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The Contradictions Defining AI’s Future

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Welcome to The AI Ethics Brief, a bi-weekly publication by the Montreal AI Ethics Institute. We publish every other Tuesday at 10 AM ET. Follow MAIEI on Bluesky and LinkedIn.

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  • One Question We’re Pondering: Are we witnessing the beginning of the end for large language models as we know them? We examine GPT-5’s mixed reception, NVIDIA and Georgia Tech researchers arguing for smaller specialized models, and University of Glasgow philosophers’ peer-reviewed argument that “ChatGPT is bullshit,” revealing fundamental questions about AI’s relationship with truth.

  • OpenAI Returns to Open Source: We analyze OpenAI’s first open-source models since GPT-2, examining the strategic contradiction of releasing capable models anyone can modify after years of justifying closed development for safety, with energy efficiency insights from Hugging Face researcher Sasha Luccioni.

  • YouTube’s AI Age Verification: We explore the platform’s new machine learning technology that predicts user age to block inappropriate content, examining the tension between child protection and privacy rights.

  • AI Copyright Battles Continue: Our AI Policy Corner with GRAIL at Purdue University examines the U.S. Copyright Office’s evolving guidance on works containing AI-generated material, including its January 2025 report and over 1,000 works containing some level of AI-generated material. Building on our coverage in Brief #168 on court rulings on fair use and Brief #169 on Cloudflare’s data governance shift, this development highlights how copyright has become the central arena where courts, infrastructure providers, and regulators are negotiating the future of cultural production in the age of generative AI.

  • Can Chatbots Replace Human Mental Health Support? Sabia Irfan examines the growing use of AI chatbots for mental health support, highlighting both accessibility benefits and concerns about emotional over-reliance and clinical oversight.

What connects these stories: The contradictions at the heart of AI development—between closed and open systems, revolutionary promises and incremental progress, protecting users while respecting privacy—revealing an industry grappling with its own stated values.

Brief #171 Banner Image Credit: Behaviour Power by Bart Fish & Power Tools of AI, featured in Better Images of AI, licensed under CC-BY 4.0.

The release of OpenAI’s GPT-5 on August 7 earlier this month was supposed to mark another triumphant milestone in our era of perpetual AI improvement. CEO Sam Altman hyped it as offering “PhD-level intelligence” and positioned it as a significant leap forward. Yet the reception has been notably mixed, with thousands of users on Reddit calling it “horrible” and “underwhelming.”

Critics have noted that it provides shorter, less nuanced responses while limiting user access to previously available models. Ironically, some of these complaints may stem from OpenAI’s efforts to reduce what they call “sycophancy,” the tendency for AI systems to be excessively agreeable, flattering, or validating rather than providing honest, balanced responses. While OpenAI claims this makes GPT-5 more truthful, many users are experiencing it as a loss of the warmth and personality they valued in earlier versions.

This lukewarm reception comes at a fascinating inflection point. While we chase ever-larger models, researchers at NVIDIA and Georgia Tech are making the case that we’re heading in the wrong direction entirely. Their preprint paper “Small Language Models are the Future of Agentic AI” argues that for most practical applications—especially the repetitive, specialized tasks that AI agents actually perform—smaller, more efficient models are not just adequate but superior.

Which brings us to a more fundamental question about the nature of these systems altogether. In their peer-reviewed paper “ChatGPT is bullshit” published in Ethics and Information Technology, philosophers Michael Townsen Hicks, James Humphries, and Joe Slater at the University of Glasgow build on Harry Frankfurt’s influential work On Bullshit to argue that large language models aren’t trying to convey truth at all, they’re designed to produce convincing-sounding text regardless of accuracy.

Unlike lying, which requires intent to deceive, or what the industry euphemistically calls “AI hallucinations,” this represents something more concerning: systematic indifference to truth itself. As the authors put it, these systems “cannot themselves be concerned with truth” and are fundamentally “indifferent to the truth of their outputs.” This matters because framing AI errors as mere “hallucinations” suggests the systems are trying but failing to perceive reality correctly, when in fact, they’re not designed to care about accuracy at all.

