The growing availability of massive data is pushing back the technical frontiers in many fields. On the one hand, the heterogeneous, semi-structured, incomplete or uncertain nature of data calls into question the usual statistical models and algorithms dedicated to decision-making. On the other hand, data management raises new operability constraints, such as security, integrity and traceability. What's more, producing knowledge requires building models that deliver explainable, statistically valid and calculable decisions. Acceptance of results also requires that confidentiality and loyalty be reinforced. At the same time, new developments in optimization should make it possible to improve estimation procedures.

Challenges

• Heterogeneous, complex, incomplete, semi-structured and/or uncertain data;

• Fast big data: structuring data to be able to exploit it;

• E-learning, methodology for massive data, efficient methods;

• Improved storage, computation and estimation for data science;

• Game-theoretic modeling of interactions between agents (human or artificial);

• Multi-scale and multimodal representation and algorithms;

• Theoretical analysis of heuristic methods (complexity theory, information geometry, Markov chain theory);

• Human-machine coevolution in autonomous systems: conversational agents, cars, social robots.

Digital trust is built on the implementation of ethically responsible methodologies through the transparency and accountability of algorithmic systems; the regulation of the collection, use and processing of personal data; and the reinforcement of regulation through appropriate digital procedures. Privacy by design is a form of regulation that includes the protection of personal data at all stages of collection and processing. The tracing of tools applied to data must also be developed in such a way as to facilitate the explanation of the model for experts and users alike, making algorithmic systems auditable. Confidentiality principles, although easy to formulate, require modifications to storage and processing infrastructures, with major legislative, sociological and economic impacts. Transparency techniques for algorithmic systems will be developed, focusing on: fairness, loyalty and non-discrimination, and accountability-by-construction.

Challenges

• Responsibility-by-design, Explicability-by-design;

• Transparency-by-design, fairness-by-design;

• Auditing algorithmic systems: non-discrimination, fairness, technical bias, neutrality, equity;

• Measuring trust and digital appropriation;

• Progressive user-centric-analytics (interactive monitoring of decision-making systems: dataviz dashboards, GUIs);

• Responsibility for information processing and decision-making: data usage control and fact-checking;

• Causal discovery, traceability of inferences from source data, interpretability of deep architectures.

Digital trust is built on the implementation of ethically responsible methodologies through the transparency and accountability of algorithmic systems; the regulation of the collection, use and processing of personal data; and the reinforcement of regulation through appropriate digital procedures. Privacy by design is a form of regulation that includes the protection of personal data at all stages of collection and processing. The tracing of tools applied to data must also be developed in such a way as to facilitate the explanation of the model for experts and users alike, making algorithmic systems auditable. Confidentiality principles, although easy to formulate, require modifications to storage and processing infrastructures, with major legislative, sociological and economic impacts. Transparency techniques for algorithmic systems will be developed, focusing on: fairness, loyalty and non-discrimination, and accountability-by-construction.

Challenges

• Responsibility-by-design, Explicability-by-design;

• Transparency-by-design, fairness-by-design;

• Auditing algorithmic systems: non-discrimination, fairness, technical bias, neutrality, equity;

• Measuring trust and digital appropriation;

• Progressive user-centric-analytics (interactive monitoring of decision-making systems: dataviz dashboards, GUIs);

• Responsibility for information processing and decision-making: data usage control and fact-checking;

• Causal discovery, traceability of inferences from source data, interpretability of deep architectures.

Companies involved in the data economy continually need to rethink how they structure themselves: they must adopt a project-oriented organization with rapid changes in resource allocation. The data economy also raises issues of concentration and monopoly. A small number of companies (GAFAM) hold most of the data. This market concentration can lead to unfair competition, and innovation in small and medium-sized businesses is likely to suffer. Citizens expect governments to intervene in the digital economy to prevent too much concentration and monopoly. Governments must prevent the leakage of information to preserve the sovereignty of states and respect for regulations.

Challenges

• Privacy-by-design, GDPR;

• Privacy-friendly learning (differential privacy);

• Development of ethically responsible methodologies and technologies to regulate the collection, use and processing of personal data, and the exploitation of knowledge derived from such data;

• IT security for data processing chains;

• Security/crypto: blockchain and trusted third parties.