1) The document discusses building intelligent systems that can explain themselves and their decisions. 2) It proposes using existing knowledge sources on the web as background knowledge to generate explanations for systems. 3) Several examples are provided of different types of explanations that could be generated by systems, such as explaining behaviors, scenes, neural attentions, inconsistencies, and more.

1. BUILDING INTELLIGENT SYSTEMS
(THAT CAN EXPLAIN)
Ilaria Tiddi
KR&R group, Faculty of Computer Science, VU Amsterdam
Cooperation Lab, Faculty of Behavioural and Movement Sciences
VU Amsterdam

2. Disclaimer
This is NOT a presentation on eXplainable AI (XAI) ...
...but rather on systems making sense of complex data
…we can argue at Q time if they somewhat overlap

3. ● Learn new knowledge
● Find meaning : we reconcile the contradictions in our knowledge
● Socially interact : we create a shared meaning, we change/influence the
others’ beliefs
● ...and because GDPR says so
Users have a “right to explanation”
for any decisions made about them
WHY DO WE NEED (SYSTEMS THAT) EXPLAIN?

4. 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
DEFINING EXPLANATIONS (ε)
V-IV AC
Plato&Aristotle
XVII BC 1948 19641903 2015
?

5. Explication (⋍ Interpretation)
Justification
Explic-/Interpret-/Explainability
SOME DISAMBIGUATIONS
Explanation
ε of why a decision is good
the degree to which an observer
can understand the cause of a
decision

6. Which types?
● factual ε : why specific ‘everyday’ events occur
● scientific ε : generalising scientific theories
● reason ε : explaining behaviour and decision making
Which processes?
1) cognitive : determining the causes (explanans) of an event (explanandum) and
relating these to a particular context
2) social : transferring knowledge between explainer and explainee
RESEARCH GOAL : INTELLIGENT SYSTEMS THAT CAN EXPLAIN

7. Which audience?
● engineers/scientists/experts
● end-users
Which characteristics?
● Transparency (traceability + verificability)
● Intelligibility + explainability
Which language?
● Visual
● Written
● Spoken
RESEARCH GOAL : INTELLIGENT SYSTEMS THAT CAN EXPLAIN

8. Existing knowledge sources can serve as background (the “old”) knowledge to generate
explanations:
● Plenty of available sources (not only RDF...)
● Connected, centralised hubs
● Multi-domain (serendipity!)
APPROACH : REUSE AVAILABLE KNOWLEDGE SOURCES

9. Generating explanations from the Web of Data [2]
EXAMPLE : FACTUAL, WRITTEN ε
[2] Tiddi. (2016), Explaining Data Patterns using Knowledge from the Web of Data, Ph.D. thesis. Demo: http://dedalo.kmi.open.ac.uk/
Why do people search for “A Song of Ice
and Fire” only in certain periods?”

10. Explaining behaviours and recommending self-learners using online resources [3]
EXAMPLE : REASON, VISUAL ε
[3] http://afel-project.eu

11. Robots finding explanations for their behavior in a smart-city datahub [4]
EXAMPLE : REASON, SPOKEN ε
[4] http://sciroc.eu
[ Shameless advert ]
1st ERL Smart CIties
RObotics Challenge
16–22/09/2019
Milton Keynes,UK
No need to have a
robot!!!

12. Explaining scenes in motion using ShapeNet [5] as background knowledge
(and YOLO [6] for pre-processing)
EXAMPLE : factual&reason, spoken ε
[5] http://www.shapenet.org
[6] https://pjreddie.com/darknet/yolo/

13. Explaining neural attentions
A multi-layer LSTM network to understanding NL
robotic commands [8]
Avoid training biases using linguistic corpora
(FrameNet [7]) combined with domain-specific
datasets
EXAMPLE : REASON, VISUAL ε
[7] https://framenet.icsi.berkeley.edu/fndrupal/
[8] Mensio et al., A Multi-layer LSTM-based Approach for Robot Command Interaction Modeling, Language and Robotics (LangRobo), IROS 2018.

14. Explaining inconsistencies using an autonomous agent in a smart office [9]
Monitoring Health&Safety using a SHACL-based model checking and behavioural trees
Centralised data integration, processing and reasoning
EXAMPLE : FACTUAL, WRITTEN ε
[9] Bastianelli et al., Meet HanS, the Heath&Safety autonomous inspector, Posters&Demos track at ISWC 2018.

15. Re-coding Black Mirror [10] workshops
Bringing social&computer scientists
together to understand the threats of
their own technologies, and raise
awareness on methods explainability
Ethics by Design methodology
MACHINE EXPLANATION NEED MACHINE ETHICS
[10] https://kmitd.github.io/recoding-black-mirror/

16. Databank
● Collection of meta-analyses to study human cooperation
● 3.5k work on social dilemmas ( = benefitting the others vs. self-interest)
My goal
● creation of a research platform generating explanations for human
cooperation (+ search facilities)
● Generalising the methodology to Life & Medical Sciences (long-term)
WHAT ABOUT SCIENTIFIC EXPLANATIONS?

17. Thank YOU
Question time!
@IlaTiddi
i.tiddi@vu.nl
http://people.kmi.open.ac.uk/ilaria (changing soon)