From 9c31a1b6d5bd5841c8e398ea4844d3e19f11d831 Mon Sep 17 00:00:00 2001 From: NotXia <35894453+NotXia@users.noreply.github.com> Date: Sat, 8 Mar 2025 17:06:37 +0100 Subject: [PATCH] Add ethics3 human agency and oversight --- src/year2/ethics-in-ai/module3/ainotes.cls | 1 + src/year2/ethics-in-ai/module3/ethics3.tex | 13 ++ .../sections/_human_agency_oversight.tex | 141 ++++++++++++++++++ 3 files changed, 155 insertions(+) create mode 120000 src/year2/ethics-in-ai/module3/ainotes.cls create mode 100644 src/year2/ethics-in-ai/module3/ethics3.tex create mode 100644 src/year2/ethics-in-ai/module3/sections/_human_agency_oversight.tex diff --git a/src/year2/ethics-in-ai/module3/ainotes.cls b/src/year2/ethics-in-ai/module3/ainotes.cls new file mode 120000 index 0000000..4a953bf --- /dev/null +++ b/src/year2/ethics-in-ai/module3/ainotes.cls @@ -0,0 +1 @@ +../../../ainotes.cls \ No newline at end of file diff --git a/src/year2/ethics-in-ai/module3/ethics3.tex b/src/year2/ethics-in-ai/module3/ethics3.tex new file mode 100644 index 0000000..d54ad54 --- /dev/null +++ b/src/year2/ethics-in-ai/module3/ethics3.tex @@ -0,0 +1,13 @@ +\documentclass[11pt]{ainotes} + +\title{Ethics in Artificial Intelligence\\(Module 3)} +\date{2024 -- 2025} +\def\lastupdate{{PLACEHOLDER-LAST-UPDATE}} +\def\giturl{{PLACEHOLDER-GIT-URL}} + +\begin{document} + + \makenotesfront + \include{./sections/_human_agency_oversight.tex} + +\end{document} \ No newline at end of file diff --git a/src/year2/ethics-in-ai/module3/sections/_human_agency_oversight.tex b/src/year2/ethics-in-ai/module3/sections/_human_agency_oversight.tex new file mode 100644 index 0000000..0b163cf --- /dev/null +++ b/src/year2/ethics-in-ai/module3/sections/_human_agency_oversight.tex @@ -0,0 +1,141 @@ +\chapter{Human agency and oversight} + + +\begin{description} + \item[AI act, article 14] \marginnote{AI act, article 14} + Article related to human oversight. It states that: + \begin{itemize} + \item Human centric AI is one of the key safeguarding principles to prevent risks. + \item AI systems must be designed and developed with appropriate interfaces to allow humans to oversee them. + \end{itemize} +\end{description} + + +\begin{description} + \item[Human agency] \marginnote{Human agency} + AI systems should empower human beings such that they can: + \begin{itemize} + \item Make informed decisions. + \item Foster their fundamental rights. + \end{itemize} + + This can be achieved with methods like: + \begin{itemize} + \item Human-centric approaches, + \item AI for social good, + \item Human computation, + \item Interactive machine learning. + \end{itemize} + + \item[Human oversight] \marginnote{Human oversight} + Oversight mechanisms to prevent manipulation, deception, conditioning from AI systems. + + Possible methods are: + \begin{itemize} + \item Human-in-the-loop, + \item Human-on-the-loop, + \item Human-in-command. + \end{itemize} + + \item[Human-centered AI framework] \marginnote{Human-centered AI framework} + Approach centered on high autonomy while keeping human control. +\end{description} + +\begin{remark} + Human agency and oversight happens at different levels: + \begin{descriptionlist} + \item[Development team] Responsible for the technical part. + \item[Organization] Decides who is in charge of accountability, validation, \dots + \item[External reviewers] (e.g., certification entities). + \end{descriptionlist} +\end{remark} + + +\section{Governance and methodology} + +\begin{description} + \item[Human-out-of-the-loop] \marginnote{Human-out-of-the-loop} + The environment is static and cannot integrate human knowledge. The AI system is a black-box that cannot be used in safety-critical settings. + + \item[Human-in-the-loop (HITL)] \marginnote{Human-in-the-loop (HITL)} + The environment is dynamic and can use expert knowledge. The AI system is explainable or transparent and suitable for safety-critical settings. + + In practice, the AI system stops and waits for human commands before making a decision. + + \item[Society-in-the-loop] \marginnote{Society-in-the-loop} + The society, with its conflicting interests and values, is taken into account. + + \item[Human-on-the-loop (HOTL)] \marginnote{Human-on-the-loop (HOTL)} + The AI system operates autonomously and the human can intervene if needed. +\end{description} + +\begin{remark} + Limitations of human-centric AI are: + \begin{itemize} + \item It does not scale well as human intervention is involved. + \item It is hard to evaluate its effectiveness. + \item Performance of the AI system might degrade. + \end{itemize} +\end{remark} + + + +\section{HITL state-of-the-art approaches} + + +\subsection{Active learning} + +\begin{description} + \item[Active learning] \marginnote{Active learning} + The system is in control of the learning process and the human acts as an oracle for labeling data. + + The learner can query, following some strategy, the human for the ground-truth of unlabeled data. A general algorithm works as follows: + \begin{enumerate} + \item Split the data into an initial (small) pool of labeled data and a pool with the remaining unlabeled ones. + \item The model selects an example(s) to be labeled by the oracle. + \item The model is trained on the available labeled data. + \item Repeat until a stop condition is met. + \end{enumerate} + + The selection strategy can be: + \begin{descriptionlist} + \item[Random] + \item[Uncertainty-based] Select examples classified with the least confidence according to some metric. + \item[Diversity-based] Select examples that are rare or representative according to some metric. + \end{descriptionlist} + + \begin{remark} + This approach is effective in settings with lots of unlabeled data and annotating all of it is expensive. + \end{remark} + + \begin{remark} + This approach is sensitive to the choice of the oracle. + \end{remark} +\end{description} + + +\subsection{Interactive machine learning} + +\begin{description} + \item[Interactive machine learning] \marginnote{Interactive machine learning} + Users interactively supply information that influences the learning process. + + \begin{remark} + Compared to active learning, with interactive machine learning it is the human that selects the learning data. + \end{remark} +\end{description} + + +\subsection{Machine teaching} + +\begin{description} + \item[Machine teaching] \marginnote{Machine teaching} + Human experts are completely in control of the learning process. There can be different types of teachers: + \begin{descriptionlist} + \item[Omniscient teacher] Complete access to the components of the learner (i.e., feature space, parameters, loss, optimization algorithm, \dots). + \item[Surrogate teacher] Access to the loss. + \item[Imitation teacher] The teacher uses a copy of the learner that it can query to create a surrogate model. + \item[Active teacher] The teacher queries the learner and evaluates it based on the output. + \item[Adaptive teacher] The teacher selects examples based on the current hypothesis of the learner. + \end{descriptionlist} +\end{description} \ No newline at end of file