Recommending to Strategic Users

Recommendation systems are pervasive in the digital economy. An important assumption in many deployed systems is that user consumption reflects user preferences in a static sense: users consume the content they like with no other considerations in mind. However, as we document in a large-scale online survey, users do choose content strategically to influence the types of content they get recommended in the future. We model this user behavior as a two-stage noisy signalling game between the recommendation system and users: the recommendation system initially commits to a recommendation policy, presents content to the users during a cold start phase which the users choose to strategically consume in order to affect the types of content they will be recommended in a recommendation phase. We show that in equilibrium, users engage in behaviors that accentuate their differences to users of different preference profiles. In addition, (statistical) minorities out of fear of losing their minority content exposition may not consume content that is liked by mainstream users. We next propose three interventions that may improve recommendation quality (both on average and for minorities) when taking into account strategic consumption: (1) Adopting a recommendation system policy that uses preferences from a prior, (2) Communicating to users that universally liked ("mainstream") content will not be used as basis of recommendation, and (3) Serving content that is personalized-enough yet expected to be liked in the beginning. Finally, we describe a methodology to inform applied theory modeling with survey results.

Benchmarking Interpretability Tools for Deep Neural Networks

Interpreting deep neural networks is the topic of much current research in AI. However, few interpretability techniques have shown to be competitive tools in practical applications. Inspired by how benchmarks tend to guide progress in AI, we make three contributions. First, we propose trojan rediscovery as a benchmarking task to evaluate how useful interpretability tools are for generating engineering-relevant insights. Second, we design two such approaches for benchmarking: one for feature attribution methods and one for feature synthesis methods. Third, we apply our benchmarks to evaluate 16 feature attribution/saliency methods and 9 feature synthesis methods. This approach finds large differences in the capabilities of these existing tools and shows significant room for improvement. Finally, we propose several directions for future work. Resources are available at https://github.com/thestephencasper/benchmarking_interpretability

Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks

Deep neural networks (DNNs) are powerful, but they can make mistakes that pose significant risks. A model performing well on a test set does not imply safety in deployment, so it is important to have additional tools to understand its flaws. Adversarial examples can help reveal weaknesses, but they are often difficult for a human to interpret or draw generalizable, actionable conclusions from. Some previous works have addressed this by studying human-interpretable attacks. We build on these with three contributions. First, we introduce a method termed Search for Natural Adversarial Features Using Embeddings (SNAFUE) which offers a fully-automated method for finding "copy/paste" attacks in which one natural image can be pasted into another in order to induce an unrelated misclassification. Second, we use this to red team an ImageNet classifier and identify hundreds of easily-describable sets of vulnerabilities. Third, we compare this approach with other interpretability tools by attempting to rediscover trojans. Our results suggest that SNAFUE can be useful for interpreting DNNs and generating adversarial data for them. Code is available at https://github.com/thestephencasper/snafue

White-Box Adversarial Policies in Deep Reinforcement Learning

Adversarial examples against AI systems pose both risks via malicious attacks and opportunities for improving robustness via adversarial training. In multiagent settings, adversarial policies can be developed by training an adversarial agent to minimize a victim agent's rewards. Prior work has studied black-box attacks where the adversary only sees the state observations and effectively treats the victim as any other part of the environment. In this work, we experiment with white-box adversarial policies to study whether an agent's internal state can offer useful information for other agents. We make three contributions. First, we introduce white-box adversarial policies in which an attacker can observe a victim's internal state at each timestep. Second, we demonstrate that white-box access to a victim makes for better attacks in two-agent environments, resulting in both faster initial learning and higher asymptotic performance against the victim. Third, we show that training against white-box adversarial policies can be used to make learners in single-agent environments more robust to domain shifts.

Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL

Multi-agent reinforcement learning (MARL) is a powerful tool for training automated systems acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group incentives diverge. Humans are remarkably capable at solving these social dilemmas. It is an open problem in MARL to replicate such cooperative behaviors in selfish agents. In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL. We propose an augmentation to a Markov game where agents voluntarily agree to binding state-dependent transfers of reward, under pre-specified conditions. Our contributions are theoretical and empirical. First, we show that this augmentation makes all subgame-perfect equilibria of all fully observed Markov games exhibit socially optimal behavior, given a sufficiently rich space of contracts. Next, we complement our game-theoretic analysis by showing that state-of-the-art RL algorithms learn socially optimal policies given our augmentation. Our experiments include classic static dilemmas like Stag Hunt, Prisoner's Dilemma and a public goods game, as well as dynamic interactions that simulate traffic, pollution management and common pool resource management.

Towards Psychologically-Grounded Dynamic Preference Models

Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people's preferences must be grounded in psychologically plausible models. We contribute a methodology for developing grounded dynamic preference models. We demonstrate this method with models that capture three classic effects from the psychology literature: Mere-Exposure, Operant Conditioning, and Hedonic Adaptation. We conduct simulation-based studies to show that the psychological models manifest distinct behaviors that can inform system design. Our study has two direct implications for dynamic user modeling in recommendation systems. First, the methodology we outline is broadly applicable for psychologically grounding dynamic preference models. It allows us to critique recent contributions based on their limited discussion of psychological foundation and their implausible predictions. Second, we discuss implications of dynamic preference models for recommendation systems evaluation and design. In an example, we show that engagement and diversity metrics may be unable to capture desirable recommendation system performance.

Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks

The last decade of machine learning has seen drastic increases in scale and capabilities, and deep neural networks (DNNs) are increasingly being deployed across a wide range of domains. However, the inner workings of DNNs are generally difficult to understand, raising concerns about the safety of using these systems without a rigorous understanding of how they function. In this survey, we review literature on techniques for interpreting the inner components of DNNs, which we call "inner" interpretability methods. Specifically, we review methods for interpreting weights, neurons, subnetworks, and latent representations with a focus on how these techniques relate to the goal of designing safer, more trustworthy AI systems. We also highlight connections between interpretability and work in modularity, adversarial robustness, continual learning, network compression, and studying the human visual system. Finally, we discuss key challenges and argue for future work in interpretability for AI safety that focuses on diagnostics, benchmarking, and robustness.

Building Human Values into Recommender Systems: An Interdisciplinary Synthesis

Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making.

How to talk so your robot will learn: Instructions, descriptions, and pragmatics

From the earliest years of our lives, humans use language to express our beliefs and desires. Being able to talk to artificial agents about our preferences would thus fulfill a central goal of value alignment. Yet today, we lack computational models explaining such flexible and abstract language use. To address this challenge, we consider social learning in a linear bandit setting and ask how a human might communicate preferences over behaviors (i.e. the reward function). We study two distinct types of language: instructions, which provide information about the desired policy, and descriptions, which provide information about the reward function. To explain how humans use these forms of language, we suggest they reason about both known present and unknown future states: instructions optimize for the present, while descriptions generalize to the future. We formalize this choice by extending reward design to consider a distribution over states. We then define a pragmatic listener agent that infers the speaker's reward function by reasoning about how the speaker expresses themselves. We validate our models with a behavioral experiment, demonstrating that (1) our speaker model predicts spontaneous human behavior, and (2) our pragmatic listener is able to recover their reward functions. Finally, we show that in traditional reinforcement learning settings, pragmatic social learning can integrate with and accelerate individual learning. Our findings suggest that social learning from a wider range of language -- in particular, expanding the field's present focus on instructions to include learning from descriptions -- is a promising approach for value alignment and reinforcement learning more broadly.