English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Artificial intelligence and machine learning algorithms have becomeubiquitous. Although they offer a wide range of benefits, their adoption indecision-critical fields is limited by their lack of interpretability,particularly with textual data. Moreover, with more data available than everbefore, it has become increasingly important to explain automated predictions.  Generally, users find it difficult to understand the underlying computationalprocesses and interact with the models, especially when the models fail togenerate the outcomes or explanations, or both, correctly. This problemhighlights the growing need for users to better understand the models' innerworkings and gain control over their actions. This dissertation focuses on twofundamental challenges of addressing this need. The first involves explanationgeneration: inferring high-quality explanations from text documents in ascalable and data-driven manner. The second challenge consists in makingexplanations actionable, and we refer to it as critiquing. This dissertationexamines two important applications in natural language processing andrecommendation tasks.  Overall, we demonstrate that interpretability does not come at the cost ofreduced performance in two consequential applications. Our framework isapplicable to other fields as well. This dissertation presents an effectivemeans of closing the gap between promise and practice in artificialintelligence.

Textual Explanations and Critiques in Recommendation Systems