Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms, or sellers in a marketplace. In order to do so responsibly, these socially consequential systems employ various fairness measures and interventions, many of which seek to allocate exposure based on worthiness. Because these constructs are typically not directly observable, platforms must instead resort to using proxy scores such as relevance and infer them from behavioral signals such as searcher clicks. Yet, it remains an open question whether relevance fulfills its role as such a worthiness score in high-stakes fair rankings. In this paper, we combine perspectives and tools from the social sciences, information retrieval, and fairness in machine learning to derive a set of desired criteria that relevance scores should satisfy in order to meaningfully guide fairness interventions. We then empirically show that not all of these criteria are met in a case study of relevance inferred from biased user click data. We assess the impact of these violations on the estimated system fairness and analyze whether existing fairness interventions may mitigate the identified issues. Our analyses and results surface the pressing need for new approaches to relevance collection and generation that are suitable for use in fair ranking.

However, a fundamental question remains: What is fairness? This question is not so easy to answer and is often skipped; instead of asking "what is fairness", the questions of "how to measure fairness of ML models" and "how to make ML models fair" are pursued. This paper does not intend to criticize individual approaches that address those latter questions and often propose important solutions. Rather, the aim is to make explicit the premises that underlie the various understandings of fairness and the approaches to solving fairness problems. In doing so, a largely concordant understanding can be elaborated that is based on a rich foundation in the history of philosophy. Subsequently, we show that the conception of fairness depends on multilayered normative evaluations; any discussion of fairML is reliant on adopting those normative stipulations. The basis for fair decisions is always the question of the equality of the people treated with respect to the subject matter concerned. With this decision basis, a decision rule is to be established, which in turn can be adapted to the concrete needs as a result of normative stipulations. Based on this basic concept of fairness, we turn to the questions of to what extent ML models can induce unfair treatments in automated decision-making (ADM), and of how to implement these normative stipulations in training an ML model and in using its predictions in ADM.

In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using a hierarchical BiLSTM model. We contribute a new benchmark dataset containing over two million sentences and their corresponding labels. We preserve the sentence order in this dataset and perform document-level train/test splits, which importantly allows incorporating contextual information in the modeling process. We evaluate the proposed approach on three benchmark datasets. Our results quantify the benefits of using context and contextual embeddings for citation worthiness. Lastly, through error analysis, we provide insights into cases where context plays an essential role in predicting citation worthiness.

Understanding customer behavior is fundamental for many use-cases in industry, especially in accelerated growth areas such as fin-tech and e-commerce. Structured data are often expensive, time-consuming and inadequate to analyze and study complex customer behaviors. In this paper, we propose a multi-graph embedding approach for creating a non-linear representation of customers in order to have a better knowledge of their characteristics without having any prior information about their financial status or their interests. By applying the current method we are able to predict users' future behavior with a reasonably high accuracy only by having the information of their friendship network. Potential applications include recommendation systems and credit risk forecasting.