publications

2022

1. Explainable Information Retrieval: A Survey

   Anand, Avishek, Lyu, Lijun,  Idahl, Maximilian, Wang, Yumeng, Wallat, Jonas, and Zhang, Zijian

   arXiv preprint arXiv:2211.02405 2022

   Abs

   Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying perspectives. In addition, it reflects on the common concern of evaluating explanations and highlights open challenges and opportunities.

2021

1. Multimodal news analytics using measures of cross-modal entity and context consistency

   Müller-Budack, Eric, Theiner, Jonas, Diering, Sebastian,  Idahl, Maximilian, Hakimov, Sherzod, and Ewerth, Ralph

   International Journal of Multimedia Information Retrieval 2021
2. Towards Benchmarking the Utility of Explanations for Model Debugging

   Idahl, Maximilian, Lyu, Lijun, Gadiraju, Ujwal, and Anand, Avishek

   In Proceedings of the First Workshop on Trustworthy Natural Language Processing Jun 2021

   Abs

   Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model’s decision. But how useful are they for an end-user towards accomplishing a given task? In this vision paper, we argue the need for a benchmark to facilitate evaluations of the utility of post-hoc explanation methods. As a first step to this end, we enumerate desirable properties that such a benchmark should possess for the task of debugging text classifiers. Additionally, we highlight that such a benchmark facilitates not only assessing the effectiveness of explanations but also their efficiency.

2020

1. Multimodal analytics for real-world news using measures of cross-modal entity consistency

   Müller-Budack, Eric, Theiner, Jonas, Diering, Sebastian,  Idahl, Maximilian, and Ewerth, Ralph

   In Proceedings of the 2020 International Conference on Multimedia Retrieval Jun 2020
2. Debiasing Word Embeddings from Sentiment Associations in Names

   Hube, Christoph,  Idahl, Maximilian, and Fetahu, Besnik

   In Proceedings of the 13th International Conference on Web Search and Data Mining Jun 2020

   Abs

   Word embeddings, trained through models like skip-gram, have shown to be prone to capturing the biases from the training corpus, e.g. gender bias. Such biases are unwanted as they spill in downstream tasks, thus, leading to discriminatory behavior.In this work, we address the problem of prior sentiment associated with names in word embeddings where for a given name representation (e.g. "Smith"), a sentiment classifier will categorize it as either positive or negative. We propose DebiasEmb, a skip-gram based word embedding approach that, for a given oracle sentiment classification model, will debias the name representations, such that they cannot be associated with either positive or negative sentiment. Evaluation on standard word embedding benchmarks and a downstream analysis show that our approach is able to maintain a high quality of embeddings and at the same time mitigate sentiment bias in name embeddings.

3. Finding interpretable concept spaces in node embeddings using knowledge bases

   Idahl, Maximilian, Khosla, Megha, and Anand, Avishek

   In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I Jun 2020