22 citations · 71 across the 13 of their papers we have counts for
15 papers
Bias, diversity, and challenges to fairness in classification and automated text analysis. From libraries to AI and back
Bettina Berendt, Özgür Karadeniz, Sercan Kıyak +2
Libraries are increasingly relying on computational methods, including methods from Artificial Intelligence (AI). This increasing usage raises concerns about the risks of AI that a…
Domain Adaptive Decision Trees: Implications for Accuracy and Fairness
Jose M. Alvarez, Kristen M. Scott, Salvatore Ruggieri +1
In uses of pre-trained machine learning models, it is a known issue that the target population in which the model is being deployed may not have been reflected in the source popula…
How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification
Ewoenam Tokpo, Pieter Delobelle, Bettina Berendt +1
To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use o…
Political representation bias in DBpedia and Wikidata as a challenge for downstream processing
Ozgur Karadeniz, Bettina Berendt, Sercan Kiyak +2
Diversity Searcher is a tool originally developed to help analyse diversity in news media texts. It relies on a form of automated content analysis and thus rests on prior assumptio…
RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use
Pieter Delobelle, Thomas Winters, Bettina Berendt
Large transformer-based language models, e.g. BERT and GPT-3, outperform previous architectures on most natural language processing tasks. Such language models are first pre-traine…
FairDistillation: Mitigating Stereotyping in Language Models
Pieter Delobelle, Bettina Berendt
Large pre-trained language models are successfully being used in a variety of tasks, across many languages. With this ever-increasing usage, the risk of harmful side effects also r…
RobBERTje: a Distilled Dutch BERT Model
Pieter Delobelle, Thomas Winters, Bettina Berendt
Pre-trained large-scale language models such as BERT have gained a lot of attention thanks to their outstanding performance on a wide range of natural language tasks. However, due…
Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models
Pieter Delobelle, Ewoenam Kwaku Tokpo, Toon Calders +1
An increasing awareness of biased patterns in natural language processing resources, like BERT, has motivated many metrics to quantify `bias' and `fairness'. But comparing the resu…
Whistleblower protection in the digital age -- why 'anonymous' is not enough. From technology to a wider view of governance
Bettina Berendt, Stefan Schiffner
When technology enters applications and processes with a long tradition of controversial societal debate, multi-faceted new ethical and legal questions arise. This paper focusses o…
Measuring Shifts in Attitudes Towards COVID-19 Measures in Belgium Using Multilingual BERT
Kristen Scott, Pieter Delobelle, Bettina Berendt
We classify seven months' worth of Belgian COVID-related Tweets using multilingual BERT and relate them to their governments' COVID measures. We classify Tweets by their stated opi…
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Pieter Delobelle, Paul Temple, Gilles Perrouin +3
Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming u…
Bias in Data-driven AI Systems -- An Introductory Survey
Eirini Ntoutsi, Pavlos Fafalios, Ujwal Gadiraju +20
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and any…
RobBERT: a Dutch RoBERTa-based Language Model
Pieter Delobelle, Thomas Winters, Bettina Berendt
Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural…
Time to Take Emoji Seriously: They Vastly Improve Casual Conversational Models
Pieter Delobelle, Bettina Berendt
Graphical emoji are ubiquitous in modern-day online conversations. So is a single thumbs-up emoji able to signify an agreement, without any words. We argue that the current state-o…
AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing
Bettina Berendt
Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for "Good". This is part of a general drive towards infusing AI research and…