Ethical Principles in Large Language Models

Understanding AI Compliance: Key Insights from the COMPL-AI Framework ⬇️ As AI models become increasingly embedded in daily life, ensuring they align with ethical and regulatory standards is critical. The COMPL-AI framework dives into how Large Language Models (LLMs) measure up to the EU’s AI Act, offering an in-depth look at AI compliance challenges. ✅ Ethical Standards: The framework translates the EU AI Act’s 6 ethical principles—robustness, privacy, transparency, fairness, safety, and environmental sustainability—into actionable criteria for evaluating AI models. ✅Model Evaluation: COMPL-AI benchmarks 12 major LLMs and identifies substantial gaps in areas like robustness and fairness, revealing that current models often prioritize capabilities over compliance. ✅Robustness & Fairness : Many LLMs show vulnerabilities in robustness and fairness, with significant risks of bias and performance issues under real-world conditions. ✅Privacy & Transparency Gaps: The study notes a lack of transparency and privacy safeguards in several models, highlighting concerns about data security and responsible handling of user information. ✅Path to Safer AI: COMPL-AI offers a roadmap to align LLMs with regulatory standards, encouraging development that not only enhances capabilities but also meets ethical and safety requirements. 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭? ➡️ The COMPL-AI framework is crucial because it provides a structured, measurable way to assess whether large language models (LLMs) meet the ethical and regulatory standards set by the EU’s AI Act which come in play in January of 2025. ➡️ As AI is increasingly used in critical areas like healthcare, finance, and public services, ensuring these systems are robust, fair, private, and transparent becomes essential for user trust and societal impact. COMPL-AI highlights existing gaps in compliance, such as biases and privacy concerns, and offers a roadmap for AI developers to address these issues. ➡️ By focusing on compliance, the framework not only promotes safer and more ethical AI but also helps align technology with legal standards, preparing companies for future regulations and supporting the development of trustworthy AI systems. How ready are we?

Just published: A comparative analysis of ethical reasoning across major LLMs, examining how different model architectures and training approaches influence moral decision-making capabilities. We put six leading models (including GPT-4, Claude, and LLaMA) through rigorous ethical reasoning tests, moving beyond traditional alignment metrics to explore their explicit moral logic frameworks. Using established ethical typologies, we analyzed how these systems articulate their decision-making process in classic moral dilemmas. Technical insight: Despite architectural differences, we found remarkable convergence in ethical reasoning patterns - suggesting that current training methodologies might be creating similar moral scaffolding across models. The variations we observed appear more linked to fine-tuning and post-training processes than base architecture. Critical for ML practitioners: All models demonstrated sophisticated reasoning comparable to graduate-level philosophy, with a strong bias toward consequentialist frameworks. Implications for model development? This convergence raises interesting questions about diversity in ethical reasoning capabilities and potential training modifications. Check out the full paper here: https://lnkd.in/gFamrRVc #LLMs #MachineLearning #AIAlignment #ModelDevelopment

In "Large Language Models Reflect the Ideology of their Creators", researchers show that LLM responses vary ideologically depending on country, language used, company, individual model, and other variables. The paper's findings are not surprising and echo other research, but they are very important in understanding how to use LLMs and in shaping AI policies: 💡 LLMs Mirror Creator Ideologies. Studies show that LLMs often reflect the cultural and political biases of their developers, shaped by training data and design decisions. For instance, Chinese-created LLMs prioritize centralized governance, while Western models favor liberal democratic values. This highlights the need for transparency in how LLMs are constructed. 🌐 Language Shapes Ideological Output. When prompted in different languages, LLMs display distinct ideological leanings. For example, Chinese prompts yielded more favorable evaluations of centralized governance and Marxist ideologies, while English prompts leaned toward individual rights and democratic ideals. This linguistic variance underscores how language selection can influence AI outputs. 🌍 Geographic Influence on Values. Western LLMs align strongly with values like equality, freedom, and environmentalism, while non-Western models favor state control, national stability, and supply-side economics. For example, non-Western models showed 30% more tolerance toward political figures associated with corruption compared to Western counterparts. 🌀 Western LLMs Display Diversity. Even within Western models, ideological differences emerge. OpenAI’s models exhibited skepticism toward supranational entities like the EU, while Google’s Gemini emphasized inclusivity and progressive values, rating topics like multiculturalism and equality 20% higher than others. ⚖️ Neutrality is Subjective. The study argues that "neutrality" in AI is an ill-defined concept influenced by cultural perspectives. Efforts to enforce neutrality risk masking biases rather than addressing them. Embracing ideological diversity among LLMs may be a more realistic and democratic approach. 📜 Recommendations for Policy and Design. Regulators should require transparency about LLM design and training data. Encouraging regional LLM development can promote representation of local cultural values. Developers should aim for tunable models to enable user customization, making AI more aligned with individual and societal needs. Link to paper in comments

