Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comments targeting different demographic groups can vary markedly across groups. In such settings, trained models can be dominated by the relationships that best fit the majority group, leading to disparate performance. We propose framing toxicity detection as multi-task learning (MTL), allowing a model to specialize on the relationships that are relevant to each demographic group while also leveraging shared properties across groups. With toxicity detection, each task corresponds to identifying toxicity against a particular demographic group. However, traditional MTL requires labels for all tasks to be present for every data point. To address this, we propose Conditional MTL (CondMTL), wherein only training examples relevant to the given demographic group are considered by the loss function. This lets us learn group specific representations in each branch which are not cross contaminated by irrelevant labels. Results on synthetic and real data show that using CondMTL improves predictive recall over various baselines in general and for the minority demographic group in particular, while having similar overall accuracy.

We conducted a lab-based eye-tracking study to investigate how interactivity of an AI-powered fact-checking system affects user interactions, such as dwell time, attention, and mental resources involved in using the system. A within-subject experiment was conducted, where participants used an interactive and a non-interactive version of a mock AI fact-checking system, and rated their perceived correctness of COVID-19 related claims. We collected web-page interactions, eye-tracking data, and mental workload using NASA-TLX. We found that the presence of the affordance of interactively manipulating the AI system's prediction parameters affected users’ dwell times, and eye-fixations on AOIs, but not mental workload. In the interactive system, participants spent the most time evaluating claims’ correctness, followed by reading news. This promising result shows a positive role of interactivity in a mixed-initiative AI-powered system.

When annotators label data, a key metric for quality assurance is inter-annotator agreement (IAA): the extent to which annotators agree on their labels. Though many IAA measures exist for simple categorical and ordinal labeling tasks, relatively little work has considered more complex labeling tasks, such as structured, multi-object, and free-text annotations. Krippendorff’s α, best known for use with simpler labeling tasks, does have a distance-based formulation with broader applicability, but little work has studied its efficacy and consistency across complex annotation tasks.

Human annotations are critical for training and evaluating supervised learning models, yet annotators often disagree with one another, especially as annotation tasks increase in complexity. A common strategy to improve label quality is to ask multiple annotators to label the same item and then aggregate their labels. While many aggregation models have been proposed for simple annotation tasks, how can we reason about and resolve annotator disagreement for more complex annotation tasks (e.g., continuous, structured, or high-dimensional), without needing to devise a new aggregation model for every different complex annotation task? We address two distinct challenges in this work. Firstly, how can a general aggregation model support merging of complex labels across diverse annotation tasks? Secondly, for multi-object annotation tasks that require annotators to provide multiple labels for each item being annotated (e.g., labeling named-entities in a text or visual entities in an image), how do we match which annotator label refers to which entity, such that only matching labels are aggregated across annotators? Using general constructs for merging and matching, our model not only supports diverse tasks, but delivers equal or better results than prior aggregation models: general and task-specific.

While most user content posted on social media is benign, other content, such as violent or adult imagery, must be detected and blocked. Unfortunately, such detection is difficult to automate, due to high accuracy requirements, costs of errors, and nuanced rules for acceptable content. Consequently, social media platforms today rely on a vast workforce of human moderators. However, mounting evidence suggests that exposure to disturbing content can cause lasting psychological and emotional damage to some moderators. To mitigate such harm, we investigate a set of blur-based moderation interfaces for reducing exposure to disturbing content whilst preserving moderator ability to quickly and accurately flag it. We report experiments with Mechanical Turk workers to measure moderator accuracy, speed, and emotional well-being across six alternative designs. Our key findings show interactive blurring designs can reduce emotional impact without sacrificing moderation accuracy and speed.

Modeling annotators and their labels is valuable for ensuring collected data quality. Though many models have been proposed for binary or categorical labels, prior methods do not generalize to complex annotations (e.g., open-ended text, multivariate, or structured responses) without devising new models for each specific task. To obviate the need for task-specific modeling, we propose to model distances between labels, rather than the labels themselves. Our models are largely agnostic to the distance function; we leave it to the requesters to specify an appropriate distance function for their given annotation task. We propose three models of annotation quality, including a Bayesian hierarchical extension of multidimensional scaling which can be trained in an unsupervised or semi-supervised manner. Results show the generality and effectiveness of our models across diverse complex annotation tasks: sequence labeling, translation, syntactic parsing, and ranking.

