Session	Synthesis Session 1
Duration	1 hour
Date/Time	27 February 2019 17:00 GMT
	9:00am PST/12:00pm EST
	5:00pm GMT/6:00pm CET
Convener	Ken Baclawski

Ontology Summit 2019 Synthesis Session 1

Abstract

The aim of this week's session is to synthesize the lessons learned so far on the tracks that are under way. Each track has met once, and so we will have gained insights from a combination of invited speakers, chat log comments and blog page discussions.

A second synthesis session will take place after the tracks have met again, and that, along with today's session outcome, will form the basis of this year's Ontology Summit Communiqué.

Agenda

Introduction: Ken Baclawski (See summary below)
The track co-champions will give summaries of their respective tracks:
- Commonsense (See Resources Section below)
- Narrative
- Financial Explanations
- Medical Explanations
- Explainable AI Ram D. Sriram and Ravi Sharma Synthesis
Video Recording

Summary of Ontology Summit 2019 Sessions

There were 9 sessions so far. Each session had the proceedings (from the chat room) and a recording (one audio recording and the rest video recordings). The following are the speakers with links to their presentation slides (when they were provided) and the recordings.

Date	Speaker	Topic	Presentation	Recording
11/14	John Sowa	Explanations and help facilities designed for people	Slides	Video
11/28	Ram D. Sriram and Ravi Sharma	Introductory Remarks on XAI	Slides	Video
11/28	Derek Doran	Okay but Really... What is Explainable AI? Notions and Conceptualizations of the Field	Slides	Video
12/05	Gary Berg-Cross and Torsten Hahmann	Introduction to Commonsense Knowledge and Reasoning	Slides	Video
1/16	Ken Baclawski	Introductory Remarks	Slides	Video
	Gary Berg-Cross and Torsten Hahmann	Commonsense	Slides
	Donna Fritzsche and Mark Underwood	Narrative	Slides
	Mark Underwood and Mike Bennett	Financial Explanation
	Ram D. Sriram and David Whitten	Medical Explanation
	Ram D. Sriram and Ravi Sharma	Explainable AI	Slides
1/23	Michael Grüninger	Ontologies for the Physical Turing Test	Slides	Video
1/23	Benjamin Grosof	An Overview of Explanation: Concepts, Uses, and Issues	Slides	Video
1/30	Donna Fritzsche	Introduction to Narrative		Audio only
	Ken Baclawski	Proof as Explanation and Narrative	Slides
	Mark Underwood	Bag of Verses: Frameworks for Narration from Cognitive Psychology	Slides
2/6	Mike Bennett	Financial Explanations Introduction	Slides	Video
	Mark Underwood	Explanation Use Cases from Regulatory and Service Quality Drivers in Retail Credit Card Finance	Slides
	Mike Bennett	Financial Industry Explanations	Slides
2/13	David Whitten	Introduction to Medical Explanation Systems		Slides
	Augie Turano	Review and Recommendations from past Experience with Medical Explanation Systems	Slides
	Ram D. Sriram	XAI for Biomedicine	Slides
2/20	William Clancey	Explainable AI Past, Present, and Future–A Scientific Modeling Approach	Slides	Video

Conference Call Information

Date: Wednesday, 27-February-2019
Start Time: 9:00am PST / 12:00pm EST / 6:00pm CET / 5:00pm GMT / 1700 UTC
- ref: World Clock
Expected Call Duration: 1 hour

The Video Conference URL is https://zoom.us/j/689971575
- iPhone one-tap :
  - US: +16699006833,,689971575# or +16465588665,,689971575#
- Telephone:
  - Dial(for higher quality, dial a number based on your current location): US: +1 669 900 6833 or +1 646 558 8665
  - Meeting ID: 689 971 575
  - International numbers available: https://zoom.us/u/Iuuiouo
Chat Room

Attendees

Proceedings

TBD

Resources

Here are some ideas for a working synthesis outline for Explanations (Gary Berg-Cross)

1. Meaning of Explanation [An explanation is the answer to the question "Why?" as well the answers to followup questions such as "Where do I go from here?"] – there are range of these

Grosof deductive Proof , with a formal knowledge representation (KR) – is the gold standard, but there are many types with different representations
- – E.g., natural deduction –HS geometry there is also probabilistic
Causal model Explanations

There are a range of concepts related to explanation

Source or provenance, say of a rule
Transparency in origin
Ability to explore and drill down
Focus on the subject on hand

Additional Aspects/Modifiers of explanation:

