  As the role of algorithmic systems and processes increases in society, so
does the risk of bias, which can result in discrimination against individuals
and social groups. Research on algorithmic bias has exploded in recent years,
highlighting both the problems of bias, and the potential solutions, in terms
of algorithmic transparency (AT). Transparency is important for facilitating
fairness management as well as explainability in algorithms; however, the
concept of diversity, and its relationship to bias and transparency, has been
largely left out of the discussion. We reflect on the relationship between
diversity and bias, arguing that diversity drives the need for transparency.
Using a perspective-taking lens, which takes diversity as a given, we propose a
conceptual framework to characterize the problem and solution spaces of AT, to
aid its application in algorithmic systems. Example cases from three research
domains are described using our framework.
