BDM ADS MEDIA

Marketing Manager

𝑫𝒐 𝒚𝒐𝒖 𝒌𝒏𝒐𝒘 𝒚𝒐𝒖 𝒄𝒂𝒏 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒆 𝒓𝒆𝒗𝒆𝒏𝒖𝒆 𝒕𝒉𝒓𝒐𝒖𝒈𝒉 𝑳𝒊𝒏𝒌𝒆𝒅𝒊𝒏 for your business but are you struggling with 𝑳𝒊𝒏𝒌𝒆𝒅𝒊𝒏 𝑺𝒕𝒓𝒂𝒕𝒆𝒈𝒚 𝒂𝒏𝒅 𝑴𝒂𝒓𝒌𝒆𝒕𝒊𝒏𝒈? Well, that's where I come in.



𝑰 𝒉𝒆𝒍𝒑 𝒚𝒐𝒖 𝒖𝒔𝒆 𝑳𝒊𝒏𝒌𝒆𝒅𝒊𝒏 𝒕𝒐:-

1. Generate organic leads and convert them.

2. Make your target market see you as an expert in your industry.

3. Make your target market know about your brand as well as increase your revenue.

Do you want to get heard by millions of your target market, then this platform will be a gold mine.




𝑫𝒎 𝒎𝒆 𝒐𝒏 𝑳𝒊𝒏𝒌𝒆𝒅𝒊𝒏.

Venkat Pagilla

Deel

Deel Lab is a research consortium for global work and labour compliance. In collaboration with the Conflict Analytics Lab at Queen’s University, Orrick LLP, Deel Lab brings experts and data together to contribute to today's discussion around the future of global employment policy and labour compliance.

Chair, Deel Lab for Global Employment 

Conflict Analytics Lab

The Conflict Analytics Lab is a consortium for AI research on law, compliance and conflict resolution.

Director and Founder 

Cornell University

Visiting Professor

Course: 
Negotiation, Arbitration and Mediation (Labour and regulatory)
Comparative Economic Governance: EU and US Responses to the Economic Crisis

Visiting Assistant Professor of Law

Queen's Law

Associate Professor & Queen's National Scholar

Cross-appointed to Smith School of Business and Ingenuity Lab

Assistant Professor & Queen's National Scholar

Court of Justice of the European Union (General Court)

- Legal counsel at the Cabinet the French Judge 
- Rapporteur (case handler) in several high-profile antitrust and labour law cases before the Court, including one that resulted in a €500 million fine.
- Trademarks 
- Eu Civil services Cases

Referendaire

Harvard Law School

Teaching Negotiation and Conflict resolution

Affiliate Faculty Member

New York University

Research collaboration on the expansion of the legal mandate of central banks (including the Fed, the ECB, the Bank of Canada and the Bank of Israel)

Emile Noël Fellow at NYU School of Law

Conseil d'Etat

•	Researched comparative EU economic and administrative regulation (competition, pharmaceutical, financial, employment and social policy) in reference to appellate complex cases
•	Advised on Court opinions concerning government legislative proposals
•	Specialised in common law and civil law systems (US, UK, Spain, Italy and transition countries)

Legal Advisor

ESSEC Business School

Major Projects:
•    European Commission. Analyst and Trainer for ESSEC team working to assess EU social, labour policy and environmental law-making process.
•    European External Action Service (EEAS). Drafted report and case study on the mediation role of the EEAS in groundwater conflict management. Developed and designed training for senior EU diplomats.
•    OECD (Organisation for Economic Cooperation and Development). One time consultation among Mediation Experts and National Contact Points (NCPs) involved in the application of OECD guidelines for multinational companies.
•    French Ministry of Foreign Affairs. Advised and trained French diplomats on international dispute resolutions mechanisms.

Research Associate

Ecole Nationale d'Administration (ENA)

Teaching Public Dispute Resolution to Top French Civil Servants

Maître de Conferences - Négociation et Gestion de Conflits

Chair @ Deel Lab | Building Legal AI l Driving Global Work Policy

Samuel Dahan

Sharing a fully funded AI Reseach / PhD position at Stephen J. R. Smith Faculty of Engineering and Applied Science at Queen's University × Conflict Analytics Lab (Queen's Law) supervised with my colleague prof. Mariam Guizani, PhD 

This project sits at the intersection of AI, law, and human systems. Instead of focusing on larger or more complex models, we study a different problem in legal AI: systems that confidently produce plausible but wrong answers, or that over-align with user prompts rather than legal reality.

