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Andreas Vlachidis

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2 papers

2 author rows

AAAI Conference 2025 Conference Paper

Measuring Error Alignment for Decision-Making Systems

Binxia Xu
Antonis Bikakis
Daniel F.O. Onah
Andreas Vlachidis
Luke Dickens

Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine how well they align with human values. We argue that good measures of the information processing similarities between AI and humans, may be able to achieve these same ends. While Representational alignment (RA) approaches measure similarity between the internal states of two systems, the associated data can be expensive and difficult to collect for human systems. In contrast, Behavioural alignment (BA) comparisons are cheaper and easier, but questions remain as to their sensitivity and reliability. We propose two new behavioural alignment metrics misclassification agreement which measures the similarity between the errors of two systems on the same instances, and class-level error similarity which measures the similarity between the error distributions of two systems. We show that our metrics correlate well with RA metrics, and provide complementary information to another BA metric, within a range of domains, and set the scene for a new approach to value alignment.

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NeSy Conference 2024 Conference Paper

Context Helps: Integrating Context Information with Videos in a Graph-Based HAR Framework

Binxia Xu
Antonis Bikakis
Daniel F. O. Onah
Andreas Vlachidis
Luke Dickens

Abstract Human Activity Recognition (HAR) from videos is a challenging, data intensive task. There have been significant strides in recent years, but even state-of-the-art (SoTA) models rely heavily on domain specific supervised fine-tuning of visual features, and even with this data- and compute-intensive fine-tuning, overall performance can still be limited. We argue that the next generation of HAR models could benefit from explicit neuro-symbolic mechanisms in order to flexibly exploit rich contextual information available in, and for, videos. With a view to this, we propose a Human Activity Recognition with Context Prompt (HARCP) task to investigate the value of contextual information for video-based HAR. We also present a neuro-symbolic graph neural network-based framework that integrates zero-shot object localisation to address the HARCP task. This captures the human activity as a sequence of graph-based scene representations relating parts of the human body to key objects, supporting the targeted injection of external contextual knowledge in symbolic form. We evaluate existing HAR baselines alongside our graph-based methods to demonstrate the advantage of being able to accommodate this additional channel of information. Our evaluations show that not only does context information from key objects boost accuracy beyond that provided by SoTA HAR models alone, there is also a greater semantic similarity between our model’s errors and the target class. We argue that this represents an improved model alignment with human-like errors and quantify this with a novel measure we call Semantic Prediction Dispersion.

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