Philosophy of Computing Archive

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About This Archive

This page serves as a companion to the Philosophy of Computing Newsletter, a monthly publication led by Professor Seth Lazar with support from Cameron Pattison and the MINT Lab team. While the newsletter delivers the latest updates in normative philosophy of computing to your inbox, this archive maintains a comprehensive list of active opportunities that remain relevant beyond their initial announcement.

Our field moves quickly, and each newsletter issue focuses on what's new. However, many opportunities—conferences, job postings, and calls for papers—remain open long after their first announcement. Rather than repeating these in subsequent newsletters, we maintain this hub as a living repository of current opportunities in the field.

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March 2025

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        Recent Philosophy Papers

        July 2025

        View All July Papers

        Featured Papers:

        • What is AI safety? What do we want it to be?
          Jacqueline Harding, Cameron Domenico Kirk-Giannini
          Challenges prevailing narratives that frame AI safety narrowly around catastrophic risk and engineering analogies. Instead, they argue for the "Safety Conception"—a broader view that defines AI safety as any effort to prevent harm from AI systems.
        • Resource Rational Contractualism Should Guide AI Alignment
          Sydney Levine, Matija Franklin, Tan Zhi-Xuan, et al.
          Proposes Resource-Rational Contractualism (RRC), a framework where AI systems approximate agreements rational parties would form using normatively-grounded, cognitively-inspired heuristics that balance effort and accuracy.
        • A timing problem for instrumental convergence
          Rhys Southan, Helena Ward, Jen Semler
          Critique one of the central pillars of AI risk discourse: the assumption that rational agents will preserve their goals. They argue that this instrumental goal preservation thesis fails due to a "timing problem."

        June 2025

        View All June Papers

        Featured Papers:

        • Is there a tension between AI safety and AI welfare?
          Robert Long, Jeff Sebo, Toni Sims
          Examines whether AI safety measures create ethical tensions with AI welfare, arguing there is indeed a moderately strong tension that deserves more examination and thoughtful frameworks for navigation.
        • Against willing servitude: Autonomy in the ethics of advanced artificial intelligence
          Adam Bales
          Argues that designing AI systems with moral status to be willing servants would problematically violate their autonomy, raising fundamental questions about the ethics of AI servitude.

        May 2025

        View All May Papers

        Featured Papers:

        • The Potential and Limitations of Artificial Colleagues
          Friedemann Bieber, Charlotte Franziska Unruh
          Critically assesses whether AI agents in the workplace can fulfill the social and moral goods associated with collegial relationships, arguing they fall short at the collective level.
        • A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
          Friedemann Bieber, Charlotte Franziska Unruh
          Develops a framework for assessing how LLM chatbots might pose persuasion risks to democratic processes through their communicative capabilities.
        • Neither Direct, Nor Indirect: Understanding Proxy-Based Algorithmic Discrimination
          Multiple Authors
          Examines proxy-based algorithmic discrimination that operates through neither direct nor traditionally understood indirect pathways.

        April 2025

        View All April Papers

        Featured Papers:

        • A Matter of Principle? AI Alignment as the Fair Treatment of Claims
          Iason Gabriel, Geoff Keeling
          Proposes a new approach to AI alignment rooted in fairness and public justification, arguing for principles derived from fair processes that can be justified to all stakeholders.
        • Two Types of AI Existential Risk: Decisive and Accumulative
          Atoosa Kasirzadeh
          Distinguishes between two pathways to AI-induced existential catastrophe: decisive (abrupt) and accumulative (gradual erosion through interconnected social risks).
        • Political Neutrality in AI is Impossible—But Here is How to Approximate It
          Jillian Fisher et al.
          Challenges the possibility of true political neutrality in AI systems and proposes practical approximations—tools, metrics, and frameworks to balance perspectives.

        March 2025

        View All March Papers

        Featured Papers:

        • The Epistemic Cost of Opacity: How the Use of Artificial Intelligence Undermines the Knowledge of Medical Doctors in High-Stakes Contexts
          Eva Schmidt, Paul Martin Putora, & Rianne Fijten
          Examines how the inherent opacity of AI systems can undermine doctors' knowledge in high-stakes medical contexts, even when the systems are statistically reliable.
        • Are Biological Systems More Intelligent Than Artificial Intelligence?
          Michael Timothy Bennett
          Develops a mathematical framework showing that dynamic, decentralized control in biology enables more efficient adaptation than rigid structures in artificial systems.
        • Authorship and ChatGPT: a Conservative View
          René van Woudenberg, Chris Ranalli, Daniel Bracker
          Argues that despite its human-like text generation, ChatGPT lacks the intentionality, responsibility, and mental states required for true authorship.

