Learning the Structure of Deep Sparse Graphical Models

Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single infinitely wide hidden layer. In this paper, we introduce the cascading Indian buffet process (CIBP), which provides a nonparametric prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network so each unit can additionally vary its behavior between discrete and continuous representations. We provide Markov chain Monte Carlo algorithms for inference in these belief networks and explore the structures learned on several image data sets.Comment: 20 pages, 6 figures, AISTATS 2010, Revise

Stretching Human Laws to Apply to Machines: The Dangers of a Colorblind Computer

Automated decision-making has become widespread in recent years, largely due to advances in machine learning. As a result of this trend, machine learning systems are increasingly used to make decisions in high-stakes domains, such as employment or university admissions. The weightiness of these decisions has prompted the realization that, like humans, machines must also comply with the law. But human decision- making processes are quite different from automated decisionmaking processes, which creates a mismatch between laws and the decision makers to which they are intended to apply. In turn, this mismatch can lead to counterproductive outcomes. We take antidiscrimination laws in employment as a case study, with a particular focus on Title VII of the Civil Rights Act of 1964. A common strategy for mitigating bias in employment decisions is to blind human decision makers to the sensitive attributes of the applicants, such as race. The same strategy can also be used in an automated decision-making context by blinding the machine learning system to the race of the applicants (strategy 1). This strategy seems to comply with Title VII, but it does not necessarily mitigate bias because machine learning systems are adroit at using proxies for race if available. An alternative strategy is to not blind the system to race (strategy 2), thereby allowing it to use this information to mitigate bias. However, although preferable from a machine learning perspective, this strategy appears to violate Title VII. We contend that this conflict between strategies 1 and 2 highlights a broader legal and policy challenge, namely, that laws designed to regulate human behavior may not be appropriate when stretched to apply to machines. Indeed, they may even be detrimental to the very people that they were designed to protect. Although scholars have explored legal arguments in an attempt to press strategy 2 into compliance with Title VII, we believe there lies a middle ground between strategies 1 and 2 that involves partial blinding-that is, blinding the system to race only during deployment and not during training (strategy 3). We present strategy 3 as a Goldilocks solution for discrimination in employment decisions (as well as other domains), because it allows for the mitigation of bias while still complying with Title VII. Ultimately, any solution to the general problem of stretching human laws to apply to machines must be sociotechnical in nature, drawing on work in both machine learning and the law. This is borne out in strategy 3, which involves innovative work in machine learning (viz. the development of disparate learning processes) and creative legal analysis (viz. analogizing strategy 3 to legally accepted auditing procedures)

Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities

Combining Sentiment Lexica with a Multi-View Variational Autoencoder

When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.Comment: To appear in NAACL-HLT 201

Auditing Search Engines for Differential Satisfaction Across Demographics

Many online services, such as search engines, social media platforms, and digital marketplaces, are advertised as being available to any user, regardless of their age, gender, or other demographic factors. However, there are growing concerns that these services may systematically underserve some groups of users. In this paper, we present a framework for internally auditing such services for differences in user satisfaction across demographic groups, using search engines as a case study. We first explain the pitfalls of na\"ively comparing the behavioral metrics that are commonly used to evaluate search engines. We then propose three methods for measuring latent differences in user satisfaction from observed differences in evaluation metrics. To develop these methods, we drew on ideas from the causal inference literature and the multilevel modeling literature. Our framework is broadly applicable to other online services, and provides general insight into interpreting their evaluation metrics.Comment: 8 pages Accepted at WWW 201

The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation

We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal dynamics evidenced via turn-taking behavior, our model captures more nuanced influence relationships, evidenced via linguistic accommodation patterns in interaction content. The model, which is based on a discrete analog of the multivariate Hawkes process, permits a fully Bayesian inference algorithm. We validate our model's ability to discover latent influence patterns using transcripts of arguments heard by the US Supreme Court and the movie "12 Angry Men." We showcase our model's capabilities by using it to infer latent influence patterns from Federal Open Market Committee meeting transcripts, demonstrating state-of-the-art performance at uncovering social dynamics in group discussions.Comment: 14 pages, 7 figures, to appear in AISTATS 2015. Fixed minor formatting issue