decision by the other. With the involvement of automated tools in our decision-
making process, the question of trust comes up time and again. Do we trust au-
tomated tools to make decisions that embody our values? Do we trust automated tools
to account for our preferences in an objective manner? Do we trust automated tools to
make decisions in a way that would be the most beneficial to us? We trust other humans
to assist our decisions when they have demonstrated, through intent and action,
that they share similar interests as us. Can we place the same trust in automated tools
that we didn’t develop and in algorithms that we didn’t design which are, nevertheless,
parts of our daily lives? This dissertation explores this question by investigating the
decisions made by Artificial Intelligence (AI) and Machine Learning (ML) tools
through the perspectives of users and stakeholders. I demonstrate how flawed al-
gorithmic mechanisms can lead to harmful automated decisions and design meth-
ods to counter algorithmic harms, whenever possible, through a judicious process
by which stakeholders are a part of the algorithmic decision-making process.
The availability of large datasets, massive computing power, and progress in
machine learning methods has led to a surge in the use of automated decision-
making frameworks in a variety of domains. Technological and monetary invest-
ments have facilitated significant improvements in the performance of algorithmic
tools and a number of applications of these tools lie in fields that make decisions
affecting humans and society in general. They are employed in numerous criti-
cal applications, including healthcare [152,286,302], advertising [245,257], online
search and recommendation feeds [40,180], lending [231,324], content modera-
tion [80,220,313], recruitment [106], criminal risk assessment [1,89,128,233], and
policing [134,149]. All these applications involve actively processing information
related to people and making decisions that affect society at an individual and in-
stitutional level. The impact of such automated frameworks in shaping our current
and future socio-technical landscape cannot be understated.
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