questions where the Church’s discernment is needed. For that matter, he is clear that these discussion are not just for the engineers or Church, but for all.

  • The first is our duty to the global poor. 
  • The second is the need for moral imagination and ambition regarding human flourishing.
  • The third is the need for discernment on the nature of AI models. 

I would like to ask how a dialogue that goes beyond those in and with power could take place? Many just wait and hope they don’t lose their jobs or end up managed by a machine.

Ross Douthat has an opinion piece on Leo’s encyclical that is interesting. He says, “the core idea that we need to treat the A.I. future as a fundamentally political sphere, requiring democratic deliberation and political constraint, seems essential and correct.”

Pope Leo’s ‘Magnifica humanitas’: AI must serve humanity not concentrate power

Pope Leo the XIV just released an encyclical on artificial intelligence, Magnifica humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence. The encyclical was released on the 135th anniversary of the encyclical Rerum Novarum: On Capital and Human Labor by his namesake pope, Leo XIII.

The two are clearly meant to be connected. Magnifica humanitas, or “magnificent humanity” doesn’t paint AI as either good or bad, or for that matter, neutral. It is about humans making choices that safeguard (or not) our dignity (and magnificence.) I rather like the emphasis on our magnificence and the suggestion and AI could be one of our great deeds.

The encyclical identifies 5 principles that are not the usual principles for AI as they are principles of Social Doctrine with application to AI:

  • Common good
  • Universal destination of good (that not all goods be concentrated in the hands of the few)
  • Subsidiarity (that have shared responsibility, not paternalistic welfare)
  • Solidarity (that we care for others and future generations)
  • Social justice

The encyclical is long and deals with a number of issues including the need for dialogue. You can read a summary on the Vatican news web site here. The New York Times has an article on Main Takeaways From Pope Leo’s Encyclical on A.I. and one on how At the Epicenter of A.I., Pope Leo’s Warnings Are Dismissed. This last article captures the attitude of some that “no one outside of Silicon Valley understands AI and therefore we don’t need to listen to them.” No need for dialogue with others when you think you are magnificent.

Anthropic is standing up to the Department of War (DoW) on what we might call ethics issues. This story has some interesting angles. 

Originally Anthropic had a contract with the DoW to provide AI services across the government. They had two red lines:

  1. Their AIs couldn’t be used for fully autonomous lethal weapons.
  2. Their AIs couldn’t be used for mass surveillance of US citizens.

The government pushed back and eventually cancelled the contract. Then they designated Anthropic a Supply Chain Risk which could make it hard for any government agency to contract with them. So … they are suing now. Here are some interesting links on the story:

Both are short and worth reading.

Deepfakes and Epistemic Degeneration

Two deepfake images of the pileup of cars.

There are a number of deepfake images of the 100 car pileup on the highway between Calgary and Airdre on the 17th. You can see some here CMcalgary with discussion. These deepfakes raise a number of issues:

  • How would you know it is a deepfake? Do we really have to examine images like this closely to make sure they aren’t fake?
  • Given the proliferation of deepfake images and videos, does anyone believe photos any more? We are in a moment of epistemic transition from generally believing photographs and videos to no longer trusting anything. We have to develop new ways of determining the truth of photographic evidence presented to us. We need to check whether the photograph makes sense; question the authority of whoever shared it; check against other sources; and check authoritative news sources.
  • Liar’s dividend – given the proliferation of deepfakes, public figures can claim anything is fake news in order avoid accountability. In an environment where no one knows what is true, bullshit reigns and people don’t feel they have to believe anything. Instead of the pursuit of truth we all just follow what fits our preconceptions. A example of this is what happened in 2019 when the New Year’s message from President Ali Bongo was not believed as it looked fake leading to an attempted coup.
  • It’s all about attention. We love to look at disaster images so the way to get attention is to generate and share them, even if they are generated. On some platforms you are even rewarded for attention.
  • Trauma is entertaining. We love to look at the trauma of others. Again, generating images of an event like the pileup that we heard about, is a way to get the attention of those looking for images of the trauma.
  • Even when people suspect the images are fake they can provide a “where’s Waldo” sort of entertainment where we comb them for evidence of the fakery.
Image of pileup with containership across the highway.
Pileup with Container Ship
  • Deepfakes then generate more deepfakes and eventually people start responding with ironic deepfakes where a container ship is beached across the highway causing the pileup.
  • Evenutally there may be legal ramifications. On the one hand people may try to use fake images for insurance claims. Insurance companies may then refuse photographs as evidence for a claim. People may treat a fake image as a form of identity theft if it portrays them or identifiable information like a license plate.

