  The need for interpretable and accountable intelligent systems grows along
with the prevalence of artificial intelligence applications used in everyday
life. Explainable intelligent systems are designed to self-explain the
reasoning behind system decisions and predictions, and researchers from
different disciplines work together to define, design, and evaluate
interpretable systems. However, scholars from different disciplines focus on
different objectives and fairly independent topics of interpretable machine
learning research, which poses challenges for identifying appropriate design
and evaluation methodology and consolidating knowledge across efforts. To this
end, this paper presents a survey and framework intended to share knowledge and
experiences of XAI design and evaluation methods across multiple disciplines.
Aiming to support diverse design goals and evaluation methods in XAI research,
after a thorough review of XAI related papers in the fields of machine
learning, visualization, and human-computer interaction, we present a
categorization of interpretable machine learning design goals and evaluation
methods to show a mapping between design goals for different XAI user groups
and their evaluation methods. From our findings, we develop a framework with
step-by-step design guidelines paired with evaluation methods to close the
iterative design and evaluation cycles in multidisciplinary XAI teams. Further,
we provide summarized ready-to-use tables of evaluation methods and
recommendations for different goals in XAI research.
