This is a regularly updated list of resources for getting up to speed on the main ideas in long-term AI safety. This is a selected list that is not intended to be comprehensive. If there are any resources that you think would be important to add to the list, please let me know via this form. See the Alignment newsletter for summaries of many resources on this list as well as more extensive references.
[Last major update on 20 June 2021 – thanks to Neel Nanda for feedback and suggestions.]
- Incentive design
- Learning human intent
- Other technical work
- Collections of technical works
For a popular audience
Kelsey Piper (2018). The case for taking AI seriously as a threat to humanity.
Robert Miles. AI Safety YouTube channel. Introductory videos on challenges in AI safety.
Open Philanthropy (2015). Potential risks from advanced artificial intelligence. An overview of AI risks and timelines, possible interventions, and current actors in this space.
For a technical audience
Richard Ngo (2020). AGI safety from first principles.
- Provably Beneficial AI (BAGI 2019). A video of Russell’s talk introducing the problem of AI alignment as we build towards provably beneficial AI.
- The long-term future of AI (longer version) (2015). A video of Russell’s talk discussing why it makes sense for AI researchers to think about AI safety, and going over various misconceptions about the issues.
- On Myths and Moonshine (2014). Russell’s response to the “Myth of AI” question on Edge.org, which draws an analogy between AI research and nuclear research, and points out some dangers of optimizing a misspecified utility function.
Scott Alexander (2015). No time like the present for AI safety work. An overview of long-term AI safety challenges, e.g. preventing wireheading and formalizing ethics.
Stuart Armstrong (2014). Smarter Than Us: The Rise Of Machine Intelligence. A short ebook discussing potential promises and challenges presented by advanced AI, and the interdisciplinary problems that need to be solved on the way there.
Steve Omohundro (2007). The basic AI drives. A classic paper arguing that sufficiently advanced AI systems are likely to develop drives such as self-preservation and resource acquisition independently of their assigned objectives.
Brundage, Avin, et al (2020). Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims.
Brundage, Avin, et al (2018). The Malicious Use of AI: Forecasting, Prevention and Mitigation.
Grace, Salvatier, Dafoe, Zhang, Evans (2017). When Will AI Exceed Human Performance? Evidence from AI Experts. A detailed survey of >300 authors at NeurIPS and ICML conferences, on when AI is likely to outperform humans in various domains.
Nick Bostrom (Global Policy, 2017). Strategic Implications of Openness in AI Development.
Hubinger (2020). An overview of 11 proposals for building safe advanced AI. Compares different variations on iterated amplification, debate, and recursive reward modeling, and evaluates them on the criteria of outer alignment, inner alignment, training competitiveness, and performance competitiveness.
Paul Christiano (2019). AI alignment landscape (EA Global).
Manheim and Garrabrant (2018). Categorizing Variants of Goodhart’s Law. Explores different failure modes of overoptimizing metrics at the expense of the real objective.
Ortega, Maini, et al (2018). Building safe artificial intelligence: specification, robustness, and assurance.
Everitt, Lea, Hutter (2018). AGI safety literature review.
Jacob Steinhardt (2015). Long-Term and Short-Term Challenges to Ensuring the Safety of AI Systems. A taxonomy of AI safety issues that require ordinary vs extraordinary engineering to address.
Nate Soares (2015). Safety engineering, target selection, and alignment theory. Identifies and motivates three major areas of AI safety research.
Ajeya Cotra (2021). The case for aligning narrowly superhuman models.
Dafoe, Hughes, et al (2020). Open problems in cooperative AI.
Abram Demski (2020). Learning Normativity: A Research Agenda.
Gruetzemacher, Dorner, Bernaola-Alvarez, Giattino, Manheim (2020). Forecasting AI Progress: A Research Agenda.
Olah, Cammarata, et al (2020). Zoom In: An Introduction to Circuits. An interpretability research agenda focused on studying the connections between neurons to find meaningful algorithms in the weights of neural networks.
Critch and Krueger (2020). AI Research Considerations for Human Existential Safety (ARCHES). Research agenda on how to steer technical AI research to avoid catastrophic outcomes for humanity.
Stuart Armstrong (2019). Synthesising a human’s preferences into a utility function. A research agenda focused on identifying partial human preferences (contrasting two situations along a single variable) and incorporating them into a utility function.
Leike, Krueger, et al (2018). Scalable agent alignment via reward modeling: a research direction. (blog post)
Allan Dafoe (2018). AI Governance: A Research Agenda.