 

The Next Generation Frontiers Symposium

The Next Generation Frontiers Symposium is in full swing in Banff! From sustainability to culture, yesterday’s sessions showcased the breadth of ideas shaping the future of AI. In a panel moderated by Hsiao-Ting Tseng, researchers Anfanny Chen, Shih-Fang Chen and Hsien-Tien Lin shared how AI can drive sustainable practices  — from smarter agriculture and resource management to greener supply chains and reduced carbon emissions. Later, Annie En-Shuin Lee, Dane Malenfant, Chi-Jui Hu, and Yun-Pu Tu led a fascinating discussion, moderated by Geoffrey Rockwell, on Indigenous AI and Culture, exploring the relationship between AI, cultural diversity and Indigenous knowledge. The day highlighted how meaningful interdisciplinary exchange can spark fresh perspectives and lead to new frontiers in research. (From here)

I’ve just come back from the Next Generation Frontiers Symposium which was organized by CIFAR, Taiwan’s National Science and Technology Council, (NSTC), and the Research Institute for Democracy, Society and Emerging Technology (DSET). This brought researchers from Taiwan and Canada to talk about Responsible AI, Sovereign AI, AI and Sustainability, and Indigenous AI and Culture. I moderated the Indigenous AI and Culture theme which looked at how AI might impact indigenous communities in both Taiwan and Canada. Some of the reflections include:

  • Indigenous community are often poorly represented in LLMs. We need ways for communities to be able to personalize models for their community with knowledge from their community.
  • The mass scraping of the Internet with little regard for ownership or consent of content creators is more of the extractive and colonizing behaviour that leads many indigenous communities to distrust settler nations.
  • There are knowledge practices and types of knowledge like gendered knowledge, age-specific knowledge, and location-based knowledge that simply cannot be datafied and modelled if they are to maintain their character.
  • Datafication and modelling work with measurable evidence. Anything that can’t be captured, sampled, and measured can’t then be datafied and thus can’t be modelled. Further, there is the danger that such evidence and knowledge will be deligitimized as unmeasurable and eventually excluded and fiction or mysticism. We could end up believing that only what we could datafy and model is knowledge.
  • Western espistemological practices of openness, science and replicable results should not be imposed on communities with different epistemological practices. AI is the product of Western epistemology and thus may never be compatible with indigenous wisdom.
  • We need to respect the desire of some communities to be forgotten and thus not scraped at all for measurable knowledge. Some may choose opacity.
  • Knowledge and its materials taken from communities should be returned. Communities should be supported to develop their own ways of preserving their knowledge including ways of datafying and modelling their knowledge, if they so wish.

Margaret Tu, one of the participants in the session, wrote a moving essay about the need for cultural safety for indigenous communities in the face of disaster in Taiwan. See Taiwan’s Barrier Lake Disaster Intersects With Its Troubled Indigenous Policy. It ends with this wisdom,

Disasters demand speed, but recovery demands reflection. For the Fata’an, healing will not come from relocation alone; it must be rooted in both land and culture.

How To Festival: How to think like an AI Ethicist

On Saturday I gave an online talk on “How to think like AI Ethicist” that was part of a How To Festival. I talked about thinking about responsibility and the issue of “responsibility gaps”. I talked about some key risks like hallucinations, bias, deep fakes, and companion AIs. I also mentioned that we need to celebrate the effective uses of AI and think not just about hazards, but also about AI for good.

Artificial intelligence (AI) is everywhere. We all need to assess what to use and how to use the new tools. In this talk Geoffrey Rockwell will discuss some of the safety issues raised by the new generative AI tools. He will suggest some ways you can think through AI.Geoffrey Rockwell is a Professor for Philosophy and Digital Humanities at the University of Alberta. He is also a Canada CIFAR AI Chair working on responsible AI.EPL’s annual How To Festival is a chance to learn something new from someone who already knows how to do it. A variety of experts from professionals to enthusiasts will share their skills with you.This is an online program. To receive a link and passcode to the online class, please register with your name and email address and instructions will be sent to you within 24 hours of the session.Zoom, a third-party app, will be used for this virtual session. By joining, you acknowledge that EPL does not take responsibility for Zoom’s privacy policies and practice.

