Explaining knock-on effects of bias mitigation

The increased use of machine learning models in many different areas of public life has led to concerns about biases and unfairnesses that the use of machine learning models can introduce.  In machine learning systems, bias-mitigation approaches aim to make outcomes...

Quality Assessment of Conversational Agents

In this poster I will present my work on quality assessment of chatbots (conversational agents, or CA). I have worked in 3 directions: (i) natural language understanding where I test the quality of chatbot models by introducing artificial faults (e.g., typos, accents)...

Expanding the Scope and Scale of Mutation Analysis​

Today’s software systems are complex, with many applications (e.g., Gmail, Facebook) composed of large numbers of software components interacting with users to form sophisticated applications. Developers use tests to check the quality of their applications – a process...
Summit 2023
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