Garrabrant and Demski (2018). Embedded agency. How can we align agents embedded in a world that is bigger than themselves? This agenda discusses and motivates the embedded agency problem and its subproblems: decision theory, embedded world models, robust delegation, and subsystem alignment.
Soares and Fallenstein (2017). Aligning Superintelligence with Human Interests: A Technical Research Agenda
Amodei, Olah, et al (2016). Concrete Problems in AI Safety. Research agenda focusing on accident risks that apply to current ML systems as well as more advanced future AI systems.
Taylor, Yudkowsky, LaVictoire, Critch (2016). Alignment for Advanced Machine Learning Systems
Paul Christiano (2020). My research methodology.
Evan Hubinger (2019). Chris Olah’s views on AGI safety.
Eliezer Yudkowsky (2018). The rocket alignment problem.
Brian Christian (2020). The Alignment Problem: Machine Learning and Human Values.
Toby Ord (2020). The Precipice: Existential Risk and the Future of Humanity.
Stuart Russell (2019). Human Compatible: AI and the Problem of Control.
Eric Drexler (2019). Reframing Superintelligence: Comprehensive AI Services as General Intelligence.
Max Tegmark (2017). Life 3.0: Being Human in the Age of AI.
Nick Bostrom (2014). Superintelligence: Paths, Dangers, Strategies.
Causal analysis of incentives
Everitt, Kumar, Krakovna, Legg (2019). Modeling AGI Safety Frameworks with Causal Influence Diagrams. Suggests diagrams for modeling different AGI safety frameworks, including reward learning, counterfactual oracles, and safety via debate.
Everitt, Ortega, Barnes, Legg (2019). Understanding Agent Incentives using Causal Influence Diagrams. Introduces observation and intervention incentives.
Impact measures and side effects
Krakovna, Orseau, Ngo, Martic, Legg (NeurIPS 2020). Avoiding Side Effects By Considering Future Tasks. Proposes an auxiliary reward for possible future tasks that provides an incentive to avoid side effects, and formalizes interference incentives.
Turner, Ratzlaff, Tadepalli (NeurIPS 2020). Avoiding Side Effects in Complex Environments. A scalable implementation of Attainable Utility Preservation that avoids side effects in the SafeLife environment.
Rahaman, Wolf, Goyal, Remme, Bengio (ICLR 2020). Learning the Arrow of Time. Proposes a potential-based reachability measure that is sensitive to magnitude of irreversible effects.
Shah, Krasheninnikov, Alexander, Abbeel, Dragan (ICLR 2019). Preferences Implicit in the State of the World. Since the initial state of the environment is often optimized for human preferences, information from the initial state can be used to infer both side effects that should be avoided as well as preferences for how the environment should be organized. (blog post, code)
Turner, Hadfield-Menell, Tadepalli (AIES 2020). Conservative Agency via Attainable Utility Preservation. Introduces Attainable Utility Preservation, a general impact measure that penalizes change in the utility attainable by the agent compared to the stepwise inaction baseline, and avoids introducing certain bad incentives. (code)
Krakovna, Orseau, Kumar, Martic, Legg (2019). Penalizing side effects using stepwise relative reachability (version 2). A general impact measure that penalizes reducing reachability of states compared to the stepwise inaction baseline, and avoids introducing certain bad incentives. (version 2 blog post, version 1 blog post)
Zhang, Durfee, Singh (IJCAI 2018). Minimax-Regret Querying on Side Effects for Safe Optimality in Factored Markov Decision Processes. Whitelisting the features of the state that the agent is permitted to change, and allowing the agent to query the human about changing a small number of features outside the whitelist.
Eysenbach, Gu, Ibarz, Levine (ICLR 2017). Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning. Reducing the number of irreversible actions taken by an RL agent by learning a reset policy that tries to return the environment to the initial state.
Armstrong and Levinstein (2017). Low Impact Artificial Intelligences. An intractable but enlightening definition of low impact for AI systems.
Interruptibility and corrigibility
Ryan Carey (AIES 2018). Incorrigibility in the CIRL Framework. Investigates the corrigibility of value learning agents under model misspecification.
El Mhamdi, Guerraoui, Hendrikx, Maurer (NeurIPS 2017). Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning.
Hadfield-Menell, Dragan, Abbeel, Russell (AIES 2017). The Off-Switch Game. This paper studies the interruptibility problem as a game between human and robot, and investigates which incentives the robot could have to allow itself to be switched off.
