125 lines
5.4 KiB
TeX
125 lines
5.4 KiB
TeX
\documentclass[runningheads]{llncs}
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\usepackage{graphicx}
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\usepackage[backend=biber,style=numeric]{biblatex}
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\usepackage{hyperref}
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\hypersetup{
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colorlinks=true,
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linkcolor=black,
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urlcolor=blue,
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citecolor=black
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}
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\addbibresource{trustworthy-ai.bib}
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\begin{document}
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\title{Trustworthy Artificial Intelligence}
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\author{Tobias Eidelpes}
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\authorrunning{T. Eidelpes}
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\institute{Technische Universität Wien, Karlsplatz 13, 1040 Wien, Austria
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\email{e1527193@student.tuwien.ac.at}}
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\maketitle
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\begin{abstract}
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The abstract should briefly summarize the contents of the paper in
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150--250 words.
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\keywords{Artificial Intelligence, Trustworthiness, Social Computing}
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\end{abstract}
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\section{Introduction}
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\label{sec:introduction}
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The use of artificial intelligence (AI) in computing has seen an unprecedented
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rise over the last few years. From humble beginnings as a tool to aid humans in
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decision making to advanced use cases where human interaction is avoided as much
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as possible, AI has transformed the way we live our lives today. The
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transformative capabilities of AI are not just felt in the area of computer
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science, but have bled into a diverse set of other disciplines such as biology,
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chemistry, mathematics and economics. For the purposes of this work, AIs are
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machines that can learn, take decision autonomously and interact with the
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environment~\cite{russell_artificial_2021}.
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While the possibilities of AI are seemingly endless, the public is slowly but
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steadily learning about its limitations. These limitations manifest themselves
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in areas such as autonomous driving and medicine, for example. These are fields
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where AI can have a direct—potentially life-changing—impact on people's lives. A
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self-driving car operates on roads where accidents can happen at any time.
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Decisions made by the car before, during and after the accident can result in
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severe consequences for all participants. In medicine, AIs are increasingly used
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to drive human decision-making. The more critical the proper use and functioning
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of AI is, the more trust in its architecture and results is required. Trust,
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however, is not easily defined, especially in relation to artificial
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intelligence.
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This work will explore the following question: \emph{Can artificial intelligence
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be trustworthy, and if so, how?} To be able to discuss this question, trust has
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to be defined and dissected into its constituent components.
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Chapter~\ref{sec:modeling-trust} analyzes trust and molds the gained insights
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into a framework suitable for interactions between humans and artifical
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intelligence. Chapter~\ref{sec:taxonomy} approaches trustworthiness in
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artificial intelligence from a computing perspective. There are various ways to
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make AIs more \emph{trustworthy} through the use of technical means. This
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chapter seeks to discuss and summarize important methods and approaches.
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Chapter~\ref{sec:social-computing} discusses combining humans and artificial
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intelligence into one coherent system which is capable of achieving more than
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either of its parts on their own.
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\section{Trust}
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\label{sec:modeling-trust}
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In order to be able to define the requirements and goals of \emph{trustworthy
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AI}, it is important to know what trust is and how we humans establish trust
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with someone or something. This section therefore defines and explores different
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forms of trust.
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\subsection{Defining Trust}
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Commonly, \emph{trusting someone} means to have confident in another person's
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ability to do certain things. This can mean that we trust someone to speak the
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truth to us or that a person is competently doing the things that we
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\emph{entrust} them to do. We trust the person delivering the mail that they do
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so on time and without mail getting lost on the way to our doors. We trust
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people knowledgeable in a certain field such as medicine to be able to advise us
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when we need medical advice. Trusting in these contexts means to cede control
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over a particular aspect of our lives to someone else. We do so in expectation
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that the trustee does not violate our \emph{social agreement} by acting against
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our interests. Often times we are not able to confirm that the trustee has
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indeed done his/her job. Sometimes we will only find out later that what was
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in fact done did not happen in line with our own interests. Trust is therefore
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also always a function of time. Previously entrusted people can—depending on
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their track record—either continue to be trusted or lose trust.
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We do not only trust certain people to act on our behalf, we can also place
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trust in things rather than people. Every technical device or gadget receives
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our trust to some extent, because we expect it to do the things we expect it to
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do. This relationship encompasses \emph{dumb} devices such as vacuum cleaners
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and refrigerators, as well as seemingly \emph{intelligent} systems such as
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algorithms performing medical diagnoses. Artificial intelligence systems belong
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to the latter category when they are functioning well, but can easily slip into
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the former in the case of a poorly trained machine learning algorithm that
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simply classifies pictures of dogs and cats always as dogs, for example.
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\textcite{ferrario_ai_2020}
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\section{Taxonomy for Trustworthy AI}
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\label{sec:taxonomy}
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\section{Social Computing}
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\label{sec:social-computing}
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\section{Conclusion}
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\label{sec:conclusion}
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\printbibliography
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\end{document}
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