Perhaps the mixed reviews of GPT-5 aren’t a bug but a feature, a sign that we’re finally recognizing these tools for what they actually are: sophisticated text generators optimized for plausibility rather than truth, whose utility may be better served by smaller, specialized models rather than ever-larger general-purpose ones.

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OpenAI released GPT-OSS, their first open-source models since GPT-2 in 2019. The release includes multiple model sizes designed to be energy-efficient and capable of running on consumer hardware, available for download on Hugging Face. This marks a significant shift for a company that has moved increasingly toward closed, proprietary systems since GPT-3. The announcement of GPT-OSS on August 5 came in the same week as GPT-5’s launch on August 7.

📌 MAIEI’s Take and Why It Matters:

This release represents a notable contradiction in OpenAI’s strategy. For years, the company justified keeping powerful models closed by citing safety concerns, yet here they’re releasing capable models that anyone can download and modify.

The timing alongside GPT-5’s mixed reception suggests this reflects competitive pressures rather than strategic planning. As companies like Meta and Mistral push open-source alternatives, OpenAI appears to be recognizing that maintaining relevance in an increasingly open ecosystem may matter more than controlling access to powerful models.

More significantly, this aligns with growing arguments that smaller, specialized models may be the future. GPT-OSS represents OpenAI acknowledging that not every use case requires flagship models, potentially signalling a more nuanced approach where different model sizes serve different needs.

Beyond strategic considerations, the environmental implications are significant. As Hugging Face researcher Sasha Luccioni demonstrates in her analysis, GPT-OSS models consume substantially less energy while maintaining competitive performance. The cumulative impact of millions of users running models locally rather than through energy-intensive cloud infrastructure could be considerable.

If OpenAI is comfortable releasing these models openly, it raises questions about whether their previous arguments for closed development were primarily about safety or about maintaining competitive advantage. As the industry faces mounting pressures around energy consumption, shifting policy priorities, and evolving regulatory frameworks, GPT-OSS suggests that openness may become less of an ideological choice and more of a business necessity.

YouTube began testing machine learning age-verification technology on a subset of American users this past week. The technology will predict a user’s age according to several factors, including video preferences and the account creation date. If the user is inferred to be under the age of 18, they will be blocked access to content deemed inappropriate, receive well-being reminders, and not be sent targeted ads. In the event of incorrect age estimation, users may prove they are an adult by providing their credit card or a government ID. If the trial is successful, this technology, which has already been implemented in other markets, will expand throughout the United States.

📌 MAIEI’s Take and Why It Matters:

YouTube’s age verification measures are only one example of a global trend. For instance, earlier this summer, the United States Supreme Court ruled in favour of a Texas law requiring identification to view sexually explicit material with the aim of protecting children from viewing such content. Moreover, the European Commission is entering a pilot phase of testing online age verification measures that will be compatible with new European Union Digital Identity Wallets.

These measures are highly controversial. They raise digital privacy concerns in an era of increased government surveillance and potentially infringe on internet free speech. Data breaches are also a serious concern, such as in the UK, where many sites are utilizing third-party platforms to meet the age verification requirements of the Online Safety Act. YouTube and many third-party verification platforms pledge that data is tightly secured to protect against leaks. However, such measures are often inefficient. For instance, the privacy of 72,000 images on Tea (a controversial women-only app through which users share safety information and perspectives on potential male partners) was compromised, many of which were gender verification images that were supposed to be deleted quickly after verification.