- Summarization, grain (coarse vs. fine), drill-down, elaboration
- Partial vs. complete
- Approximate vs. precise
- Structuring of inference in presentation
- Assumptions and presumptions
- Targeting to user knowledge and goals (i.e., user model)
- Natural language (NL) generation
- Graphical presentation
- Use of Terminology (source and validity)
Understand-ability and presentability

Trending-Up concepts of explanation

Influentiality – , heavily weighted hidden nodes and edges effect
Reconstruction – simpler / easier-to-comprehend model
Lateral relevance – interactivity for exploration
Affordance of Conversational human-computer interaction (HCI)
Good explanations quickly get into issue of understanding & meaning since much meaning involves background knowledge and commonsense and lies in the implicit and unspoken.
- What does it mean to understand , follow and explain a set of instructions?

2. Problems and issues

From GOFAI
- An early goal of AI was to teach/program computers with enough factual (often commonsense) knowledge about the world so that they could reason about it in the way people do which is not strictly logical is some circumstances
early AI demonstrated that the nature and scale of the problem was difficult.
- one reason is that simple, direct approaches like rule based systems were brittle.
People seemed to need a vast store of everyday, background knowledge for common tasks. A variety of background knowledge was needed to understand & explain decisions
Do we have a small, common ontology that we mostly all share for representing and reasoning about the physical world?

it remains challenging to design and evaluate a software system that represents commonsense knowledge and that can support reasoning (such as deduction and explanation) in everyday tasks. (evidence from modified Physical Turing Tests)

- PRAxIS work (Perception, Reasoning, and Action across Intelligent Systems)

3. From XAI

Performance vs. Explainability: DARPA XAI Program
- While DL performs well is is very low on explainability which decision trees are the reverse
- One context of recent work is that of Deep machine-learning systems. Explanations for their decisions can be problematic since one cay say what they learn is a dimension space of numbers that have no words of any kind, so explanation in natural language is not immediately available.
- One approach to handling this problem is called Deep Explanation which uses modified deep learning techniques to learn explainable features as part of what it learns.
- More traditional GOFAI approaches may develop Interpretable Models using techniques that learn more structured, interpretable, causal models.
Concept of Humagic Knowledge
TBD

Bridging from sub-symbolic to symbolic -ontologies help constrain options

4. Application areas

Medicine
Finance
- Automated Decision Support for Financial Regulatory/Policy Compliance
- Has requirements like competency Qs it needs to explain

Examples of successes? Rulelog’s Core includes Restraint bounded rationality

5. Relevance and relation to context

TBD

6. Synergies with commonsense reasoning

Spatial and physical reasoning are good areas.

7. Success stories/systems

ErgoAI Architecture ?

Issues Today in the Field of Explanation /Questions

How do we evaluate these ontologies supporting explanations and commonsense understanding?
How are these explanations ontologies related to existing upper ontologies?

8. Conclusions

In the future, we’ll share meanings with

computers, AIs, and robots. And that makes meanings matter even more. But it remains a hard problem.

- Smart systems may have to be embodied & have

sentience—the capacity to feel, perceive, or experience subjectively.

Benefits of Explanation (Grosof)
- Semi-automatic decision support
- Might lead to fully-automatic decision making – E.g., in deep deduction about policies and legal – especially the business and medicine topics.
- Useful for Education and training, i.e., e-learning – E.g., Digital Socrates concept by Janine Bloomfield of Coherent Knowledge
- Accountability • Knowledge debugging in KB development
- Trust in systems – Competence and correctness – Ethicality, fairness, and legality
- Supports Human-machine interaction and User engagement (see Sowa also)
- Supports Reuse / and guide choice for transfer of knowledge

9. Contemporary Issues

Confusion about concepts – Esp. among non-research industry and media – But needs to be addressed first in the research community
Mission creep, i.e., expansivity of task/aspect – Esp. among researchers. E.g., IJCAI-18 workshop on explainable AI.
Ignorance of what’s already practical – E.g., in deep policy/legal deduction for decisions: full explanation of extended logic programs, with NL generation and interactive drill-down navigation – E.g., in cognitive search: provenance and focus and lateral relevance, in extended knowledge graphs
Disconnect between users and investors
(Ignorance of past relevant work)
Some envision a fruitful marriage between classic logical approaches (ontologies) with statistical approaches which may lead to context-adaptive systems (stochastic ontologies) that might work similar to the human brain.

[A preliminary summary of the track on CommonSense Knowledge and Reasoning along with some discussion of its Relation to Explanation.]

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