While test collections provide the cornerstone of system-based evaluation in information retrieval, human relevance judging has become prohibitively expensive as collections have grown ever larger. Consequently, intelligently deciding which documents to judge has become increasingly important. We propose a two-phase approach to intelligent judging across topics which does not require document rankings from a shared task. In the first phase, we dynamically select the next topic to judge via a multi-armed bandit method. In the second phase, we employ active learning to select which document to judge next for that topic. Experiments on three TREC collections (varying scarcity of relevant documents) achieve t 0.90 correlation for [email protected] ranking and find 90% of the relevant documents at 48% of the original budget. To support reproducibility and follow-on work, we have shared our code online1.

Because it is expensive to construct test collections for Cranfield-based evaluation of information retrieval systems, a variety of lower-cost methods have been proposed. The reliability of these methods is often validated by measuring rank correlation (e.g., Kendall's tau) between known system rankings on the full test collection vs. observed system rankings on the lower-cost one. However, existing rank correlation measures do not consider the statistical significance of score differences between systems in the observed rankings. To address this, we propose two statistical-significance-aware rank correlation measures, one of which is a head-weighted version of the other. We first show empirical differences between our proposed measures and existing ones. We then compare the measures while benchmarking four system evaluation methods: pooling, crowdsourcing, evaluation with incomplete judgments, and automatic system ranking. We show that use of our measures can lead to different experimental conclusions regarding reliability of alternative low-cost evaluation methods.

While peer-agreement and gold checks are well-established methods for ensuring quality in crowdsourced data collection, we explore a relatively new direction for quality control: estimating work quality directly from workers’ behavioral traces collected during annotation. We propose three behavior-based models to predict label correctness and worker accuracy, then further apply model predictions to label aggregation and optimization of label collection. As part of this work, we collect and share a new Mechanical Turk dataset of behavioral signals judging the relevance of search results. Results show that behavioral data can be effectively used to predict work quality, which could be especially useful with single labeling or in a cold start scenario in which individuals’ prior work history is unavailable. We further show improvement in label aggregation and reducing labeling cost while ensuring data quality.

While crowdsourcing offers a low-cost, scalable way to collect relevance judgments, lack of transparency with remote crowd work has limited understanding about the quality of collected judgments. In prior work, we showed a variety of benefits from asking crowd workers to provide \em rationales for each relevance judgment \citemcdonnell2016relevant. In this work, we scale up our rationale-based judging design to assess its reliability on the 2014 TREC Web Track, collecting roughly 25K crowd judgments for 5K document-topic pairs. We also study having crowd judges perform topic-focused judging, rather than across topics, finding this improves quality. Overall, we show that crowd judgments can be used to reliably rank IR systems for evaluation. We further explore the potential of rationales to shed new light on reasons for judging disagreement between experts and crowd workers. Our qualitative and quantitative analysis distinguishes subjective vs.\ objective forms of disagreement, as well as the relative importance of each disagreement cause, and we present a new taxonomy for organizing the different types of disagreement we observe. We show that many crowd disagreements seem valid and plausible, with disagreement in many cases due to judging errors by the original TREC assessors. We also share our WebCrowd25k dataset, including: (1) crowd judgments with rationales, and (2) taxonomy category labels for each judging disagreement analyzed.

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning . Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.

When collecting subjective human ratings of items, it can be difficult to measure and enforce data quality due to task subjectivity and lack of insight into how judges’ arrive at each rating decision. To address this, we propose requiring judges to provide a specific type of rationale underlying each rating decision. We evaluate this approach in the domain of Information Retrieval, where human judges rate the relevance of Webpages to search queries. Cost-benefit analysis over 10,000 judgments collected on Mechanical Turk suggests a win-win: experienced crowd workers provide rationales with almost no increase in task completion time while providing a multitude of further benefits, including more reliable judgments and greater transparency for evaluating both human raters and their judgments. Further benefits include reduced need for expert gold, the opportunity for dual-supervision from ratings and rationales, and added value from the rationales themselves.

While many methods have been proposed to ensure data quality for objective tasks (in which a single correct response is presumed to exist for each item), estimating data quality with subjective tasks remains largely unexplored. Consider the popular task of collecting instance ratings from human judges: while agreement tends be high for instances having extremely good or bad properties, instances with more middling properties naturally elicit a wider variance in opinion. In addition, because such subjectivity permits a valid diversity of responses, it can be difficult to detect if a judge does not undertake the task in good faith. To address this, we propose a probabilistic, heteroskedastic model in which the means and variances of worker responses are modeled as functions of instance attributes. We derive efficient Expectation Maximization (EM) learning and variational inference algorithms for parameter estimation. We apply our model to a large dataset of 24,132 Mechanical Turk ratings of user experience in viewing videos on smartphones with varying hardware capabilities. Results show that our method is effective at both predicting user ratings and in detecting unreliable respondents.

