Assessing the Transparency and Explainability of AI Algorithms in
Planning and Scheduling tools: A Review of the Literature
Authors: Morandini Sofia , Fraboni Federico , Balatti Enzo , Hackmann Aranka , Brendel Hannah , Puzzo Gabriele , Volpi Lucia , Giusino Davide , De Angelis Marco , Pietrantoni Luca 
Published: July 2023

As AI technologies enter our working lives at an ever-increasing pace,
there is a greater need for AI systems to work synergistically with humans
at work. One critical requirement for such synergistic human-AI interaction
is that the AI systems' behavior be explainable to the humans in the loop.
The performance of decision-making by artificial intelligence has exceeded
the capability of human beings in many specific domains. In the AI
decision-making process, the inherent black-box algorithms and opaque system
information lead to highly correct but incomprehensible results. The need
for explainability of intelligent decision-making is becoming urgent and a
transparent process can strengthen trust between humans and machines. The As
AI technologies enter our working lives at an ever-increasing pace, there is
a greater need for AI systems to work synergistically with humans at work.
One critical requirement for such synergistic human-AI interaction is that
the AI systems' behavior be explainable to the humans in the loop. The
performance of decision-making by artificial intelligence has exceeded the
capability of human beings in many specific domains. In the AI
decision-making process, the inherent black-box algorithms and opaque system
information lead to highly correct but incomprehensible results. The need
for explainability of intelligent decision-making is becoming urgent and a
transparent process can strengthen trust between humans and machines. The
TUPLES project, a three-year Horizon Europe R&I project, aims to bridge
this gap by developing AI-based planning and scheduling (P&S) tools
using a comprehensive, human-centered approach. TUPLES leverages data-driven
and knowledge-based symbolic AI methods to provide scalable, transparent,
robust, and secure algorithmic planning and scheduling systems solutions. It
adopts a use-case-oriented methodology to ensure practical applicability.
Use cases are chosen based on input from industry experts, cutting-edge
advances, and manageable risks (e.g., manufacturing, aviation, waste
management). The EU guidelines for Trustworthy Artificial Intelligence
highlight key requirements such as human agency and oversight, transparency,
fairness, societal well-being, and accountability. The Assessment List for
Trustworthy Artificial Intelligence (ALTAI) is a practical self-assessment
tool for businesses and organizations to evaluate their AI systems. Existing
AI-based P&S tools only partially meet these criteria, so innovative AI
development approaches are necessary. We conducted a literature review to
explore current research on AI algorithms' transparency and explainability
in P&S, aiming to identify metrics and recommendations. The findings
highlighted the importance of Explainable AI (XAI) in AI design and
implementation. XAI addresses the black box problem by making AI systems
explainable, meaningful, and accurate. It uses pre-modeling, in-modeling,
and post-modeling explainability techniques, relying on psychological
concepts of human explanation and interpretation for a human-centered
approach. The review pinpoints specific XAI methods and offered evidence to
guide the selection of XAI tools in planning and scheduling.