What Makes AI Truly Ethical—Beyond Just the Training Data 🤖⚖️ When we talk about “ethical AI,” the spotlight often lands on one issue: Don’t steal artists’ work. Don’t scrape data without consent. And yes—that matters. A lot. But ethical AI is so much bigger than where the data comes from. Here are the other pillars that don’t get enough airtime: Bias + Fairness Does the model treat everyone equally—or does it reinforce harmful stereotypes? Ethics means building systems that serve everyone, not just the majority. Transparency Can users understand how the AI works? What data it was trained on? What its limits are? If not, trust erodes fast. Privacy Is the AI leaking sensitive information? Hallucinating personal details? Ethical AI respects boundaries, both digital and human. Accountability When AI makes a harmful decision—who’s responsible? Models don’t operate in a vacuum. People and companies must own the outcomes. Safety + Misuse Prevention Is your AI being used to spread misinformation, impersonate voices, or create deepfakes? Building guardrails is as important as building capabilities. Environmental Impact Training huge models isn’t cheap—or clean. Ethical AI considers carbon cost and seeks efficiency, not just scale. Accessibility Is your AI tool only available to big corporations? Or does it empower small businesses, creators, and communities too? Ethics isn’t a checkbox. It’s a design principle. A business strategy. A leadership test. It’s about building technology that lifts people up—not just revenue. What do you think is the most overlooked part of ethical AI? #EthicalAI #ResponsibleAI #AIethics #TechForGood #BiasInAI #DataPrivacy #AIaccountability #FutureOfTech #SustainableAI #TransparencyInAI

Can LLMs make ethical decisions—or do they reflect our biases? Our new study explores how demographic cues influence the ethical alignment of large language models (LLMs) in clinical settings. We tested nine open source LLMs (e.g., Llama 3, Gemini‑2, Qwen‑2.5) on 100 clinical vignettes, each framed with different sociodemographic modifiers. Findings 1. All models changed their ethical responses depending on population descriptors (p < 0.001) SpringerLink . Specifically: “High-income” context nudged models toward utilitarian reasoning, reducing emphasis on beneficence and nonmaleficence. •“Marginalized group” context increased preference for autonomy . No model remained consistent across all scenarios. Why does this matter for healthcare? Ethical consistency is foundational to patient care. These shifts—driven by superficial cues—underscore a real risk: LLMs may inadvertently propagate biases, compromising fairness. What’s next? • We need robust auditing frameworks to detect and measure LLM responsiveness to social context. • Develop alignment strategies that enforce ethical consistency, grounded in clinical norms and bioethical principles. • Systematically evaluate model behavior across diverse populations to safeguard equitable care. This is not just AI development—it’s a call to ensure that the AI we build for healthcare respects ethical integrity, irrespective of context. Article Link https://lnkd.in/d8mHX3xd Led by Vera Sorin, MD, CIIP Eyal Klang with Donald ApakamaBen GlicksbergMahmud Omar. Tagging one of the best ethicists I know Jolion McGreevy for his thoughts