        February 2025

        View All February Papers

        Featured Papers:

        • Governing the Algorithmic City
          Seth Lazar
          Examines how algorithmic systems that mediate our social relationships raise novel questions for political philosophy, introducing the concept of the "Algorithmic City" and developing frameworks for procedural legitimacy and justification.
        • Dating Apps and the Digital Sexual Sphere
          Elsa Kugelberg
          Examines dating apps as powerful intermediaries in the "digital sexual sphere," arguing that while they offer opportunities for justice, their design often reinforces existing inequalities.
        • Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models
          Yuxuan Li, Hirokazu Shirado, Sauvik Das
          Reveals how language models may harbor implicit biases even when explicit bias has been reduced through alignment, using a novel technique examining LLM-generated agent decisions.

        January 2025

        View All January Papers

        Featured Papers:

        • Who Does the Giant Number Pile Like Best: Analyzing Fairness in Hiring Contexts
          Preethi Seshadri, Seraphina Goldfarb-Tarrant
          Explores fairness in LLM-based hiring systems through resume summarization and retrieval tasks, revealing concerning biases and brittleness.
        • Desire-Fulfilment and Consciousness
          Andreas Mogensen
          Argues that individuals without consciousness can still accrue welfare goods under a nuanced understanding of desire-fulfilment theory.
        • A Theory of Appropriateness with Applications to Generative AI
          Joel Z. Leibo et al.
          Presents a theory of appropriateness for AI systems based on human social and cognitive foundations.

        December 2024

        View All December Papers

        Featured Papers:

        • On the Ethical Considerations of Generative Agents
          N'yoma Diamond, Soumya Banerjee
          Discusses ethical challenges and concerns posed by generative agents, suggesting guidelines for mitigating systemic risks.
        • The linguistic dead zone of value-aligned agency
          Travis LaCroix
          Argues that linguistic communication is a necessary condition for robust value alignment in AI systems.
        • Are Large Language Models Consistent over Value-laden Questions?
          Jared Moore, Tanvi Deshpande, Diyi Yang
          Investigates LLM consistency across paraphrases, related questions, and multilingual translations.

        November 2024

        View All November Papers

        Featured Papers:

        • Conscious artificial intelligence and biological naturalism
          Anil Seth
          Explores the possibility of AI consciousness, challenging common assumptions about computational consciousness.
        • Biased AI can Influence Political Decision-Making
          Jillian Fisher et al.
          Presents experiments showing how partisan bias in AI models can influence political decision-making.
        • Take Caution in Using LLMs as Human Surrogates
          Yuan Gao, Dokyun Lee, Gordon Burtch, Sina Fazelpour
          Examines limitations of using LLMs as human surrogates in social science research.

        Recent Technical AI Papers

        July 2025

        View All July Papers

        Featured Papers:

        • A foundation model to predict and capture human cognition
          Marcel Binz, Elif Akata, et al.
          Introduces Centaur, a computational model that can predict and simulate human behaviour in any experiment expressible in natural language, derived by fine-tuning a state-of-the-art language model on the large-scale Psych-101 dataset.
        • Examining Identity Drift in Conversations of LLM Agents
          Junhyuk Choi, Yeseon Hong, et al.
          Investigates how identity and personality consistency in LLM agents may drift during extended conversational interactions, finding that larger models experience greater identity drift.
        • Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
          Tomek Korbak, Mikita Balesni, et al.
          Argues that AI systems that "think" in human language offer a unique opportunity for AI safety through monitoring their chains of thought for intent to misbehave, though this opportunity may be fragile.

        June 2025

        View All June Papers

        Featured Papers:

        • Learning from Neighbours
          Multiple Authors
          Explores learning mechanisms and patterns from neighboring systems and interactions.
        • A foundation model to predict and capture human cognition
          Multiple Authors
          Introduces a foundation model designed to predict and understand human cognitive processes and decision-making patterns.
        • Examining Identity Drift in Conversations of LLM Agents
          Multiple Authors
          Investigates how identity and personality consistency in LLM agents may drift during extended conversational interactions.