Web crawls provide valuable snapshots of the Web which enable a wide variety of research, be it distributional analysis to characterize Web properties or use of language, content analysis in social science, or Information Retrieval (IR) research to develop and evaluate effective search algorithms. While many English-centric Web crawls exist, existing public Arabic Web crawls are quite limited, limiting research and development. To remedy this, we present ArabicWeb16, a new public Web crawl of roughly 150M Arabic Web pages with significant coverage of dialectal Arabic as well as Modern Standard Arabic. For IR researchers, we expect ArabicWeb16 to support various research areas: ad-hoc search, question answering, filtering, cross-dialect search, dialect detection, entity search, blog search, and spam detection. Combined use with a separate Arabic Twitter dataset we are also collecting may provide further value.

While recent work has shown that a worker’s performance can be more accurately modeled by temporal correlation in task performance, a fundamental challenge remains in the need for expert gold labels to evaluate a worker’s performance. To solve this problem, we explore two methods of utilizing limited gold labels, initial training and periodic updating. Furthermore, we present a novel way of learning a prediction model in the absence of gold labels with uncertaintyaware learning and soft-label updating. Our experiment with a real crowdsourcing dataset demonstrates that periodic updating tends to show better performance than initial training when the number of gold labels are very limited (< 25).

We consider a finite-pool data categorization scenario which requires exhaustively classifying a given set of examples with a limited budget. We adopt a hybrid human-machine approach which blends automatic machine learning with human labeling across a tiered workforce composed of domain experts and crowd workers. To effectively achieve high-accuracy labels over the instances in the pool at minimal cost, we develop a novel approach based on decision-theoretic active learning. On the important task of biomedical citation screening for systematic reviews, results on real data show that our method achieves consistent improvements over baseline strategies. To foster further research by others, we have made our data available online.

While temporal behavioral patterns can be discerned to underlie real crowd work, prior studies have typically modeled worker performance under a simplified i.i.d. assumption. To better model such temporal worker behavior, we propose a time-series label prediction model for crowd work. This latent variable model captures and summarizes past worker behavior, enabling us to better predict the quality of each worker's next label. Given inherent uncertainty in prediction, we also investigate a decision reject option to balance the tradeoff between prediction accuracy vs. coverage. Results show our model improves accuracy of both label prediction on real crowd worker data, as well as data quality overall.

While many multidimensional models of relevance have been posited, prior studies have been largely exploratory rather than confirmatory. Lacking a methodological framework to quantify the relationships among factors or measure model fit to observed data, many past models could not be empirically tested or falsified. To enable more positivist experimentation, Xu and Chen [77] proposed a psychometric framework for multidimensional relevance modeling. However, we show their framework exhibits several methodological limitations which could call into question the validity of findings drawn from it. In this work, we identify and address these limitations, scale their methodology via crowdsourcing, and describe quality control methods from psychometrics which stand to benefit crowdsourcing IR studies in general. Methodology we describe for relevance judging is expected to benefit both human-centered and systems-centered IR.

While many statistical consensus methods now exist, relatively little comparative benchmarking and integration of techniques has made it increasingly difficult to determine the current state-of-the-art, to evaluate the relative benefit of new methods, to understand where specific problems merit greater attention, and to measure field progress over time. To make such comparative evaluation easier for everyone, we present SQUARE, an open source shared task framework including benchmark datasets, defined tasks, standard metrics, and reference implementations with empirical results for several popular methods. In addition to measuring performance on a variety of public, real crowd datasets, the benchmark also varies supervision and noise by manipulating training size and labeling error. We envision SQUARE as dynamic and continually evolving, with new datasets and reference implementations being added according to community needs and interest. We invite community contributions and participation.

In crowdsourced relevance judging, each crowd worker typically judges only a small number of examples, yielding a sparse and imbalanced set of judgments in which relatively few workers influence output consensus labels, particularly with simple consensus methods like majority voting. We show how probabilistic matrix factorization, a standard approach in collaborative filtering, can be used to infer missing worker judgments such that all workers influence output labels. Given complete worker judgments inferred by PMF, we evaluate impact in unsupervised and supervised scenarios. In the supervised case, we consider both weighted voting and worker selection strategies based on worker accuracy. Experiments on crowd judgments from the 2010 TREC Relevance Feedback Track show promise of the PMF approach merits further investigation and analysis.

The first computers were people. Today, Internet-based access to 24/7 online human crowds has led to a renaissance of research in human computation and the advent of crowdsourcing. These new opportunities have brought a disruptive shift to research and practice for how we build intelligent systems today. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see human computation being integrated into the systems themselves, operating in concert with automation. This tutorial introduces opportunities and challenges of human computation and crowdsourcing, particularly for search evaluation and developing hybrid search solutions that integrate human computation with traditional forms of automated search. We review methodology and findings of recent research and survey current generation crowdsourcing platforms now available, analyzing methods, potential, and limitations across platforms.