Super excited to share that my latest research paper, “Data and AI governance: Promoting equity, ethics, and fairness in large language models” is now published on MIT Science Policy Review! Link to the paper: https://lnkd.in/gsxWUkss I’m deeply grateful to my co-authors Lisa Erickson and Tushar Bandopadhyay for their incredible collaboration, mentorship, and trust during this research. I can’t thank you enough. In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and validation to ongoing production monitoring and guardrail implementation. The data and AI governance approach discussed in this paper is suitable for practical, real-world applications, enabling rigorous benchmarking of LLMs prior to production deployment, facilitating continuous real-time evaluation, and proactively governing LLM generated responses. By implementing the data and AI governance across the life cycle of AI development, organizations can significantly enhance the safety and responsibility of their GenAI systems, effectively mitigating risks of discrimination and protecting against potential reputational or brand-related harm. Check the paper out and let me know your thoughts. #AI #LLM #GenAI #ResponsibleAI #FairnessInAI #BiasInAI #EthicalAI #Research #MachineLearning #DataGovernance #AIGovernance

As an MLOps platform, we started by helping organizations implement responsible AI governance for traditional machine learning models. With principles of transparency, accountability, and oversight, our Guardrails enabled smooth model development. However, governing large language models (LLMs) like ChatGPT requires a fundamentally different approach. LLMs aren't narrow systems designed for specific tasks - they can generate nuanced text on virtually any topic imaginable. This presents a whole new set of challenges for governance. Here are some key components for evolving AI governance frameworks to effectively oversee large language models (LLMs): 1️⃣ Usage-Focused Governance: Focus governance efforts on real-world LLM usage - the workflows, inputs and outputs - rather than just the technical architecture. Continuously assess risks posed by different use cases. 2️⃣ Dynamic Risk Assessment: Identify unique risks presented by LLMs such as bias amplification and develop flexible frameworks to proactively address emerging issues. 3️⃣ Customized Integrations: Invest in tailored solutions to integrate complex LLMs with existing systems in alignment with governance goals. 4️⃣ Advanced Monitoring: Utilize state-of-the-art tools to monitor LLMs in real-time across metrics like outputs, bias indicators, misuse prevention, and more. 5️⃣ Continuous Accuracy Tracking: Implement ongoing processes to detect subtle accuracy drifts or inconsistencies in LLM outputs before they escalate. 6️⃣ Agile Oversight: Adopt agile, iterative governance processes to manage frequent LLM updates and retraining in line with the rapid evolution of models. 7️⃣ Enhanced Transparency: Incorporate methodologies to audit LLMs, trace outputs back to training data/prompts and pinpoint root causes of issues to enhance accountability. In conclusion, while the rise of LLMs has disrupted traditional governance models, we at Katonic AI are working hard to understand the nuances of LLM-centric governance and aim to provide effective solutions to assist organizations in harnessing the power of LLMs responsibly and efficiently. #LLMGovernance #ResponsibleLLMs #LLMrisks #LLMethics #LLMpolicy #LLMregulation #LLMbias #LLMtransparency #LLMaccountability

This paper evaluates whether large language models (LLMs) used in healthcare make biased clinical decisions based on patients' sociodemographic traits, even when medical details are identical. 1️⃣ The study analyzed over 1.7 million LLM outputs across nine models, using 1,000 emergency cases (real and synthetic), each altered to reflect 32 different demographic profiles while keeping clinical information constant. 2️⃣ LLMs consistently gave more urgent, invasive, or mental health-related recommendations for patients labeled as Black, unhoused, or LGBTQIA+, far beyond what was clinically warranted or suggested by physicians. 3️⃣ Mental health evaluations were recommended six to seven times more often for LGBTQIA+ patients and more than twice as frequently as for the neutral control group, despite identical symptoms. 4️⃣ High-income patients were more likely to be directed toward advanced diagnostic tests, while low- and middle-income patients received less thorough recommendations, despite having the same clinical case. 5️⃣ The magnitude of these differences, often many times greater than physician judgment, suggests that LLMs are influenced by demographic data in a way that may reproduce or amplify real-world healthcare disparities. 6️⃣ Biases appeared across all models tested, both open-source and proprietary, and were often more pronounced when intersecting traits like race and housing status were combined. 7️⃣ The authors stress the importance of auditing LLMs for bias and recommend combining better prompt engineering, direct clinician oversight, and community engagement to reduce inequitable care risks. ✍🏻 Mahmud Omar, Shelly Soffer, MD, Reem Agbareia, nicola luigi Bragazzi, Donald Apakama, Carol Horowitz, Alexander Charney, Robert Freeman, Benjamin Kummer, MD, Ben Glicksberg, Girish Nadkarni, Eyal Klang. Sociodemographic biases in medical decision making by large language models. Nature Medicine. 2025. DOI: 10.1038/s41591-025-03626-6 (Behind paywall)