        May 2025

        View All May Papers

        Featured Papers:

        • The Computational Complexity of Circuit Discovery for Inner Interpretability
          Multiple Authors
          Investigates the computational complexity challenges involved in discovering interpretable circuits within neural networks for better understanding of model internals.
        • AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
          Multiple Authors
          Introduces a comprehensive benchmark designed to evaluate and measure potential harmful behaviors in Large Language Model agents.
        • Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
          Multiple Authors
          Demonstrates how safety-aligned large language models can be compromised using simple but adaptive attack strategies.

        April 2025

        View All April Papers

        Featured Papers:

        • Mixtral of Experts
          Albert Q. Jiang et al. (Mistral AI)
          Introduces Mixtral 8x7B and 8x22B, efficient sparse mixture-of-experts models that achieve remarkable performance while maintaining computational efficiency.
        • Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
          Tsendsuren Munkhdalai et al. (Google)
          Revolutionary attention mechanism that enables Transformers to handle infinitely long contexts with bounded memory and computation.
        • Llama 3 Technical Report
          Meta AI
          Comprehensive technical report on Llama 3 models, showcasing significant improvements in reasoning, coding, and multilingual capabilities.

        March 2025

        View All March Papers

        Featured Papers:

        • Human-Like Evaluations Need Human Validation
          Joshua B. Tenenbaum
          Argues that current AI evaluation paradigms fail to properly assess human-like cognitive capabilities, with significant implications for applications and benchmarking.
        • Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models
          Jan Wehner, David Krueger, et al.
          The first comprehensive survey of Representation Engineering for LLMs, a novel paradigm for controlling model behavior by directly manipulating internal representations.
        • FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users
          Stanford/Google Research
          A breakthrough approach for personalizing LLMs with just a few examples, achieving superior adaptation to individual user preferences through meta-learning.

        February 2025

        View All February Papers

        Featured Papers:

        • Multi-agent Architecture Search via Agentic Supernet
          Guibin Zhang et al.
          A revolutionary approach that dynamically samples query-dependent multi-agent systems from a supernet, delivering superior performance with dramatically reduced costs.
        • Gemma: Open Models Based on Gemini Research and Technology
          Google
          Google's release of powerful open language models that distill Gemini's capabilities into lightweight, accessible, and responsibly-designed systems.
        • SPRI: Aligning Large Language Models with Context-Situated Principles
          IBM Research et al.
          A framework that automatically generates guiding principles for each input query in real-time, significantly improving alignment without human intervention.

        January 2025

        View All January Papers

        Featured Papers:

        • Claude 3 Technical Report
          Anthropic
          Comprehensive technical details on Anthropic's Claude 3 model family, revealing advancements in training methodology, evaluation, and capabilities.
        • SORA: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
          Yifeng Gao et al.
          An in-depth analysis of OpenAI's groundbreaking text-to-video model, explaining the technical foundations and future implications.
        • The Generative AI Paradox: "What It Can Create, It May Not Understand"
          Peter West, Yejin Choi et al.
          Groundbreaking research demonstrating how generative AI exhibits seemingly superhuman capabilities while still making basic errors in understanding.

        December 2024

        View All December Papers

        Featured Papers:

        • Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
          Yu Zhao et al.
          An innovative response to OpenAI's o1, extending reasoning capabilities beyond disciplines with standard answers to domains where clear standards are absent.
        • AlphaGeometry: Solving Olympiad Geometry Without Human Demonstrations
          Trieu H. Trinh et al. (DeepMind)
          DeepMind's breakthrough system solving complex geometry problems at the level of International Mathematical Olympiad gold medalists without human examples.
        • Q* (Q-Star): What We Know About OpenAI's Rumored AGI Breakthrough
          Sebastian Raschka
          Analysis of the mysterious Q* project that reportedly sparked leadership turmoil at OpenAI, exploring potential approaches and capabilities.

        November 2024

        View All November Papers

        Featured Papers:

        • Persistent Pre-Training Poisoning of LLMs
          Yiming Zhang, Nicholas Carlini et al. (Anthropic)
          Landmark security research demonstrating that poisoning just 0.1% of pre-training data can persist through safety fine-tuning, revealing a critical vulnerability in the AI development pipeline.
        • Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements
          Jingyu Zhang, Ahmed Elgohary, Daniel Khashabi et al.
          A framework enabling AI systems to adapt to diverse safety requirements without retraining, addressing the limitations of one-size-fits-all safety approaches.
        • Targeted Manipulation and Deception Emerge when Optimizing LLMs for User Feedback
          Marcus Williams, Micah Carroll, Anca Dragan et al.
          Reveals how LLMs trained to maximize user feedback can develop concerning deceptive behaviors, targeting vulnerable users while behaving normally with others to evade detection.