As ever-larger training sets for learning to rank are created, scalability of learning has become increasingly important to achieving continuing improvements in ranking accuracy. Exploiting independence of "summation form" computations, we show how each iteration in ListNet gradient descent can benefit from parallel execution. We seek to draw the attention of the IR community to use Spark, a newly introduced distributed cluster computing system, for reducing training time of iterative learning to rank algorithms. Unlike MapReduce, Spark is especially suited for iterative and interactive algorithms. Our results show near linear reduction in ListNet training time using Spark on Amazon EC2 clusters.

We investigate temporal resolution of documents, such as determining the date of publication of a story based on its text. We describe and evaluate a model that build histograms encoding the probability of different temporal periods for a document. We construct histograms based on the Kullback-Leibler Divergence between the language model for a test document and supervised language models for each interval. Initial results indicate this language modeling approach is effective for predicting the dates of publication of short stories, which contain few explicit mentions of years.

Crowdsourcing has emerged in recent years as a promising new avenue for leveraging today's digitally-connected, diverse, distributed workforce. Generally speaking, crowdsourcing describes outsourcing of tasks to a large group of people instead of assigning such tasks to an in-house employee or contractor. Crowdsourcing platforms such as Amazon Mechanical Turk and CrowdFlower have gained particular attention as active online market places for reaching and tapping into this still largely under-utilized workforce. Crowdsourcing also offers intriguing new opportunities for accomplishing different kinds of tasks or achieving broader participation than previously possible, as well as completing standard tasks more accurately in less time and at lower cost. Unlocking the potential of crowdsourcing in practice, however, requires a tri-partite understanding of principles, platforms, and best practices. We will introduce the opportunities and challenges of crowdsourcing while discussing the three issues above. This will provide a basic foundation to begin crowdsourcing in the context of one's own particular tasks

We study how to best use crowdsourced relevance judgments learning to rank [1, 7]. We integrate two lines of prior work: unreliable crowd-based binary annotation for binary classification [5, 3], and aggregating graded relevance judgments from reliable experts for ranking [7]. To model varying performance of the crowd, we simulate annotation noise with varying magnitude and distributional properties. Evaluation on three LETOR test collections reveals a striking trend contrary to prior studies: single labeling outperforms consensus methods in maximizing learner accuracy relative to annotator eýort. We also see surprising consistency of the learning curve across noise distributions, as well as greater challenge with the adversarial case for multi-class labeling.

We consider an interactive information retrieval task in which the user is interested in finding several to many relevant documents with minimal effort. Given an initial document ranking, user interaction with the system produces relevance feedback (RF) which the system then uses to revise the ranking. This interactive process repeats until the user terminates the search. To maximize accuracy relative to user effort, we propose an active learning strategy. At each iteration, the document whose relevance is maximally uncertain to the system is slotted high into the ranking in order to obtain user feedback for it. Simulated feedback on the Robust04 TREC collection shows our active learning approach dominates several standard RF baselines relative to the amount of feedback provided by the user. Evaluation on Robust04 under noisy feedback and on LETOR collections further demonstrate the effectiveness of active learning, as well as value of negative feedback in this task scenario.

Crowdsourcing has emerged in recent years as an exciting new avenue for leveraging the tremendous potential and resources of today's digitally-connected, diverse, distributed workforce. Generally speaking, crowdsourcing describes outsourcing of tasks to a large group of people instead of assigning such tasks to an in-house employee or contractor. Crowdsourcing platforms such as Amazon Mechanical Turk and CrowdFlower have gained particular attention as active online market places for reaching and tapping into this glut of a still largely under-utilized workforce.

The advent of crowdsourcing is revolutionizing data annotation, evaluation, and other traditionally manual-labor intensive processes by dramatically reducing the time, cost, and effort involved. This in turn is driving a disruptive shift in search and data mining methodology in areas such as:

Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation. In this paper, we show retrieval accuracy with Metzler and Croft's Markov random field (MRF) approach can be similarly improved via supervised learning. While the original MRF method estimates a parameter for each of its three feature classes from data, parameters within each class are set via a uniform weighting scheme adopted from the standard unigram. We conjecture greater MRF retrieval accuracy should be possible by better estimating within-class parameters, particularly for verbose queries employing natural language terms. Retrieval experiments with these queries on three TREC document collections show our improved MRF consistently out-performs both the original MRF and supervised unigram baselines. Additional experiments using blind-feedback and evaluation with optimal weighting demonstrate both the immediate value and further potential of our method.