Source: How To Festival: How to think like an AI Ethicist

Personal Superintelligence

Explore Meta’s vision of personal superintelligence, where AI empowers individuals to achieve their goals, create, connect, and lead fulfilling lives. Insights from Mark Zuckerberg on the future of AI and human empowerment.

Mark Zuckerberg has just posted his vision of superintelligence: Personal Superintelligence. He starts by reiterating what a lot of people are saying; namely that AGI (Artificial General Intelligence) or superintelligence is coming soon,

Over the last few months we have begun to see glimpses of our AI systems improving themselves. The improvement is slow for now, but undeniable. Developing superintelligence is now in sight.

He distinguishes what Meta is going to do with superintelligence from “others in the industry who believe superintelligence should be directed centrally towards automating all valuable work, …”. The “others” here is a poke at OpenAI who, in their Charter, define AGI as “highly autonomous systems that outperform humans at most economically valuable work …” He juxtaposes OpenAI as automating work (for companies and governments) while Meta will put superintelligence in our personal hands for creative and communicative play.

Along the way, Zuckerberg hints that future models may not be open any more, a change in policy. Until now Meta has released open models rather than charging for access. Zuckerberg not worries that “superintelligence will raise novel safety concerns.” For this reason they will need to “be rigorous about mitigating these risks and careful about what we choose to open source.”

Why don’t I trust either Meta or OpenAI company?

Feminist Data Manifest-No

Hannah L. Jacobs presented a great paper on “Critical Refusal, Slowness, and Openness: Possibilities and Challenges in Community-Oriented Digital Archival Initiatives” at DH 2025. She talked about refusing to complete a project once they realized they didn’t really have community approval to share their data. She also pointed to this Feminist Data Manifest-No.

There was a great question about whether one can mention in a grant that one wants to go slow and that the community may refuse to be studied. Our grant system rewards and supports innovation, not slow research. I’m reminded of The Slow Professor. Perhaps it is tenure that makes slowness possible, not grants.

Colloque « DH@LLM: Grands modèles de langage et humanités numériques » @ IEA & Sorbonne U

DH@LLM: Grands modèles de langage et humanités numériques Colloque organisé par Alexandre Gefen (CNRS-Sorbonne Nouvelle), Glenn Roe (Sorbonne Université), Ayla Rigouts Terryn (Université de Montréal) et Michael Sinatra (Université de Montréal) En collaboration avec l’Observatoire des textes, des idées et des corpus (ObTIC), le Centre de recherche interuniversitaire sur les humanités numériques (CRIHN), l’Institut d’Études […]

Today I gave a keynote to open this symposium on Large Language Models and the digital humanities, Colloque « DH@LLM: Grands modèles de langage et humanités numériques » @ IEA & Sorbonne U. I didn’t talk much about LLMs, instead I talked about “Care and Repair for Responsibility Practices in Artificial Intelligence”. I argued that the digital humanities has a role play in developing the responsibility practices that address the challenges of LLMs. I argued for an ethics of care approach that looks at the relationships between stakeholders (both individual and institutional) and asks how we can care for those more vulnerable and how can we repair emergent systems.

Brandolini’s law

In a 3QuarksDaily post about Bullshit and Cons: Alberto Brandolini and Mark Twain Issue a Warning About Trump I came across Brandolini’s law of Refutation which states:

The amount of energy needed to refute bullshit is an order of magnitude bigger than to produce it.

This law or principle goes a long way to explaining why bullshit, conspiracy theories, and disinformation are so hard to refute. The very act of refutation becomes suspect as if you are protesting too much. The refuter is made to look like the person with an agenda that we should be skeptical of.

The corollary is that it is less work to lie about someone before they have accused you of lying than to try to refute the accusation. Better to accuse the media of purveying fake news early than to wait until they publish news about you.

As for AI hallucinations, which I believe should be called AI bullshit, we can imagine Rockwell’s corollary:

The amount of energy needed to correct for AI hallucinations in a prompted essay is an order of magnitude bigger than the work of just writing it yourself.