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Shaping the EU AI Act’s Code of Practice

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This weekend, the EU’s General-Purpose AI (GPAI) Code of Practice will be assessed by the AI Board and AI Office to see if it meets the requirements of the AI Act. A voluntary framework developed with the help of nearly 1,000 stakeholders, including Partnership on AI (PAI), model developers, AI safety experts, academics, representatives from EU Member States, and civil society organizations, it sets out measures that developers and providers of general purpose AI can use to demonstrate compliance with the EU’s AI Act and protect users of these systems from potential harms and risks. As AI evolves, it is important to us that we foster the development and deployment of systems that contribute to a more just, equitable, and prosperous world.

The GPAI covered by the Code includes powerful large language models like ChatGPT, Claude, and Llama, as well as other foundation models that can be adapted to a range of tasks. Compliance with the Code will require providers of all GPAI models to provide documentation about their models to the AI Office and to downstream developers, and will require providers of the most powerful GPAI to take steps to ensure their models are safe. This includes conducting evaluations, assessing and mitigating risks, reporting incidents, and ensuring adequate cybersecurity measures are in place. The Code has three sections, addressing Transparency, Copyright (both addressing all GPAI models), and Safety and Security (addressing GPAI models with systemic risk).

“As governments across the world work towards developing comprehensive AI governance strategies, it is important for frameworks like the Code of Practice to pave the way for clear guidance and fostering responsible innovation.”

Since the drafting process began last September, PAI has contributed significantly to the development of the Code, joining plenary sessions and contributing to all four working groups. We provided written feedback on multiple iterations through the drafting process. Our submissions addressed the Transparency and Safety and Security Sections of the Code, drawing on our published work on those topics. With most of our recommendations reflected in the finalized Code, we applaud the degree to which stakeholder feedback has been incorporated at each phase of the drafting process, improving the ability of the Code to promote safety, transparency, and compliance with the AI Act to better uphold and protect rights of EU citizens.

Transparency

The Code requires model developers to draw up model documentation and keep it up to date, and to provide relevant information to the AI Office, national AI regulators, and downstream providers. Regulators need this information to monitor compliance with the AI Act, and downstream providers need it to integrate GPAI models into their own systems and to comply with their own obligations.

PAI has undertaken extensive work on the importance of documentation for AI models and systems, including our ABOUT ML workstream, our Model Deployment Guidance, and our 2025 Progress Report on post-deployment documentation. We welcome the focus on documentation and the inclusion in the Code of a template Model Documentation Form.

There has been to date no consensus on either the form or content of documentation artifacts. Yet the benefits of documentation are greatest when it is comparable across models and systems, making it easier to judge relative performance, suitability, or impact of models.

Standardization of model documentation is also a key foundation for interoperability between legal and policy frameworks for foundation models, which we discuss at length in our report on the topic. The Model Documentation Form has the potential to promote policy interoperability, and PAI urges the Code’s drafters to consider promoting harmonization with evolving international best practices in future iterations of the Code.

The Code requires disclosure of information to EU regulators and downstream providers. It also encourages signatories to consider what information can be publicly disclosed. PAI would like to see additional guidance about what information about models should be publicly released in subsequent versions of the Code. Increased transparency will promote independent evaluations of models and ultimately increase the safety of, and public trust in, deployed AI models.

“. . . our recommendations reflected in the finalized Code . . . [improve] the ability of the Code to promote safety, transparency, and compliance with the AI Act to better uphold and protect rights of EU citizens.”

Safety and Security for GPAI models with systemic risks

PAI has undertaken significant work on foundation model safety. In particular, our Model Deployment Guidance contains detailed safety guidance for developers that is tailored to model capabilities and release strategy.

PAI is pleased to see that feedback on previous versions of the Code was taken on board by the drafters, and the Code now addresses a wider variety of systemic risks, including risks to fundamental rights, consistent with the AI Act.

PAI also welcomes the requirement for external evaluations of some GPAI models. Independent assessment of model capabilities and risks is crucial to ensure that evaluators have the broad range of expertise needed to do their job. It is also needed to build wider trust that evaluation outcomes are objective.

Independent evaluations are a critical plank of a vibrant AI assurance ecosystem. PAI launched a policy research project at the AI Action Summit in France earlier this year to address the core factors needed to build out an assurance ecosystem to create justified trust in AI models and systems. In future iterations of the Code, we would like to see more detailed guidance about external evaluations both pre- and post-deployment, including robust safe harbor provisions for evaluators.

As with the transparency section of the Code, consideration should be given in future iterations to expanding the guidance relating to the release of summaries of Safety and Security Frameworks and Model Reports, to include more detail about when those summaries should be released and their content.

We also welcome the inclusion in the code of provisions for post-market monitoring and incident reporting. Sharing relevant information about a model’s impact after deployment is crucial to understanding how to amplify societal benefits, manage and mitigate risks, develop evidence-based proportionate policy, and advance industry-wide norms.

Looking forward

While the Code offers a strong foundation, some areas could benefit from further development. This includes greater guidance about identifying systemic risks and more detail about external evaluations. We urge the Commission to keep these matters under review and commit to regular and ongoing updates to the Code to ensure it reflects evolving best practices.

Regular review of the Code will be necessary to accommodate rapidly evolving best practices, as well as increasing model capabilities and the emergence of novel risks. In future reviews of the Code, we hope to see a number of issues addressed:

  • Ongoing research: Research on evaluations, metrics, and benchmarks for capabilities and risks is still ongoing. Similarly, best practices for post-market monitoring are continuing to develop. As our understanding of GPAI and best practices progresses, it should be reflected by the inclusion of more detailed guidance in the Code.
  • Updates to the threshold for GPAI models with systemic risks: The current compute-based threshold is widely acknowledged to be an imprecise proxy for risk. Methods to identify which models require closer scrutiny are likely to evolve and the Code should be updated to reflect this. As well as being a core part of the risk management framework in the Code, thresholds are a foundational plank of policy interoperability across jurisdictions. Future iterations of the Code should seek to harmonize the threshold for models with systemic risk with thresholds for frontier models in other national and international frameworks as closely as possible, within the constraints of the definitions and in-scope risks set out in the AI Act.
  • Address in more detail the public disclosure of standardized model documentation and summaries of Safety and Security Frameworks and Model Reports.

The endorsement of the GPAI Code of Practice will mark a significant step forward in global AI governance. As governments across the world work towards developing comprehensive AI governance strategies, it is important for frameworks like the Code of Practice to pave the way for clear guidance and fostering responsible innovation. The Code will provide developers and model providers a structured approach to building systems that comply with the EU AI Act, ensuring that these systems are developed and deployed responsibly.

We are especially pleased to see the commitment to a multistakeholder process throughout the drafting process, ensuring that voices from collaborators across sectors are heard. While views differ about the precise terms of the final Code, the efforts made to respond to feedback at each phase of the drafting process have been impressive and have set a valuable precedent for collaborative AI governance. We are excited to see the Code welcomed into practice, and look forward to continuing our work in ensuring AI is developed and deployed responsibly for the benefit of all. To stay up to date with our work in this area, sign up for our newsletter.



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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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