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.
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 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 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:
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.
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.
It’s vital to “keep humans in the loop” to avoid humanizing machine-learning models in research
Today I was part of a panel organized by the Carnegie Council and the UNESCO Information for All Programme Working Group on AI for Information Accessibility: From the Grassroots to Policy Action. We discussed three issues starting with the issue of environmental sustainability and artificial intelligence, then moving to the issue of principles for AI, and finally policies and regulation. I am in awe of the other speakers who were excellent and introduced new ways of thinking about the issues.
Dariia Opryshko, for example, talked about the dangers of how Too Much Trust in AI Poses Unexpected Threats to the Scientific Process. We run the risk of limiting what we think is knowable to what can be researchers by AI. We also run the risk that we trust only research conducted by AI. Alternatively the misuse of AI could lead to science ceasing to be trusted. The Scientific American article linked to above is based on the research published in Nature on Artificial intelligence and illusions of understanding in scientific research.
I talked about the implications of the sorts of regulations we seen in AIDA (AI and Data Act) in C-27. AIDA takes a risk-management approach to regulating AI where they define a class of dangerous AIs called “high-risk” that will be treated differently. This allows the regulation to be “agile” in the sense that it can be adapted to emerging types of AIs. Right now we might be worried about LLMs and misinformation at scale, but five years from now it may be AIs that manage nuclear reactors. The issue with agility is that it will depend on there being government officers who stay on top of the technology or the government will end up relying on the very companies they are supposed to regulate to advise them. We thus need continuous training and experimentation in government for it to be able to regulate in an agile way.
As A.I.-generated data becomes harder to detect, it’s increasingly likely to be ingested by future A.I., leading to worse results.
The New York Times has a terrific article on model collapse, When A.I.’s Output Is a Threat to A.I. Itself. They illustrate what happens when an AI is repeatedly trained on its own output.
Model collapse is likely to become a problem for new generative AI systems trained on the internet which, in turn, is more and more a trash can full of AI generated misinformation. That companies like OpenAI don’t seem to respect the copyright and creativity of others makes is likely that there will be less and less free human data available. (This blog may end up the last source of fresh human text 🙂
The article also has an example of how output can converge and thus lose diversity as it trained on its own output over and over.
Perhaps the biggest takeaway of this research is that high-quality, diverse data is valuable and hard for computers to emulate.
One solution, then, is for A.I. companies to pay for this data instead of scooping it up from the internet, ensuring both human origin and high quality.
Counting frequently spoken words and phrases at both events.
The New York Times ran a neat story that used text analysis to visualize the differences between Words Used at the Democratic and Republican National Conventions. They used a number of different visualization including butterfly bar graphs like the one above. They also had a form of word bubbles that I thought was less successful.
This week I’m at DH 2024 at George Mason in Washington DC. I presented as part of two sessions.
On Wednesday I presented a short paper with Lauren Klein on work a group of us are doing on Visualization Ethics: A Case Study Approach. We met at a Dagstuhl on Visualization and the Humanities: Towards a Shared Research Agenda. We developed case studies for teaching visualization ethics and that’s what our short presentation was about. The link above is to a Google Drive with drafts of our cases.
Thursday morning I was part of a panel on Text Analysis Tools and Infrastructure in 2024 and Beyond. (The link, again, takes you to a web page where you can download the short papers we wrote for this “flipped” session.) This panel brought together a bunch of text analysis projects like WordCruncher and Lexos to talk about how we can maintain and evolve our infrastructure.
How to Write Poetry Using Copilot is a short guide on how to use Microsoft Copilot to write different genres of poetry. Try it out, it is rather interesting. Here are some of the reasons they give for asking Copilot to write poetry:
- Create a thoughtful surprise. Why not surprise a loved one with a meaningful poem that will make their day?
- Add poems to cards. If you’re creating a birthday, anniversary, or Valentine’s Day card from scratch, Copilot can help you write a unique poem for the occasion.
- Create eye-catching emails. If you’re trying to add humor to a company newsletter or a marketing email that your customers will read, you can have Copilot write a fun poem to spice up your emails.
- See poetry examples. If you’re looking for examples of different types of poetry, like sonnets or haikus, you can use Copilot to give you an example of one of these poems.