Our approach is to let lawyers encode reasoning and experiential knowledge directly—things you won’t find in casebooks or datasets:

client intake and triage

settlement and negotiation patterns

lawyer–client interaction logic


We do this through a no-code human–AI collaboration framework, designed to make legal AI more reliable, transparent, and practice-aligned.

This PhD is not an NLP-heavy role. It’s about the human and organizational side of AI:

how open-source legal AI communities form,

how practitioners contribute knowledge, and

how these systems scale in courts, firms, and legal aid.


We’re looking for someone comfortable with tech, but especially strong in interpersonal, legal, business, or socio-technical skills, and interested in open-source community development in a real-world setting.

Start: September 2026
Original post from Smith Engineering below 👇

Frontier AI Research for Legal Reasoning | No-Code Legal AI Design | Law Professor

Stephen J. R. Smith Faculty of Engineering and Applied Science at Queen's University

View company: Stephen J. R. Smith Faculty of Engineering and Applied Science at Queen's University

Fully funded Master's, PhD, and Postdoc positions are now available. Specific research projects and a list of supervisors are here: https://lnkd.in/gDidh9jH 

Please share widely to help us find great candidates for these positions!

I’m writing a short op-ed on the recent Quebec case where a self-represented litigant was fined $5,000 for submitting AI-generated, hallucinated case law.

If you have an opinion, please reach out or comment — I’d really appreciate hearing different perspectives
.
What struck me is less the amount of the fine than what it says about where we are.
Are we punishing the symptoms instead of the problem?

Is it just me, or is something off here?
Yes — this wasn’t a small-stakes matter (the claim was around $2.7M, with sophisticated plaintiffs).

And yes — courts are exhausted by a flood of GenAI nonsense mostly coming from lawyers.

But I still can’t shake the feeling that this sets the wrong precedent.
Globally, 5 billion people lack meaningful access to justice.
https://lnkd.in/eFm9vMxC

In Canada, around 80% of legal problems go unmet (https://lnkd.in/evh-BGwE)

In the U.S., 50% of eligible legal-aid seekers are turned away because the system simply can’t cope. https://lnkd.in/eDPvufJk

Against that backdrop, fining a non-lawyer for using the only tools they can access feels… misguided.

Courts understandably want to deter unreliable AI submissions. Lawyers absolutely should be held to high standards. But when ordinary people — who already can’t get help — are punished for turning to tools that everybody says will empower them, it sends a strange message:
“We can’t assist you, and you also can’t use anything else.”

This won’t reduce the use of AI in court. It won’t help overloaded judges. And it certainly won’t improve access to justice.

If anything, it highlights a deeper truth: people are desperate for help, and the justice system isn’t meeting them where they are.

Curious what others think — is this a necessary deterrent, or are we punishing the symptom instead of the problem?


https://lnkd.in/eCy8BRqW
CTV News
#a2j

Frontier AI for Legal Reasoning | No-Code Legal AI Design | Law Professor

Sharing the benchmarking results on how #Gemini-3 performs on Canadian #employment-law reasoning, as part of the broader #HuggingLegal benchmarkathon led by Hugging Face.

Key takeaways from the benchmarkathon:

On standard contract- and evidence-law tasks (e.g., hearsay admissibility, NDA-clause interpretation), Gemini-3 delivered almost flawless performance — well above previous models.

On more complex statutory and case-law reasoning under the Wilful Misconduct benchmark (including Canadian employment-law dismissal scenarios), Gemini-3 produced structured, plausible analyses that show real potential for practical, high-stakes domains.

However, in fact-recall tasks like AA-Omniscience, the model still answers confidently when uncertain — a reminder that expert oversight remains essential.

 benchmarkathons are an important step toward rigorous, community-driven evaluation of legal AI — work that ultimately supports safer deployment and better access to justice.

