How truthful explanation of a conceder negotiation agent affects human trust, compared to a neutral one

Trust in negotiation agents plays a crucial role in their adoption and utilization. However, there is not enough research on what factors influence it. This paper aims to investigate how different explanations of a negotiation agent’s strategy affect human trust and decision-making. Specifically, it compares the effects of a truthful explanation...

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...

Model-Agnostic XAI Models: Benefits, Limitations and Research Directions

The ever increasing presence of Machine Learning (ML) algorithms and Artificial Intelligence (AI) agents in safety-critical and sensitive fields over the past few years has spurred massive amounts of research in Explainable Artificial Intelligence (XAI) techniques (models). This new frontier of AI research aims to resolve some of the fundamental...

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...

Tailoring User-Aware Agent Explanations to Properly Align Human Trust

Aligning human trust to correspond with an agent's trustworthiness is an essential collaborative element within Human-Agent Teaming (HAT). Misalignment of trust could cause sub-optimal usage of the agent. Trust can be influenced by providing explanations which clarify the agent's actions. However, research often approaches explanations...

Interpretability and performance of surrogate decision trees produced by Viper

Machine learning models are being used extensively in many high impact scenarios. Many of these models are ‘black boxes’, which are almost impossible to interpret. Successful implementations have been limited by this lack of interpretability. One approach to increasing interpretability is to use imitation learning to extract a more interpretable...