On the other hand, such regulations are important to protect the well-being of children, preventing them from viewing sexually explicit, violent, and otherwise harmful content, in addition to guarding against predatory advertising methods. Platforms such as YouTube have a history of hosting material aimed at exploiting minors. A famous example of this phenomenon is the Elsagate scandal, through which platforms utilized “educational” themes and popular children’s characters from Disney and Nick Jr. to slip lewd and explicit content through the filtering mechanisms of YouTube Kids. For instance, a video with the stated purpose of facilitating the “learning and development of children!” depicts Paw Patrol characters in a strip club.

Age verification is a difficult issue as it puts two important values head-to-head: digital privacy and protecting children from harm. Promising advancements such as Zero Knowledge Proof (ZKP) technology adopted by Google and potentially EU Digital Identity Wallets may serve as future solutions, but it is very difficult to fully protect one’s privacy online. Moving forward, effective solutions must balance protecting user privacy with safeguarding children from harm.

Did we miss anything? Let us know in the comments below.

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This edition of our AI Policy Corner, produced in partnership with the Governance and Responsible AI Lab (GRAIL) at Purdue University, explores how the U.S. Copyright Office’s 2023 guidance on works containing AI-generated material is shaping the legal landscape. The piece focuses on the human authorship requirement, clarifying that only work reflecting a human’s original creative input is eligible for protection. It also outlines the threshold for “sufficient human authorship,” noting that prompt-based generation typically does not qualify. A follow-up report issued in January 2025 reaffirms that existing copyright law remains flexible enough to apply to generative AI, while emphasizing the need for discernible human contribution. Since the guidance was issued, the Office has registered over 1,000 works containing some level of AI-generated material, reflecting how its evolving interpretation is being tested in practice and shaping ongoing debates around authorship, ownership, and creative accountability.

To dive deeper, read the full article here.

In this op-ed, Sabia Irfan examines the growing use of AI chatbots for mental health support, highlighting a 2025 survey in which nearly half of Americans reported using large language models for psychological support, with 75 percent seeking help for anxiety and nearly 60 percent for depression. This marks a dramatic shift from a 2021 survey by Woebot, which found that only 22 percent of adults had used a mental health chatbot, though 47 percent expressed willingness to try one. While these tools offer accessible, non-judgmental support, Irfan raises concerns about emotional over-reliance, lack of clinical oversight, and the risks of unsafe responses, including a recent case involving Character.ai. The piece also highlights recent legislation in Illinois that restricts the use of AI in mental health services, highlighting the need for clearer boundaries, stronger safeguards, and professional involvement in the development of AI therapeutic tools.

To dive deeper, read the full article here.

Please help us keep The AI Ethics Brief free and accessible for everyone by becoming a paid subscriber on Substack or making a donation at montrealethics.ai/donate. Your support sustains our mission of democratizing AI ethics literacy and honours Abhishek Gupta’s legacy.

For corporate partnerships or larger contributions, please contact us at support@montrealethics.ai

Have an article, research paper, or news item we should feature? Leave us a comment below — we’d love to hear from you!



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Ethics & Policy

7 Life-Changing Books Recommended by Catriona Wallace | Books

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7 Life-Changing Books Recommended by Catriona Wallace (Picture Credit – Instagram)

Some books ignite something immediate. Others change you quietly, over time. For Dr Catriona Wallace—tech entrepreneur, AI ethics advocate, and one of Australia’s most influential business leaders, books are more than just ideas on paper. They are frameworks, provocations, and spiritual companions. Her reading list offers not just guidance for navigating leadership and technology, but for embracing identity, power, and inner purpose. These seven titles reflect a mind shaped by disruption, ethics, feminism, and wisdom. They are not trend-driven. They are transformational.

1. Lean In by Sheryl Sandberg

A landmark in feminist career literature, Lean In challenges women to pursue their ambitions while confronting the structural and cultural forces that hold them back. Sandberg uses her own journey at Facebook and Google to dissect gender inequality in leadership. The book is part memoir, part manifesto, and remains divisive for valid reasons. But Wallace cites it as essential for starting difficult conversations about workplace dynamics and ambition. It asks, simply: what would you do if you weren’t afraid?