Many thanks to Hugging Face for enabling this collaboration with the OpenJustice team (led by PHD candidate Chu-Fei Luo and Queen's Law's JD Sophie Franco at the Conflict Analytics Lab.
Joel Niklaus 
David J. Doorey 

here is full article 
https://lnkd.in/eQxhDwmJ

Canada has world-class AI researchers thanks to Vector Institute or Mila - Quebec Artificial Intelligence Institute
 
 What we still lack is top-tier compute at global scale — that we control.

To be clear: Canada does have serious supercomputing capacity. BC’s Fir system is a real step forward and now ranks globally.

But Canada remains the only G7 country without a supercomputer in the world’s top tier — the kind of infrastructure that anchors AI leadership, data sovereignty, and long-term innovation.

Why this matters:
 – much of Canada’s sensitive data and AI training still happens on foreign infrastructure
 – that creates governance, security, and dependency risks
 – sovereign compute is becoming as strategic as energy or telecom

Congratulations to Stephen J. R. Smith Faculty of Engineering and Applied Science at Queen's University @Ryan Grant for leading this initiative with Bell. Good to see this kind of work getting underway in Canada.
https://lnkd.in/g9DXtufd

It was a real privilege to partner with EPFL and the @legal design code law  community for this year’s #LauzHack.
Great to see teams using the #OpenJustice platform to prototype legal-AI tools that respond to real needs — from privacy compliance to client-facing legal processes.

Congratulations to all the participants, and to the students who built the Legal Compliance Assistant. Impressive work in just 24 hours.
Conflict Analytics Lab 
Queen's Law

Bringing Legal Reasoning to AI Models l Law Professor l former EU Official

At this weekend’s LauzHack, the Legal Design & Code Lab challenged teams to "hack for justice". In just 24 hours, 7 teams built innovative legal AI tools for the public good, with the goal of improving access to law and streamlining legal processes ⚖️🧑‍💻

Huge congrats to the Legal Compliance Assistant team for winning our LLM x Law challenge! 🏆🥁

🚀 What did they build? Developers often struggle to interpret complex privacy and security laws, leading to unintentional violations. This team built a VS Code Extension that performs a privacy and compliance audit on a codebase. It helps engineers build software that respects data protection and security standards while they code, translating legal requirements into practical recommendations.

Special thanks to ​​Reymond & Partners for sponsoring this prize and fueling innovation in the legal sector, and to the OpenJustice team for providing the technical platform and on-site expertise to help the teams succeed. 

LauzHack | Conflict Analytics Lab | Aurelia Tamo-Larrieux | Johannes David | Kevin Yu | Vlada Druta | Alice Palmieri | Oleg Lavrovsky | Samuel Dahan | JetBrains | FDCA UNIL - Faculté de droit, des sciences criminelles et d'administration publique | EPFL | Laia Mogas Pladevall | Biel Altimira Tarter | Pol Resina Martínez | Adrià Vico Sánchez

#LegalTech #AIforGood #DevTools #LauzHack #LegalDesign #AccessToJustice

I don’t usually comment on competition or antitrust law anymore (since my days at the CJEU), but this paper by Maxime Cohen caught my attention.

It doesn’t argue that competition law itself has already been reshaped. Rather, it shows how AI-driven pricing is reshaping pricing conduct — and amplifying risks in ways existing doctrine wasn’t built to detect.

We’ve moved well beyond a world where collusion is about cartel communications. Pricing is now dynamic, adaptive, and often delegated to learning systems. Coordination can emerge without any human agreement — yet most detection rules still assume that older paradigm.

Another tension: when prices change constantly and differ across users, it becomes genuinely hard to say what counts as a “spike,” “gouging,” or even parallel conduct.

The most interesting part, to me, is the GenAI angle. Generative AI dramatically lowers the barrier to pricing experimentation. Non-technical teams can now interact with pricing logic through prompts and scenarios, at scale. That doesn’t imply misconduct — but it amplifies risk by increasing speed, diffusion of responsibility, and opacity.

In other words, teams may drift into problematic outcomes without intent, simply because underlying models learn to align or react to one another. That’s a serious challenge for competition authorities.

