  A major concern of Machine Learning (ML) models is their opacity. They are
deployed in an increasing number of applications where they often operate as
black boxes that do not provide explanations for their predictions. Among
others, the potential harms associated with the lack of understanding of the
models' rationales include privacy violations, adversarial manipulations, and
unfair discrimination. As a result, the accountability and transparency of ML
models have been posed as critical desiderata by works in policy and law,
philosophy, and computer science.
  In computer science, the decision-making process of ML models has been
studied by developing accountability and transparency methods. Accountability
methods, such as adversarial attacks and diagnostic datasets, expose
vulnerabilities of ML models that could lead to malicious manipulations or
systematic faults in their predictions. Transparency methods explain the
rationales behind models' predictions gaining the trust of relevant
stakeholders and potentially uncovering mistakes and unfairness in models'
decisions. To this end, transparency methods have to meet accountability
requirements as well, e.g., being robust and faithful to the underlying
rationales of a model.
  This thesis presents my research that expands our collective knowledge in the
areas of accountability and transparency of ML models developed for complex
reasoning tasks over text.
