The document discusses the importance of generating explanations in intelligent systems, emphasizing their role in knowledge acquisition, meaning-making, and compliance with GDPR regulations. It outlines different types of explanations and their characteristics, including audiences and formats, while providing examples of how existing knowledge sources can be leveraged to create new explanations. The author advocates for cross-disciplinary collaboration and the reuse of data to enhance the effectiveness of explainable systems.

1. BUILDING INTELLIGENT SYSTEMS
(THAT CAN EXPLAIN)
Ilaria Tiddi
Faculty of Computer Science && Faculty of Behavioural Sciences
Vrije Universiteit Amsterdam
@IlaTiddi

2. DISCLAIMER
This is not a presentation on eXplainable AI (XAI)
...but rather on systems using data to making sense of other data

3. ● Why
● What
● Which
● How
● Examples
● Lessons learnt
GENERATING EXPLANATIONS

4. Why do we need (systems generating) explanations?
● to learn new knowledge
● to find meaning (reconciling contradictions in our knowledge)
● to socially interact (creating a shared meaning with the others)
● ...and because GDPR says so
Users have a “right to explanation”
for any decision made about them
EXPLANATIONS: WHY?

5. Different disciplines, common features [1]:
● Generation of coherence between old and new knowledge
● Same elements (theory, anterior, posterior, circumstances)
● Same processes (psychological , linguistic)
[1] Tiddi et al. (2015), An Ontology Design Pattern to Define Explanations, K-CAP2015.
Determinists Hempel&
Oppenheim
Weber&
Durkheim
Charles
Peirce
EXPLANATIONS: WHAT/1
V-IV BC
Plato&Aristotle
XVII AC 1948 19641903 2015
?

6. Explication =
Justification =
Explic-/Interpret-/Explainability =
EXPLANATIONS: WHAT/2
Explanation (⋍ Interpretation)
why a decision is good
the degree to which an observer
can understand the cause of a
decision

7. Which types?
● factual : why specific ‘everyday’ events occur
● scientific : generalising scientific theories
● behavioural : explaining behaviour and decision making
Which processes?
● cognitive : determining the causes (explanans) of an event (explanandum) and
relating these to a particular context
● social : transferring knowledge between explainer and explainee
EXPLANATIONS: WHICH?

8. Which audience?
● engineers/scientists/experts
● end-users
Which characteristics?
● Transparency (traceability + verificability)
● Intelligibility + clarity
EXPLANATIONS: WHICH?
Which language?
● Visual
● Written
● Spoken

9. Reuse!! Existing knowledge sources serve as background knowledge (the
“old”) to generate explanations (the “new”):
● Plenty of available sources (KGs, datahubs, open data...)
● Connected, centralised hubs
● Multi-domain, allowing serendipity
EXPLANATIONS: HOW?

10. Some examples

11. [2] Tiddi. (2016), Explaining Data Patterns using Knowledge from the Web of Data, Ph.D. thesis.
Demo: http://dedalo.kmi.open.ac.uk/
Explaining web searches
using the Linked Data Cloud
Why do people search for “A Song of Ice and
Fire” only in certain periods?
EXPLAINING DATA PATTERNS

12. Explaining user online activities
with Wikidata, recommending
Open University courses
[3] http://afel-project.eu
EXPLAINING BEHAVIOURS

13. Using identity links to find:
● The NYT dataset is about places in
the US (trivial)
● The Reading Experience Dataset is
about poets/novelists which
committed suicide (less trivial)
[4] Tiddi. (2014), Quantifying the bias in data links (EKAW201 4)
owl:sameAs
skos:exactMatch
...
A
B
Projection of B in A
EXPLAINING BIAS IN DATASETS

14. Using open data (DBpedia,
MK:DataHub) to enhance
smart-city applications
[5] Tiddi et al. (2018), Allowing exploratory search from podcasts: the case of Secklow Sounds Radio (ISWC2018)
EXPLAINING RADIO CONTENTS

15. Semantic mapping with
ShapeNet and ConceptNet
DBpedia ConceptNet ShapeNet
EXPLAINING SCENES IN MOTION
[6] Chiatti et al., Task-agnostic, ShapeNet-based Object Recognition for Mobile Robots, DARLI-AP 2019 (EDBT/ICDT 2019)

16. Explaining and rebalancing
LSTM networks using linguistic
corpora (e.g. FrameNet)
[7] Mensio et al., Towards Explainable Language Understanding for Human Robot Interaction
EXPLAINING NEURAL ATTENTIONS

17. Cooperation Databank : 50
years of scientific studies on
human cooperation
Scholarly KGs (e.g. Scigraph) to
support systematic
reviews/meta-analyses
[8] https://amsterdamcooperationlab.com/databank/
EXPLAINING SCIENTIFIC RESEARCH

18. Bringing together social and
computer scientists
Reflect on the threats and
misuse of our technologies
[9] https://kmitd.github.io/recoding-black-mirror/
EXPLAINING ETHICS TO MACHINES?

19. Sharing and reusing is the key to explainable systems
● Lots of data
● Lots of theories (e.g. insights from the social/cognitive sciences [10])
(My) desiderata:
+ cross-disciplinary discussions
+ formalised common-sense knowledge (Web of entities, Web of actions)
+ links between data, allow serendipitous knowledge discovery
SOME TAKEAWAYS
[10] Tim Miller (2018), Explanations in artificial intelligence: Insights from the social sciences, Artificial Intelligence.

20. Thank you
...and all of them!
@IlaTiddi
i.tiddi@vu.nl
kmitd.github.io/ilaria