Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals

Counterfactual explanations offer an intuitive and straightforward way to explain black-box models and offer algorithmic recourse to individuals. To address the need for plausible explanations, existing work has primarily relied on surrogate models to learn how the input data is distributed. This effectively reallocates the task of learning...

“It's the most fair thing to do, but it doesn't make any sense”: Perceptions of Mathematical Fairness Notions by Hiring Professionals

We explore the alignment of organizational representatives involved in hiring processes with five different, commonly proposed fairness notions. In a qualitative study with 17 organizational professionals, for each notion, we investigate their perception of understandability, fairness, potential to increase diversity, and practical applicability...

Annotation Practices in Societally Impactful Machine Learning Applications: What are Popular Recommender Systems Models Actually Trained On?

Machine Learning (ML) models influence all aspects of our lives. They also commonly are integrated in recommender systems, which facilitate users’ decision-making processes in various scenarios, such as e-commerce, social media, news and online learning. Training performed on large volumes of data is what ultimately drives such systems to...

Some Advice for Psychologists Who Want to Work With Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue...

Modeling, Recognizing, and Explaining Apparent Personality from Videos

Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our...

De (On)mogelijkheden van machine learning voor het verminderen van bias en discriminatie bij personeelsbeslissingen

In this article, we describe the implementation of algorithms based on machine learning for personnel selection procedures and how this data-driven approach corresponds to and differentiates from classical psychological assessment. We discuss if, and in what way, bias and discrimination occur when using algorithms based on machine learning...

One deep music representation to rule them all? A comparative analysis of different representation learning strategies

Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach...

Audiovisual Analysis of Music Performances: Overview of an Emerging Field

In the physical sciences and engineering domains, music has traditionally been considered an acoustic phenomenon. From a perceptual viewpoint, music is naturally associated with hearing, i.e., the audio modality. Moreover, for a long time, the majority of music recordings were distributed through audio-only media, such as vinyl records,...

Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings

Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time,...

Detecting Socially Significant Music Events using Temporally Noisy Labels

In this paper, we focus on event detection over the timeline of a music track. Such technology is motivated by the need for innovative applications such as searching, non-linearaccess and recommendation. Event detection over the timeline requires time-code level labels in order to train machine learning dels. We use timed comments from...

On detecting the playing/non-playing activity of musicians in symphonic music videos

Information on whether a musician in a large symphonic orchestra plays her instrument at a given time stamp or not is valuable for a wide variety of applications aiming at mimicking and enriching the classical music concert experience on modern multimedia platforms. In this work, we propose a novel method for generating playing/non-playing...

Using social media mining for estimating theory of planned behaviour parameters

In this position paper we present the scenario of making interventions for increasing the classical music concert-going behaviour of end users. Within the FP7 Phenicx project we are developing a personalized persuasive system that attempts at changing the concert-going behaviour of users. The system is based on the theory of planned behaviour...

MusiClef 2013: Soundtrack selection for commercials

MusiClef was one of the "brave new tasks" at MediaEval 2013 with a multimodal approach that combined music, video and textual information in order to evaluate systems that recommend a music soundtrack given the video of a commercial and the information on the product to be advertised.

Brave new task: MusiClef multimodal music tagging

In this paper, we give an overview of the MusiClef Brave New Task at MediaEval 2012. After introducing the background of the task and related initiatives in the music information retrieval domain, we give an overview of the actual task and baseline results obtained for a reference implementation by the task organizers.

Energy Transduction by Anaerobic Ferric Iron Respiration in Thiobacillus-Ferrooxidans

Energy Transduction by Anaerobic Ferric Iron Respiration in Thiobacillus ferrooxidans