Lean In
Lean In (Picture Credit – Instagram)

2. Women and Power: A Manifesto by Mary Beard

In this sharp, incisive book, classicist Mary Beard examines the historical exclusion of women from power and public voice. From Medusa to misogynistic memes, Beard exposes how narratives built around silence and suppression persist today. The writing is fiery, brief, and packed with centuries of insight. Wallace recommends it for its ability to distil complex ideas into cultural clarity. It’s a reminder that power is not just a seat at the table; it is a script we are still rewriting.

3. The World of Numbers by Adam Spencer

A celebration of mathematics as storytelling, this book blends fun facts, puzzles, and history to reveal how numbers shape everything from music to human behaviour. Spencer, a comedian and maths lover, makes the subject inviting rather than intimidating. Wallace credits this book with sparking new curiosity about logic, data, and systems thinking. It’s not just for mathematicians. It’s for anyone ready to appreciate the beauty of patterns and the thinking habits that come with them.

4. Small Giants by Bo Burlingham

This book is a love letter to companies that chose to be great instead of big. Burlingham profiles fourteen businesses that opted for soul, purpose, and community over rapid growth. For Wallace, who has founded multiple mission-driven companies, this book affirms that success is not about scale. It is about integrity. Each story is a blueprint for building something meaningful, resilient, and values-aligned. It is a must-read for anyone tired of hustle culture and hungry for depth.

5. The Misogynist Factory by Alison Phipps

A searing academic work on the production of misogyny in modern institutions. Phipps connects the dots between sexual violence, neoliberalism, and resistance movements in a way that is as rigorous as it is radical. Wallace recommends this book for its clear-eyed confrontation of how systemic inequality persists beneath performative gestures. It equips readers with language to understand how power moves, morphs, and resists change. This is not light reading. It is a necessary reading for anyone seeking to challenge structural harm.

6. Tribes by Seth Godin

Godin’s central idea is simple but powerful: people don’t follow brands, they follow leaders who connect with them emotionally and intellectually. This book blends marketing, leadership, and human psychology to show how movements begin. Wallace highlights ‘Tribes’ as essential reading for purpose-driven founders and changemakers. It reminds readers that real influence is built on trust and shared values. Whether you’re leading a company or a cause, it’s a call to speak boldly and build your own tribe.

7. The Tibetan Book of Living and Dying by Sogyal Rinpoche

Equal parts spiritual guide and philosophical reflection, this book weaves Tibetan Buddhist teachings with Western perspectives on mortality, grief, and rebirth. Wallace turns to it not only for personal growth but also for grounding ethical decision-making in a deeper sense of purpose. It’s a book that speaks to those navigating endings—personal, spiritual, or professional and offers a path toward clarity and compassion. It does not offer answers. It offers presence, which is often far more powerful.

The Tibetan Book of Living and Dying
The Tibetan Book of Living and Dying (Picture Credit – Instagram)

The books that shape us are often those that disrupt us first. Catriona Wallace’s list is not filled with comfort reads. It’s made of hard questions, structural truths, and radical shifts in thinking. From feminist manifestos to Buddhist reflections, from purpose-led business to systemic critique, this bookshelf is a mirror of her own leadership—decisive, curious, and grounded in values. If you’re building something bold or seeking language for change, there’s a good chance one of these books will meet you where you are and carry you further than you expected.





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Hyderabad: Dr. Pritam Singh Foundation hosts AI and ethics round table at Tech Mahindra

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The Dr. Pritam Singh Foundation and IILM University hosted a Round Table on “Human at Core: AI, Ethics, and the Future” in Hyderabad. Leaders and academics discussed leveraging AI for inclusive growth while maintaining ethics, inclusivity, and human-centric technology.

