Towards Fair and Explainable AI using a Human-Centered AI Approach
Bhavya Ghai
The rise of machine learning (ML) is accompanied by several high-profile
cases that have stressed the need for fairness, accountability, explainability
and trust in ML systems. The existing literature has largely focused on fully
automated ML approaches that try to optimize for some performance metric.
However, human-centric measures like fairness, trust, explainability, etc. are
subjective in nature, context-dependent, and might not correlate with
conventional performance metrics. To deal with these challenges, we explore a
human-centered AI approach that empowers people by providing more transparency
and human control.
  In this dissertation, we present 5 research projects that aim to enhance
explainability and fairness in classification systems and word embeddings. The
first project explores the utility/downsides of introducing local model
explanations as interfaces for machine teachers (crowd workers). Our study
found that adding explanations supports trust calibration for the resulting ML
model and enables rich forms of teaching feedback. The second project presents
D-BIAS, a causality-based human-in-the-loop visual tool for identifying and
mitigating social biases in tabular datasets. Apart from fairness, we found
that our tool also enhances trust and accountability. The third project
presents WordBias, a visual interactive tool that helps audit pre-trained
static word embeddings for biases against groups, such as females, or
subgroups, such as Black Muslim females. The fourth project presents DramatVis
Personae, a visual analytics tool that helps identify social biases in creative
writing. Finally, the last project presents an empirical study aimed at
understanding the cumulative impact of multiple fairness-enhancing
interventions at different stages of the ML pipeline on fairness, utility and
different population groups. We conclude by discussing some of the future
directions.