Hi everybody, I am Francesco Sovrano and I am going to present you our paper: From Philosophy
to Interfaces, an Explanatory Method and a Tool Inspired by Achinstein’s Theory of
Explanation.
Nowadays Algorithmic Decision Making is changing industry.
This is why people and Countries (EU, California, etc..) begun to be concerned about the impact
that may have on everyone’s lives.
This is one of the reasons that gave birth to the so-called Right to Explanation “recently”
introduced in the EU legislation, and further explored by the High-Level Expert Group on
Artificial Intelligence (AI-HLEG) established in 2018 by the EU Commission.
Some of the given guidelines (by the AI-HLEG) point to the need for user-centrality in explanations.
The fact that explanations are better conceived when they are pragmatic is also shared by
all the most recent philosophical theories of explanation.
Explanation in philosophy has been conceived within the following 5 traditions:
Causal Realism Constructive Empiricism
Ordinary Language Philosophy Cognitive Science
Naturalism and Scientific Realism The first one is not pragmatic, the others
are pragmatic.
Apparently a lot of literature on user-centred XAI is now steering towards the interpretation
of explanations given by Cognitive Science.
In our paper, we propose a new approach to explanations in AI that is mainly inspired
by Ordinary Language Philosophy instead of Cognitive Science.
Achinstein’s model is probably the most prominent example of Theory of Explanations
within the scope of Ordinary Language Philosophy.
According to this model, explaining is an illocutionary act coming from a clear intention
of producing new understandings in an explainee by providing a correct content-giving answer
to a question.
But, what is illocution, precisely?
Our hypothesis is that illocution (in explanations) is equivalent to the act of pertinently answering
to archetypal questions (e.g.
Why?
What for?
How?
When?
etc..).
In fact, explaining is not just answering a given question in a punctual way (that would
simply be answering) but it is also answering all the other implicit (archetypal) questions
defined by: the explainee’s background knowledge, the objectives of the explanatory process
and the given context.
In other terms, the more archetypal answers about the explanandum’s aspects are covered
by the act of explaining, the more likely the resulting explanation is going to meet
the explainee’s objectives.
So, how can we measure the pertinence of an answer?
The likelihood of pertinence to an answer can be quantitatively estimated with strong-enough
statistical evidence; for example collected in deep language models, as suggested by existing
literature.
But we are defining pertinence by means of statistical tools, where is user-centrality?
A statistical definition of pertinence does not preclude a pragmatic iterative explanatory
process that is locally non-pragmatic but globally pragmatic.
In other words: an explanans at step t is non-pragmatically built by organising knowledge
according to a statistical pertinence, but the sequence of explanans is pragmatically
chosen by the users.
So, we can design an explanatory process as an iterative process where user-centrality
is in the way answers are visited, explored, expanded (and eventually asked).
So far we discussed about theory, how can we build this iterative process in practice?
To do so we need an algorithm for the extraction and organisation of Explanatory Spaces (or
ES) out of explainable documents.
An ES is defined as the set of all possible explanations (on an explainable explanandum)
reachable by a user, through an explanatory process, starting from an initial explanans,
via a pre-defined set of actions.
Therefore an ES is a graph of interconnected bits of explanation that are about different
aspects of the explanandum.
To every aspect is associated an an information cluster explaining that aspect.
An explanatory process over an ES can be defined by the choice of appropriate heuristics for
visiting and organising the space.
To this end, we identified the ARS heuristics, that are: Abstraction, Relevance and Simplicity.
Abstraction is for selecting and organising the different aspects of the explanandum (that
are the external nodes of the ES).
For instance, the core aspects to explain might be the different concepts or entities
mentioned in the explanandum.
Relevance is for organising the information about the aspects (the internal nodes of the
ES), according to the archetypal questions.
Simplicity is for filtering or grouping the edges of the graph.
For instance, some pieces-of-information can be hidden, because redundant, or some others
may be grouped together to simplify the visualisation.
In order to implement these ARS heuristics, we created a pipeline for:
Extracting a knowledge graph of aspects out of natural language documents.
Building information clusters for every aspect, according to the identified archetypal questions.
Presenting information clusters through a simplified hierarchy of expandable summaries.
We put this pipeline at work.
If our hypothesis is correct, through the identification of a minimal set of archetypal
questions, it is possible to obtain a generator of explanations that is good enough to significantly
ease the acquisition of knowledge, thus resulting in a user-centred explanatory tool that is
more effective than its non-pragmatic counterpart (on the same explanandum).
To verify this hypothesis, we designed a user study on a baseline credit approval system.
The baseline credit approval system we are considering has been designed by IBM and it
consists in a XAI used to generate a static explanation about the system’s decision.
This explanation is meant to explain to a customer what to change in order to get a
loan application accepted.
On the left you may see a screenshot of such explanation.
For the sake of the experiment we created an interactive version of that baseline that
is using our pipeline for generating explanatory overviews of the explanandum’s aspects.
This interactive version is obtained by means of a javascript module that automatically
annotates the static explanations, by using the extracted knowledge graph.
The documentation we used for building the knowledge graph was taken from roughly 60
online web pages.
As result, the user can interactively explore the ES by clicking on annotations (the underlined
syntagms), opening an overview containing further annotations.
As we can see, on the image on the left, information is organised according to the ARS heuristics.
Aspects are explained through expandable archetypal answers summarising more punctual information.
For the user study we recruited more than 100 different participants on the online platform
Prolific.
The first half (Group 1) of the participants tested the baseline system, while the second
one (Group 2) tested our extension.
Participants were invited to answer two sets of questions: a SUS questionnaire and some
domain-specific questions.
The set of domain-specific questions was used to measure the effectiveness and efficiency
of the explanatory tools.
Importantly: More than 70% of these questions were designed so that the correct answers
were not immediately available in the initial explanans, forcing the users to visit multiple
nodes of the ES.
The participants of Group 1 were explicitly allowed to search the web for the correct
answers (that were available online on the websites used for the extraction of the knowledge
graph).
We chose to not make assumptions on the distribution of the collected data.
This choice excluded a lot of statistical tests.
So we performed a few one-sided Mann-Whitney U-Tests.
The results showed that: Effectiveness (that is the number of correct
answers) was significantly greater than the baseline with a p-value lower than 0.05 (p=0.036).
There was not enough statistical evidence for claiming anything on Satisfaction and
Efficiency.
We proposed an original and generic paradigmatic explanatory AI inspired to Achinstein’s
illocutionary theory of explanations, where most of literature usually prefers to focus
on Cognitive Science.
We proposed a way to concretely implement illocution as the act of pertinently answering
archetypal questions (e.g.
Why?
What for?
How?
When?
etc..).
We tested our theory inventing a new pipeline of AI algorithms and running a user study
on a realistic Credit Approval System, showing a statistically significant improvement in
effectiveness over the non-paradigmatic explanatory AI provided by IBM.