The paper’s quieter implication is that antitrust may need to shift focus: less on outcomes alone, more on processes — data inputs, model behavior, prompts, and guardrails.

Curious how others see this.
Are competition rules adapting quickly enough to algorithmic and GenAI-driven pricing — or are we still applying 20th-century concepts to 21st-century systems?

Tagging Aymeric de Moncuit  and Daniel Francis, who think about this far more than I do.

https://lnkd.in/gK8htW3R

If you have kids who spend time watching other kids play with toys, unbox gadgets, or review games on YouTube or TikTok, the EU’s new kidfluencer proposal is worth paying attention to — and not only in Europe.

I’m sharing a recent CBC piece on this topic, which I discussed briefly with the author - James Dunne, MPPA, earlier this week. The article raises questions that go well beyond Europe, and I’d like to turn this into a broader community conversation — and eventually a advocacy/policy project. If you work in this area, or simply have thoughts as a parent or caregiver, I’d really welcome your ideas and comments.


The core idea is simple: when children become online performers, they deserve the same basic protections as child actors.
But the details are where things get complicated.

Banning kidfluencing won’t work — legally or technically.

Legally, it’s hard to define who the “influencer” even is when parents run the account and children appear constantly.

Technically, detecting harmful or exploitative content is still a cat-and-mouse game. Platforms have little incentive to invest in the kind of detection systems that would be required.


Still, there are elements in the EU proposal that are worth applauding:

Stronger age and identity verification for accounts monetizing children

Independent third-party oversight

Clearer obligations for platforms to assess risks to minors

And of course, what we often call the Brussels Effect matters:
when the EU moves, global standards tend to shift.
Canada doesn’t have an equivalent framework yet, but if big platforms adjust their systems for Europe, Canadian children may benefit indirectly.

It's an important conversation, and one that will only grow as more kids (and parents) turn online visibility into work.


https://lnkd.in/edvwh8XS

Should #Kidfluencers be banned (part 2)?

A quick follow-up to the discussion earlier this week on the EU’s #kidfluencer proposal — Global News also covered the issue today.

The growing interest shows how important (and complicated) this topic is for parents, policymakers, and platforms.

Thanks to Angela Kokott and shaye ganam for taking the time to look into this issue.

 https://lnkd.in/eaB_cEqA

Open article: Should 'kidfluencers' be banned? by open.spotify.com

Professor of Law @ Illinois Tech - Chicago Kent College of Law

🚨"What is an Agent? A Conceptual Primer and History of Agents and Agentic AI" -- Draft Book Chapter for forthcoming textbook on Artificial Intelligence for Law and Finance (2026 Forthcoming) ✅ 🚀

📕LINK HERE:  https://lnkd.in/gcYNemrR 

🤖 ABSTRACT: This chapter addresses the need for definitional clarity regarding "agents" and "agentic AI" amid widespread confusion in industry and academia. Drawing upon nearly a century of scholarship across eight disciplines—including philosophy, law, economics, cognitive science, and computer science—we offer a comprehensive framework for identifying and evaluating agentic systems.

Synthesizing work from Anscombe to recent advances in Agentic AI, we propose a three-level hierarchy of agency: Level 1 (Agent) is defined by the minimal properties of Goal, Perception, and Action (GPA); Level 2 (Agentic System) adds the operational properties of Iteration, Adaptation, and Termination (IAT), implemented through traditional algorithms; and Level 3 (Agentic AI) fulfills these six properties using AI or large language models (LLMs) for planning and orchestration. 

Beyond theoretical definitions, we provide a practical evaluation rubric to help practitioners distinguish genuine agentic systems from sophisticated tools or single-shot chatbots. The analysis explores critical dimensions, such as the autonomy spectrum and entity frames, to clarify the boundaries between delegated proxies and self-directed entities. Finally, the chapter outlines essential safeguards for professional deployment in high-stakes domains like law and finance, including attribution, provenance, escalation protocols, and confidentiality controls.

CC: Michael Bommarito Jillian Bommarito, CPA, CIPP/US/E

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Venkat Pagilla

Samuel Dahan

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Samuel Dahan's LinkedIn Performance

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