Orseau and Armstrong (UAI 2016). Safely interruptible agents. Provides a formal definition of safe interruptibility and shows that off-policy RL agents are more interruptible than on-policy agents. (blog post)
Soares, Fallenstein, Yudkowsky, Armstrong (2015). Corrigibility. Designing AI systems without incentives to resist corrective modifications by their creators.
Krueger, Maharaj, Leike (2020). Hidden incentives for auto-induced distributional shift. Introduces the auto-induced distributional shift (ADS) problem, where the agent cheats by modifying the task distribution.
Everitt, Krakovna, Orseau, Hutter, Legg (IJCAI 2017). Reinforcement learning with a corrupted reward channel. A formalization of the reward hacking problem in terms of true and corrupt reward, a proof that RL agents cannot overcome reward corruption, and a framework for giving the agent extra information to overcome reward corruption. (blog post, demo, code)
Amodei and Clark (2016). Faulty Reward Functions in the Wild. An example of reward function gaming in a boat racing game, where the agent gets a higher score by going in circles and hitting the same targets than by actually playing the game.
Jessica Taylor (AAAI Workshop 2016). Quantilizers: A Safer Alternative to Maximizers for Limited Optimization. Addresses objective specification problems by choosing from the top quantile of high-utility actions instead of the optimal action.
Tampering and wireheading
Everitt and Hutter (2019). Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective. Classifies reward tampering problems into reward function tampering, feedback tampering, and reward function input tampering, and compares different solutions to those problems. (blog post)
Everitt and Hutter (2016). Avoiding Wireheading with Value Reinforcement Learning. An alternative to RL that reduces the incentive to wirehead.
Laurent Orseau (2015). Mortal universal agents and wireheading. An investigation into how different types of artificial agents respond to opportunities to wirehead (unintended shortcuts to maximize their objective function).
Learning human intent
Inverse reinforcement learning
Shah, Gundotra, Abbeel, Dragan (ICML 2019). On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference.
Hadfield-Menell, Milli, Abbeel, Russell, Dragan (NeurIPS 2017). Inverse Reward Design. Formalizes the problem of inferring the true objective intended by the human based on the designed reward, and proposes an approach that helps to avoid side effects and reward hacking.
Fisac, Gates, et al (ISRR 2017). Pragmatic-Pedagogic Value Alignment. A cognitive science approach to the cooperative inverse reinforcement learning problem.
Milli, Hadfield-Menell, Dragan, Russell (IJCAI 2017). Should robots be obedient? Obedience to humans may sound like a great thing, but blind obedience can get in the way of learning human preferences.
Hadfield-Menell, Dragan, Abbeel, Russell (NeurIPS 2016). Cooperative inverse reinforcement learning. Defines value learning as a cooperative game where the human tries to teach the agent about their reward function, rather than giving optimal demonstrations like in standard IRL.
Evans, Stuhlmüller, Goodman (AAAI 2016). Learning the Preferences of Ignorant, Inconsistent Agents. Relaxing some assumptions on human rationality when inferring preferences.
Beth Barnes (2021). Imitative Generalisation (AKA ‘Learning the Prior’). An approach to teaching machines to imitate how humans generalize, which addresses some of the shortcomings of iterated amplification.
Barnes and Christiano (2020). Progress on AI safety via Debate.
Christiano, Shlegeris, Amodei (2018). Supervising strong learners by amplifying weak experts. Proposes Iterated Amplification, which progressively builds up a training signal for difficult problems by combining solutions to easier subproblems.
Irving, Christiano, Amodei (2018). AI safety via debate. Agents are trained to debate topics and point out flaws in one another’s arguments, and a human decides who wins. This could allow agents to learn to perform tasks at a superhuman level while remaining aligned with human preferences. (blog post)
Armstrong, Leike, Orseau, Legg (2020). Pitfalls of learning a reward function online. Introduces desirable properties for a learning process that prevent it from being manipulated by the agent: unriggability and uninfluenceability.
Jeon, Milli, Dragan (2020). Reward-rational (implicit) choice: A unifying formalism for reward learning.
Reddy, Dragan, Levine, Legg, Leike (2019). Learning Human Objectives by Evaluating Hypothetical Behavior. Introduces ReQueST (reward query synthesis via trajectory optimization), an algorithm for safely learning a reward model by querying the user about hypothetical behaviors. (blog post)
Ibarz, Leike, et al (NeurIPS 2018). Reward learning from human preferences and demonstrations in Atari.
