Artificial intelligence & Information retrieval

Category: Events (Page 1 of 2)

FAT* Conference on Fairness, Accountability, and Transparency

FAT* is a multi-disciplinary conference that brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems.

Artificial intelligence, automation, and machine learning are being adopted in a growing number of contexts. Fueled by big data, these systems filter, sort, score, recommend, personalize, and otherwise shape human experiences of socio-technical systems. Although these systems bring myriad benefits, they also contain inherent risks, such as codifying and entrenching biases; reducing accountability and hindering due process; and increasing the information assymmetry between data producers and data holders.

FAT* is an annual conference dedicating to bringing together a diverse community to investigate and tackle issues in this emerging area. FAT* builds upon several years of successful workshops on the topics of fairness, accountability, transparency, ethics, and interpretability in machine learning, recommender systems, the web, and other technical disciplines.

The inaugural 2018 FAT* Conference will be held February 23 and 24th, 2018 at New York University, NYC. Details will be announced at

Neural Networks for Information Retrieval tutorial at SIGIR 2017

Title: Neural Networks for Information Retrieval (NN4IR)

Description: Machine learning plays an important role in many aspects of modern IR systems, and deep learning is applied to all of those. The fast pace of modern-day research into deep learning has given rise to many different approaches to many different IR problems. What are the underlying key technologies and what key insights into IR problems are they able to give us? This full-day tutorial gives a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research and our understanding of IR problems. Additionally, we peek into the future by examining recently introduced paradigms as well as current challenges. Expect to learn about neural networks in semantic matching, ranking, user interaction, and response generation in a highly interactive tutorial.

Presenters: Tom Kenter, Alexey Borisov, Christophe van Gysel, Mostafa Dehghani, Maarten de Rijke, Bhaskar Mitra

Where: SIGIR 2017, Tokyo

When: August 7, 2017

FAT/WEB: Workshop on Fairness, Accountability, and Transparency on the Web

Recent academic and journalistic reviews of online web services have revealed that many systems exhibit subtle biases reflecting historic discrimination. Examples include racial and gender bias in search advertising, image recognition services, sharing economy mechanisms, pricing, and web-based delivery. The list of production systems exhibiting biases continues to grow and may be endemic to the way models are trained and the data used.

At the same time, concerns about user autonomy and fairness have been raised in the context of web-based experimentation such as A/B testing or explore/exploit algorithms. Given the ubiquity of this practice and increasing adoption in potentially-sensitive domains (e.g. health, employment), user consent and risk will become fundamental to the practice.

Finally, understanding the reasons behind predictions and outcomes of web services is important in optimizing a system and in building trust with users. However, it also has legal and ethical implications when the algorithm has an unintended or undesirable impact along social boundaries.
The objective of this full day workshop is to study and discuss the problems and solutions with algorithmic fairness, accountability, and transparency of models in the context of web-based services.


CIKM 2016 Workshop on Data-Driven Talen Acquisition

The Workshop on Data-Driven Talent Acquisition (DDTA’16) will be co-located with CIKM 2016, held in Indianapolis, USA, on October 28, 2016.

Expertise search is a well-established field in information retrieval. In recent years, the increasing availability of data enables accumulation of evidence of talent and expertise from a wide range of domains. The availability of big data significantly benefits employers and recruiters. By analyzing the massive amounts of structured and unstructured data, organizations may be able to find the exact skillsets and talent they need to grow their business. The aim of this workshop is to provide a forum for industry and academia to discuss the recent progress in talent search and management, and how the use of big data and data-driven decision making can advance talent acquisition and human resource management.

Important Dates

  • Submission deadline: September 8, 2016
  • Acceptance notification: September 22, 2016
  • Workshop date: October 28, 2016

Further details

Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval

The deadline for submissions to Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval is less than three weeks away. Neu-IR will be a highly interactive full day workshop, featuring a mix of presentation and interaction formats. We welcome application papers, papers that address fundamental modeling challenges, and best practices papers. Please see for details and submission instructions.

SIGIR 2016 tutorial on online learning to rank

Artem Grotov and I will be teaching a half-day tutorial on online learning to rank for information retrieval at SIGIR 2016.

During the past 10–15 years offline learning to rank has had a tremendous influence on information retrieval, both scientifically and in practice. Recently, as the limitations of offline learning to rank for information retrieval have become apparent, there is increased attention for online learning to rank methods for information retrieval in the community. Such methods learn from user interactions rather than from a set of labeled data that is fully available for training up front.

Today’s search engines have developed into complex systems that combines hundreds of ranking criteria with the aim of producing the optimal result list in response to users’ queries. For automatically tuning optimal combinations of large numbers of ranking criteria, learning to rank (LTR) has proved invaluable. For a given query, each document is represented by a feature vector. The features may be query dependent, document dependent or capture the relationship between the query and documents. The task of the learner is to find a model that combines these features such that, when this model is used to produce a ranking for an unseen query, user satisfaction is maximized.

Traditionally, learning to rank algorithms are trained in batch mode, on a complete dataset of query and document pairs with their associated manually created relevance labels. This setting has a number of disadvantages and is impractical in many cases. First, creating such datasets is expensive and therefore infeasible for smaller search engines, such as small web-store search engines. Second, it may be impossible for experts to annotate documents, as in the case of personalized search. Third, the relevance of documents to queries can change over time, like in a news search engine.

Online learning to rank addresses all of these issues by incrementally learning from user feedback in real time. Online learning is closely related to active learning, incremental learning, and counterfactual learning. However, online learning is more difficult because the agent has to balance exploration and exploitation: actions with unknown performance have to be explored to learn better solutions.

There is a growing body of established methods for online learning to rank for information retrieval. The time is right to organize and present this material to a broad audience of interested information retrieval researchers, whether junior or senior, whether academic or industrial. The online learning to rank methods available today have been proposed by different communities, in machine learning and information retrieval. A key aim of the tutorial is to bring these together and offer a unified perspective. To achieve this we illustrate the core and state of the art methods in online learning to rank, their theoretical foundations and real-world applications, as well as existing online learning algorithms that have not been used by information retrieval community so far.

Neu-IR: SIGIR 2016 Workshop on Neural Information Retrieval

SIGIR 2016 will feature a workshop on Neural Information Retrieval. In recent years, deep neural networks have yielded significant performance improvements in application areas such as speech recognition and computer vision. They have also had an impact in natural language applications such as machine translation, image caption generation and conversational agents. Our focus with the Neu-IR workshop is on the applicability of deep neural networks to information retrieval. There are two complementary dimensions to this: one involves demonstrating performance improvements on public or private information retrieval datasets, the other concerns thinking about deep neural network architectures and what they tell us about information retrieval problems. Neu-IR (pronounced “new IR”) will be a highly interactive full day workshop that focuses on advances and challenges along both dimensions.

See, where further information will be shared.

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