Published Date – 30 August 2025, 12:57 PM




Hyderabad: The Dr. Pritam Singh Foundation, in collaboration with IILM University, hosted a high-level Round Table Discussion on “Human at Core: AI, Ethics, and the Future” at Tech Mahindra, Cyberabad.

The event, held in memory of the late Dr. Pritam Singh, pioneering academic, visionary leader, and architect of transformative management education in India, brought together policymakers, business leaders, and academics to explore how India can harness artificial intelligence (AI) while safeguarding ethics, inclusivity, and human values.


In his keynote address, Padmanabhaiah Kantipudi, IAS (Retd.), Chairman of the Administrative Staff College of India (ASCI),

paid tribute to Dr. Pritam Singh, describing him as a nation-builder who bridged academia, business, and governance.
The Round Table theme, Leadership: AI, Ethics, and the Future, underscored India’s opportunity to leverage AI for inclusive growth across healthcare, agriculture, education, and fintech—while ensuring technology remains human-centric and trustworthy.



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AI ethics: Bridging the gap between public concern and global pursuit – Pennsylvania

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(The Center Square) – Those who grew up in the 20th and 21st centuries have spent their lives in an environment saturated with cautionary tales about technology and human error, projections of ancient flood myths onto modern scenarios in which the hubris of our species brings our downfall.

They feature a point of no return, dubbed the “singularity” by Manhattan Project physicist John von Neumann, who suggested that technology would advance to a stage after which life as we know it would become unrecognizable.

Some say with the advent of artificial intelligence, that moment has come. And with it, a massive gap between public perception and the goals of both government and private industry. While states court data center development and tech investments, polling from Pew Research indicates Americans outside the industry have strong misgivings about AI.

In Pennsylvania, giants like Amazon and Microsoft have pledged to spend billions building the high-powered infrastructure required to enable the technology. Fostering this progress is a rare point of agreement between the state’s Democratic and Republican leadership, even bringing Gov. Josh Shapiro to the same event – if not the same stage – as President Donald Trump.

Pittsburgh is rebranding itself as the “global capital of physical AI,” leveraging its blue-collar manufacturing reputation and its prestigious academic research institutions to depict the perfect marriage of code and machine. Three Mile Island is rebranding itself as Crane Clean Energy Center, coming back online exclusively to power Microsoft AI services. Some legislators are eager to turn the lights back on fossil fuel-burning plants and even build new ones to generate the energy required to feed both AI and the everyday consumers already on the grid.

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At the federal level, Trump has revoked guardrails established under the Biden administration with an executive order entitled “Removing Barriers to American Leadership in Artificial Intelligence.” In July, the White House released its “AI Action Plan.”

The document reads, “We need to build and maintain vast AI infrastructure and the energy to power it. To do that, we will continue to reject radical climate dogma and bureaucratic red tape, as the Administration has done since Inauguration Day. Simply put, we need to ‘Build, Baby, Build!’”

To borrow an analogy from Shapiro’s favorite sport, it’s a full-court press, and there’s hardly a day that goes by that messaging from the state doesn’t tout the thrilling promise of the new AI era. Next week, Shapiro will be returning to Pittsburgh along with a wide array of luminaries to attend the AI Horizons summit in Bakery Square, a hub for established and developing tech companies.

According to leaders like Trump and Shapiro, the stakes could not be higher. It isn’t just a race for technological prowess — it’s an existential fight against China for control of the future itself. AI sits at the heart of innovation in fields like biotechnology, which promise to eradicate disease, address climate collapse, and revolutionize agriculture. It also sits at the heart of defense, an industry that thrives in Pennsylvania.

Yet, one area of overlap in which both everyday citizens and AI experts agree is that they want to see more government control and regulation of the technology. Already seeing the impacts of political deepfakes, algorithmic bias, and rogue chatbots, AI has far outpaced legislation, often to disastrous effect.

In an interview with The Center Square, Penn researcher Dr. Michael Kearns said that he’s less worried about autonomous machines becoming all-powerful than the challenges already posed by AI.

