Presentation of Nozha BOUJEMAA at Data Driven Paris - January 17th 2017

1. Algorithmic Systems
Transparency and Accountability
in the Big Data Era
Nozha Boujemaa - Director of Research
Advisor to The CEO of Inria in Big Data
Member of the Boards of DTL* & BDVA*
Décembre 2013Nozha.Boujemaa@inria.fr – Data Driven Paris - January 2016
* Data Transparency Lab, Big Data Value Association

2. Introduction

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Focus of data analytics is changing –
From description of past to decision supportValueandcomplexity
Inform
Analyze
Act
Descriptive
Examples
– Plant operation
report
– Fault report
What happened?
Diagnostic
– Alarm
management
– Root cause
identification
Why did it happen?
Predictive
– Power
consumption
prediction
– Fault prediction
What will happen?
Prescriptive
– Operation point
optimization
– Load balancing
What shall we do?
Gartner 2013 - N. Gauss/Siemens - 2015
N. Boujemaa - Data Driven Paris - January 2016
Big Data Technologies are enablers for AI capabilities
From « Data Analytics » to « Cognitive Systems »
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Drivers & Barriers
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Drivers/opportunities:
• Data Science performance and capabilities growth
• New Business Models for Value Creation: CRM, Industry 4.0, ...
=> Prerequisite: Data- Algorithm duality
Barriers:
• Trust & appropriation: Data & Algorithmic transparency &
accountability
§ Data veracity (malicious attack, noise, etc)
§ Quality and usage conditions of Algorithms
• Interdisciplinary skills in Data Science is KEY
N. Boujemaa - Data Driven Paris - January 2016

5. DATA SCIENCE
PILLARS & CHALLENGES

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5 Pillars for Data Science*
1- Data Management: unstructured and semi-structured
§ Semantic interoperability of heterogeneous sources and representations,
Data quality, Data provenance,
2- Data Processing Architecture :
§ Scalability, Decentralization (Cloud/Fog etc), Low-energy consumption
3- Data Analytics:
§ Semantic Analysis, Content Validation, Predictive/Presciptive Analytics
4- Data Protection:
§ Privacy-enhancing models and techniques, Robusteness against
reversibility
5- Data Visualization:
§ Interactive visual analytics, Collaborative, Cross-platform data frameworks
* Inspired by BDVA SRIA technical priorities
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Main R&D Challenges
1. Progressive user-centric analytics:
§ On-line Learning (real-time learning from few examples)
§ Correlation vs Causality
§ Seamless co-evolution between Humans & Machines
2. Energy efficient & optimized architecture
3. Scientific and Technical Methods for Transparency,
Accountability and Explainability
Applications: Responsive communities, Energy
optimization, Food security, Industry 4.0 etc.
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Challenges for Data Science
Responsible and Ethical Data Management and Analytics
It is often assumed that big data techniques are unbiased:
§ because of the scale of the data
§ because the techniques are implemented through algorithmic systems.
⇒ it is a mistake to assume they are objective simply because
they are data-driven * (“Data fundamentalism”)
Consensus is emerging to develop methods and Tools to build
Trust over Transparency & Accountability for Data and
Algorithms
⇒ Implementing the “Responsible-by-design” principle (fairness/
equity, loyalty, neutrality etc.)
* White House – OSTP Report « Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights », May 2016
* Federal Trade Commission Report: “Big Data: A Tool for Inclusion or Exclusion? January 2016
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Mastering Big Data Technologies: Bias problems could impact
data technologies accuracy and people’s lives
Challenges 1: Data Inputs to an Algorithm
§ Poorly selected data
§ Incomplete, incorrect, or outdated data
§ Data sets that lack disproportionately represent certain populations
§ Malicious attack
Challenges 2: The Design of Algorithmic Systems and Machine
Learning
§ Poorly designed matching systems
§ Unintentional perpetuation and promotion of historical biases
§ Decision-making systems that assume correlation implies causation
Data Science Challenges:
Responsible and Ethical Data Management and Analytics
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§ Trust and Transparency of computer-aided decision-
making process (decision responsibility): what are the
different criteria/data/settings that have led to the specific
decision in order to understand the global path for the
reasoning?
§ “How can I trust Machine Learning prediction?” it
happens to build the model of the object context rather
the object itself
§ Decision explanation and tractability
§ Robustness to bias/diversion/corruption
§ Careful software reuse
Data Science Challenges:
Responsible and Ethical Data Management and Analytics
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Potential Synergies for International
collaborations
1. Opening the black box of Deep Learning,
2. Trustworthiness of Machine Learning Algorithms
(bias typology, software reuse, etc.)
3. Algorithmic explanability approaches,
4. Cross views on fairness definitions and related
measuring methods
5. Interdisciplinary Training for Data Scientist (in
addition to Maths-Computer Science)
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DARPA initiative: Explainable AI (August 2016)
The Explainable AI (XAI) program aims to create a suite of
machine learning techniques that:
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• Produce more
explainable models,
while maintaining a
high level of learning
performance
(prediction accuracy);
• Enable human users to understand, appropriately
trust, and effectively manage the emerging
generation of artificially intelligent partners.

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Relevant French Programs
TransAlgo (Inria) – Dec 2016, Minister of Digital Economy
§ National Scientific Platform for Transparency & Accountability Tools and
Methods for Data and Algorithms (Fairness, Neutrality, Loyalty); b2b & b2c
§ Support of The new “Law for Digital Republic” after CGE report,
§ Contributors: CNNum, DGCCRF (French FTC) besides academia and
associations,
§ 3 Objectives: * Resource center (reports, publications, software, initiatives),
*Research & Dev. programs -with DTL, * Best practices & Moocs
I2-DRIVE Program: Interdisciplinary Institute for Data Research: Intelligence,
Value and Ethics (ongoing)
§ 4 Overarching Challenges: From Data to Knowledge, from Data to Decision, Deep
learning toward Artificial Intelligence, Digital Trust and Appropriation, Data economy
and regulation
§ Scientific and disciplinary foundations: Data Science, Management and Economy,
Social Sciences, Legal Sciences
§ Roadmap for 10 years, 200 M€ Budget, 12 academic institutions

14. CONCLUSION

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§ Algorithmic systems transparency is essential for digital
trust and appropriation
§ Transparency and Responsible-by-Design approaches are
economical competitiveness factors
§ Tools for consumers (b2b & b2c) empowerment
§ Tools for regulators (Loi pour la République Numérique)
Conclusion :

16. Thanks for your attention
Nozha.Boujemaa@inria.fr
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