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 2013 [email protected] – Data Driven Paris - January 2016 * Data Transparency Lab, Big Data Value Association
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Algorithmic Systems Transparency and Accountability in Big Data & Cognitive Era
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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*
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,
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
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)