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Kearns spends his time creating mathematical models and writing about how to embed ethical human principles into machine code. He believes that in some areas like chatbots, progress may have reached a point where improvements appear incremental for the average user. He cites the most recent ChatGPT update as evidence.

“I think the harms that are already being demonstrated are much more worrisome,” said Kearns. “Demographic bias, chatbots hurling racist invectives because they were trained on racist material, privacy leaks.”

Kearns says that a major barrier to getting effective regulatory policy is incentivizing experts to leave behind engaging work in the field as researchers and lucrative roles in tech in order to work on policy. Without people who understand how the algorithms operate, it’s difficult to create “auditable” regulations, meaning there are clear tests to pass.

Kearns pointed to ISO 420001. This is an international standard that focuses on process rather than outcome to guide developers in creating ethical AI. He also noted that the market itself is a strong guide. When someone gets hurt or hurts someone else using AI, it’s bad for business, incentivizing companies to do their due diligence.

He also noted crossroads where two ethical issues intersect. For instance, companies are entrusted with their users’ personal data. If policing misuse of the product requires an invasion of privacy, like accessing information stored on the cloud, there’s only so much that can be done.

OpenAI recently announced that it is scanning user conversations for concerning statements and escalating them to human teams, who may contact authorities when deemed appropriate. For some, the idea of alerting the police to someone suffering from mental illness is a dangerous breech. Still, it demonstrates the calculated risks AI companies have to make when faced with reports of suicide, psychosis, and violence arising out of conversations with chatbots.

Kearns says that even with the imperative for self-regulation on AI companies, he expects there to be more stumbling blocks before real improvement is seen in the absence of regulation. He cites watchdogs like the investigative journalists at ProPublica who demonstrated machine bias against Black people in programs used to inform criminal sentencing in 2016.

Kearns noted that the “headline risk” is not the same as enforceable regulation and mainly applies to well-established companies. For the most part, a company with a household name has an investment in maintaining a positive reputation. For others just getting started or flying under the radar, however, public pressure can’t replace law.

One area of AI concern that has been widely explored in the media is the use of AI by those who make and enforce the law. Kearns said, for his part, he’s found “three-letter agencies” to be “among the most conservative of AI adopters just because of the stakes involved.

In Pennsylvania, AI is used by the state police force.

In an email to The Center Square, PSP Communications Director Myles Snyder wrote, “The Pennsylvania State Police, like many law enforcement agencies, utilizes various technologies to enhance public safety and support our mission. Some of these tools incorporate AI-driven capabilities. The Pennsylvania State Police carefully evaluates these tools to ensure they align with legal, ethical, and operational considerations.”

PSP was unwilling to discuss the specifics of those technologies.

AI is also used by the U.S. military and other militaries around the world, including those of Israel, Ukraine, and Russia, who are demonstrating a fundamental shift in the way war is conducted through technology.

In Gaza, the Lavender AI system was used to identify and target individuals connected with Hamas, allowing human agents to approve strikes with acceptable numbers of civilian casualties, according to Israeli intelligence officials who spoke to The Guardian on the matter. Analysis of AI use in Ukraine calls for a nuanced understanding of the way the technology is being used and ways in which it should be regulated by international bodies governing warfare in the future.

Then, there are the more ephemeral concerns. Along with the long-looming “jobpocalypse,” many fear that offloading our day-to-day lives into the hands of AI may deplete our sense of meaning. Students using AI may fail to learn. Workers using AI may feel purposeless. Relationships with or grounded in AI may lead to disconnection.

Kearns acknowledged that there would be disruption in the classroom and workplace to navigate but it would also provide opportunities for people who previously may not have been able to gain entrance into challenging fields.

As for outsourcing joy, he asked “If somebody comes along with a robot that can play better tennis than you and you love playing tennis, are you going to stop playing tennis?”



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