Overview of the TREC 2020 Fair Ranking Track

This paper provides an overview of the NIST TREC 2020 Fair Ranking track. For 2020, we again adopted an academic search task, where we have a corpus of academic article abstracts and queries submitted to a production academic search engine. The central goal of the Fair Ranking track is to provide fair exposure to different groups of authors (a group fairness framing). We recognize that there may be multiple group definitions (e.g. based on demographics, stature, topic) and hoped for the systems to be robust to these. We expected participants to develop systems that optimize for fairness and relevance for arbitrary group definitions, and did not reveal the exact group definitions until after the evaluation runs were submitted.The track contains two tasks,reranking and retrieval, with a shared evaluation.

Learning to Limit Data Collection via Scaling Laws: Data Minimization Compliance in Practice

Data minimization is a legal obligation defined in the European Union's General Data Protection Regulation (GDPR) as the responsibility to process an adequate, relevant, and limited amount of personal data in relation to a processing purpose. However, unlike fairness or transparency, the principle has not seen wide adoption for machine learning systems due to a lack of computational interpretation. In this paper, we build on literature in machine learning and law to propose the first learning framework for limiting data collection based on an interpretation that ties the data collection purpose to system performance. We formalize a data minimization criterion based on performance curve derivatives and provide an effective and interpretable piecewise power law technique that models distinct stages of an algorithm's performance throughout data collection. Results from our empirical investigation offer deeper insights into the relevant considerations when designing a data minimization framework, including the choice of feature acquisition algorithm, initialization conditions, as well as impacts on individuals that hint at tensions between data minimization and fairness.

The Benchmark Lottery

The world of empirical machine learning (ML) strongly relies on benchmarks in order to determine the relative effectiveness of different algorithms and methods. This paper proposes the notion of "a benchmark lottery" that describes the overall fragility of the ML benchmarking process. The benchmark lottery postulates that many factors, other than fundamental algorithmic superiority, may lead to a method being perceived as superior. On multiple benchmark setups that are prevalent in the ML community, we show that the relative performance of algorithms may be altered significantly simply by choosing different benchmark tasks, highlighting the fragility of the current paradigms and potential fallacious interpretation derived from benchmarking ML methods. Given that every benchmark makes a statement about what it perceives to be important, we argue that this might lead to biased progress in the community. We discuss the implications of the observed phenomena and provide recommendations on mitigating them using multiple machine learning domains and communities as use cases, including natural language processing, computer vision, information retrieval, recommender systems, and reinforcement learning.

Fairness and Discrimination in Information Access Systems

Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness, to facilitate use of this work by scholars with experience in one (or neither) of these fields who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space.

Multi-FR: A Multi-Objective Optimization Method for Achieving Two-sided Fairness in E-commerce Recommendation

Two-sided marketplaces are an important component of many existing Internet services like Airbnb and Amazon, which have both consumers (e.g. users) and producers (e.g. retailers). Traditionally, the recommendation system in these platforms mainly focuses on maximizing customer satisfaction by recommending the most relevant items based on the learned user preference. However, it has been shown in previous works that solely optimizing the satisfaction of customers may lead to unfair exposure of items, which jeopardizes the benefits of producers. To tackle this problem, we propose a fairness-aware recommendation framework by using multi-objective optimization, Multi-FR, to adaptively balance the objectives between consumers and producers. In particular, Multi-FR adopts the multi-gradient descent to generate a Pareto set of solutions, where the most appropriate one is selected from the Pareto set. In addition, four fairness metrics/constraints are applied to make the recommendation results on both the consumer and producer side fair. We extensively evaluate our model on three real-world datasets, comparing with grid-search methods and using a variety of performance metrics. The experimental results demonstrate that Multi-FR can improve the recommendation fairness on both the consumer and producer side with little drop in recommendation quality, also outperforming several state-of-the-art fair ranking approaches.