Christiano, Leike, et al (NeurIPS 2017). Deep reinforcement learning from human preferences. Communicating complex goals to AI systems using human feedback (comparing pairs of agent trajectory segments).
Other technical work
Scott Garrabrant (2021). Finite factored sets. An alternative framework to Pearlian causal inference, with applications to embedded agency.
Yudkowsky and Soares (2017). Functional Decision Theory: A New Theory of Instrumental Rationality. New decision theory that avoids the pitfalls of causal and evidential decision theories. (blog post)
Garrabrant, Benson-Tilsen, Critch, Soares, Taylor (2016). Logical Induction. A computable algorithm for the logical induction problem.
Andrew Critch (2016). Parametric Bounded Löb’s Theorem and Robust Cooperation of Bounded Agents. FairBot cooperates in the Prisoner’s Dilemma if and only if it can prove that the opponent cooperates. Surprising result: FairBots cooperate with each other. (blog post: Open-source game theory is weird)
Yudkowsky and Herreshoff (2013). Tiling Agents for Self-Modifying AI, and the Löbian Obstacle.
Cohen, Vellambi, Hutter (2019). Asymptotically Unambitious Artificial General Intelligence. Introduces Boxed Myopic Artificial Intelligence (BoMAI), an RL algorithm that remains indifferent to gaining power in the outside world as its capabilities increase.
Babcock, Kramar, Yampolskiy (2017). Guidelines for Artificial Intelligence Containment.
Eliezer Yudkowsky (2002). The AI Box Experiment.
Rohin Shah (2021). BASALT: A Benchmark for Learning from Human Feedback. A set of Minecraft environments and a human evaluation protocol for solving tasks with no pre-specified reward function.
Wainwright and Eckersley (2019). SafeLife 1.0: Exploring Side Effects in Complex Environments. Presents a collection of environment levels testing for side effects in a Game of Life setting. (blog post)
Michaël Trazzi (2018). A Gym Gridworld Environment for the Treacherous Turn. (code)
Stuart Armstrong (2015). A toy model of the control problem. Agent obstructs the supervisor’s camera to avoid getting turned off.
Kaplan, McCandlish, et al (2020). Scaling laws for neural language models.
Hernandez and Brown (2020). AI and Efficiency. An analysis showing that the amount of compute needed to train a neural net to the same performance on ImageNet classification has been decreasing by a factor of 2 every 16 months.
Amodei and Hernandez (2018). AI and Compute. An analysis showing that the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time.
Koch, Langosco, Pfau, Le, Sharkey (2021). Objective robustness in deep reinforcement learning. Empirically studies objective robustness failures, which occur when an RL agent retains its capabilities out-of-distribution yet pursues the wrong objective.
Hubinger, van Merwijk, Mikulik, Skalse, Garrabrant (2019). Risks from Learned Optimization in Advanced Machine Learning Systems. Introduces the concept of mesa-optimization, which occurs when a learned model is itself an optimizer that may be pursuing a different objective than the outer model.
Goh, Cammarata, et al (Distill 2021). Multimodal Neurons in Artificial Neural Networks.
Cammarata, Carter, et al (Distill 2020). Thread: Circuits.
Olah, Satyanarayan, et al (Distill 2018). The Building Blocks of Interpretability.
Olah, Mordvintsev, Schubert (Distill 2017). Feature visualization.
Collections of technical works
Rohin Shah. Alignment newsletter. A weekly newsletter summarizing recent work relevant to AI safety.
Paul Christiano. AI Alignment. A blog on designing safe, efficient AI systems (approval-directed agents, aligned reinforcement learning agents, etc).
Berkeley AGI Safety course (CS294)
Rohin Shah (2021). FAQ: advice for AI alignment researchers.
Adam Gleave (2020). Beneficial AI Research Career Advice (mostly focused on grad school).
Buck Shlegeris (2020). How I think students should orient to AI safety.
Catherine Olsson (2018). ML engineering for AI safety and robustness: a Google Brain engineer’s guide for entering the field.
Leike et al (2016). AI Safety Syllabus.
Anderljung and Carlier (2021). Some AI governance research ideas.
Paul Christiano (2021). Experimentally evaluating whether honesty generalizes.
Owain Evans (2021). AI Safety Research Project Ideas.
Evan Hubinger (2019). Concrete experiments in inner alignment and Towards an empirical investigation of inner alignment.
(This list of resources was originally published as a blog post.)