Negotiating task allocation in a team with the help of a negotiator agent

This research experiment aimed to investigate the level of trust placed in an AI negotiation assistant paired with a truthful explanation of their negotiation strategy versus an opposite explanation within the Pocket Negotiator platform. A between-user study involving 30 participants was conducted to assess participants’ trust perceptions based...

Evaluating Feature Attribution Methods: an Usecase on a Neural Fact-checking Model

In today's society, claims are everywhere, in the online and offline world. Fact-checking models can check these claims and predict if a claim is true or false, but how can these models be checked? Post-hoc XAI feature attribution methods can be used for this. These methods give scores indicating the influence of the individual tokens on the...

Explainable Artificial Intelligence (XAI) Techniques - A Review and Case Study

The significant progress of Artificial Intelligence (AI) and Machine Learning (ML) techniques such as Deep Learning (DL) has seen success in their adoption in resolving a variety of problems. However, this success has been accompanied by increasing model complexity resulting in a lack of transparency and trustworthiness. Explainable Artificial...

Investigation of the evaluation techniques and tools used for model-specific XAI models

The spread of AI techniques has lead to its presence in critical situations, with increasing performance that can compromise on its understanding. Users with no prior AI knowledge rely on these techniques such as doctors or recruiters with a need for transparency and comprehensibility of the mechanisms. The advent of Explainable Artificial...

The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making

Governments look at explainable artificial intelligence's (XAI) potential to tackle the criticisms of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a solution for automated decisions, the wicked nature of the challenges governments face complicates the use of XAI. Wickedness means that the facts that define...

Determine and explain confidence in predicting violations on inland ships in the Netherlands

For real-world problems even the most complex machine learning models can only achieve a certain accuracy. This makes it important to understand why a specific prediction is made. Explanations can provide human decision support by allowing human experts to assess the reasoning of the model as well as the correctness. Specifically, in this thesis...

Will Algorithms Blind People? The Effect of Explainable AI and Decision-Makers’ Experience on AI-supported Decision-Making in Government

Computational artificial intelligence (AI) algorithms are increasingly used to support decision making by governments. Yet algorithms often remain opaque to the decision makers and devoid of clear explanations for the decisions made. In this study, we used an experimental approach to compare decision making in three situations: humans making...