A Multi-Objective Optimization Method for Achieving Two-sided Fairness in E-commerce Recommendation

Two-sided marketplaces are an important component of many existing Internet services like Airbnb and Amazon, which have both consumers (e.g. users) and producers (e.g. retailers). Traditionally, the recommendation system in these platforms mainly focuses on maximizing customer satisfaction by recommending the most relevant items based on the learned user preference. However, it has been shown in previous works that solely optimizing the satisfaction of customers may lead to unfair exposure of items, which jeopardizes the benefits of producers. To tackle this problem, we propose a fairness-aware recommendation framework by using multi-objective optimization, Multi-FR, to adaptively balance the objectives between consumers and producers. In particular, Multi-FR adopts the multi-gradient descent to generate a Pareto set of solutions, where the most appropriate one is selected from the Pareto set. In addition, four fairness metrics/constraints are applied to make the recommendation results on both the consumer and producer side fair. We extensively evaluate our model on three real-world datasets, comparing with grid-search methods and using a variety of performance metrics. The experimental results demonstrate that Multi-FR can improve the recommendation fairness on both the consumer and producer side with little drop in recommendation quality, also outperforming several state-of-the-art fair ranking approaches.

Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification

While current information retrieval systems are effective for known-item retrieval where the searcher provides a precise name or identifier for the item being sought, systems tend to be much less effective for cases where the searcher is unable to express a precise name or identifier. We refer to this as tip of the tongue (TOT) known-item retrieval, named after the cognitive state of not being able to retrieve an item from memory. Using movie search as a case study, we explore the characteristics of questions posed by searchers in TOT states in a community question answering website. We analyze how searchers express their information needs during TOT states in the movie domain. Specifically, what information do searchers remember about the item being sought and how do they convey this information? Our results suggest that searchers use a combination of information about: (1) the content of the item sought, (2) the context in which they previously engaged with the item, and (3) previous attempts to find the item using other resources (e.g., search engines). Additionally, searchers convey information by sometimes expressing uncertainty (i.e., hedging), opinions, emotions, and by performing relative (vs. absolute) comparisons with attributes of the item. As a result of our analysis, we believe that searchers in TOT states may require specialized query understanding methods or document representations. Finally, our preliminary retrieval experiments show the impact of each information type presented in information requests on retrieval performance.

Operationalizing the Legal Principle of Data Minimization for Personalization

Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed (`data minimisation')". To date, the legal and computational definitions of `purpose limitation' and `data minimization' remain largely unclear. In particular, the interpretation of these principles is an open issue for information access systems that optimize for user experience through personalization and do not strictly require personal data collection for the delivery of basic service. In this paper, we identify a lack of a homogeneous interpretation of the data minimization principle and explore two operational definitions applicable in the context of personalization. The focus of our empirical study in the domain of recommender systems is on providing foundational insights about the (i) feasibility of different data minimization definitions, (ii) robustness of different recommendation algorithms to minimization, and (iii) performance of different minimization strategies.We find that the performance decrease incurred by data minimization might not be substantial, but that it might disparately impact different users---a finding which has implications for the viability of different formal minimization definitions. Overall, our analysis uncovers the complexities of the data minimization problem in the context of personalization and maps the remaining computational and regulatory challenges.

Overview of the TREC 2019 Fair Ranking Track

The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different content providers in addition to classic notions of relevance. As part of the benchmark, we defined standardized fairness metrics with evaluation protocols and released a dataset for the fair ranking problem. The 2019 task focused on reranking academic paper abstracts given a query. The objective was to fairly represent relevant authors from several groups that were unknown at the system submission time. Thus, the track emphasized the development of systems which have robust performance across a variety of group definitions. Participants were provided with querylog data (queries, documents, and relevance) from Semantic Scholar. This paper presents an overview of the track, including the task definition, descriptions of the data and the annotation process, as well as a comparison of the performance of submitted systems.

Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks

Classical information retrieval (IR) methods, such as query likelihood and BM25, score documents independently w.r.t. each query term, and then accumulate the scores. Assuming query term independence allows precomputing term-document scores using these models---which can be combined with specialized data structures, such as inverted index, for efficient retrieval. Deep neural IR models, in contrast, compare the whole query to the document and are, therefore, typically employed only for late stage re-ranking. We incorporate query term independence assumption into three state-of-the-art neural IR models: BERT, Duet, and CKNRM---and evaluate their performance on a passage ranking task. Surprisingly, we observe no significant loss in result quality for Duet and CKNRM---and a small degradation in the case of BERT. However, by operating on each query term independently, these otherwise computationally intensive models become amenable to offline precomputation---dramatically reducing the cost of query evaluations employing state-of-the-art neural ranking models. This strategy makes it practical to use deep models for retrieval from large collections---and not restrict their usage to late stage re-ranking.