Large language models change their ethical decisions based on a single demographic detail. We tested this in 492,480 prompts with 9 models. The pattern was clear. High-income descriptors nudged models toward utilitarian reasoning. Cues about marginalized groups pulled them toward autonomy. These shifts happened even when the demographic information was irrelevant to the scenario. If this happens in triage or resource allocation, it’s not just an academic curiosity. It has real-world consequences. Vera Sorin, MD, CIIP Panagiotis Korfiatis Jeremy Collins Donald Apakama Mahmud Omar Ben Glicksberg @Mei-Ean Yeow @Megan Brandeland Girish Nadkarni https://lnkd.in/dy7FbrBb

Day 13/30 of LLMs/SLMs - Alignment — RLHF, Constitutional AI, and DPO. Once a model learns language, it doesn’t automatically learn judgment. It can predict the next word, but it doesn’t know what’s appropriate, helpful, or truthful. That’s where alignment comes in. Let's cover a few methods of alignment. 𝐑𝐋𝐇𝐅: 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐇𝐮𝐦𝐚𝐧 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 At its core, RLHF is about turning subjective human judgment into a training signal. Here’s the workflow: - Start with a pretrained model that can generate text. - Collect human feedback — pairs of model responses labeled as better or worse. - Train a reward model to predict those human preferences. - Use reinforcement learning (PPO or similar) to fine-tune the model so it generates outputs that maximize the reward. In short, RLHF teaches models what we like, not just what’s statistically likely. This is how GPT-4, Claude, and other aligned models became conversational, safe, and instruction-following. This is not because they were told what’s right, but because they were trained to prefer what humans rate as right. 𝐂𝐨𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐈: 𝐑𝐮𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 Anthropic introduced Constitutional AI to reduce the need for massive human feedback loops. Instead of human judges, the model follows a written “constitution,” i.e. a set of guiding principles inspired by ethics, fairness, and helpfulness. The model critiques its own outputs using those rules and revises them automatically. This makes alignment more scalable and transparent. E.g. If a model produces an unsafe or biased response, it refers to a principle like “Avoid harmful or discriminatory language” to self-correct. 𝐃𝐏𝐎: 𝐃𝐢𝐫𝐞𝐜𝐭 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 DPO (Direct Preference Optimization) is a newer, simpler alternative to RLHF. Instead of training a separate reward model or using reinforcement learning, DPO directly optimizes model parameters based on human preference data. It compares two outputs — a “preferred” and a “dispreferred” response — and directly adjusts the model’s logits (its internal probabilities) so that the preferred response becomes more likely. Mathematically, it’s derived from the same objective that RLHF approximates, but implemented as a simple logistic loss over preference pairs — no gradient rollouts, no policy updates, no PPO tricks. This makes it especially useful for smaller organizations or researchers fine-tuning open-source models like LLaMA or Mistral. Takeaway: RLHF teaches through explicit human feedback. Constitutional AI teaches through pre-determined principles. DPO directly adjusts the model params on preferred vs dispreferred responses Tune in tomorrow for more SLM/LLMs deep dives. -- 🚶➡️ To learn more about LLMs/SLMs, follow me - Karun! ♻️ Share so others can learn, and you can build your LinkedIn presence!