The 5th IEEE International
Conference on Data Science
and Advanced Analytics

1–4 October 2018
Turin — Italy

Industrial Day

This year edition of DSAA will feature, for the first time, an Industrial Day which will be held on the first day of DSAA, October 1st, at the Intesa Sanpaolo skyscraper, in Turin, Corso Inghilterra 3, a modern landmark of the city (
The Industrial Day will be single-track and only based on keynotes and industrial invited talks.
Coffee breaks and lunch will be provided. The day will be concluded by DSAA Welcome Reception.

The event is sponsored by

Industrial day
Auditorium, Grattacielo Intesa Sanpaolo

Registration desk open all day,
from 9:00 to 19:30.


President of ISI Foundation
What strange a brain is the planet, what strange a planet is the brain; a brief reflection on science and society

There is a revolution going on, the digital revolution, from whose explosive force we are all shaken; a revolution that is at the crossroads between a cultural revolution and an industrial revolution, and shares hopes and risks of both: the revolution of 'Big Data' and 'Artificial Intelligence'. It will change the structure of human relations and interactions between man and nature and man and its own artifacts in the centuries to come, for better or for worse. It is a revolution that affects the whole planet of our fundamental values: work, sustainability of life, democracy, human relationships, even our way of doing science and business, economy and finance, and of taking care of health and happiness. And if on the one hand the perspective it gives us a glimpse of is of a radiant future of progress, unprecedented growth of human values (both individual and collective), fantastic quality of life for those who will come after us, on the other, it conditions all this to ethical constraints and choices that humanity never had to face collectively. Will we be able to win this epoch-making global battle, which so much fear and instability is instilling in society and politics? We have the tools: first of all, that unparalleled machine which is our brain; we must build – and immediately – the missing ingredients: courage, solidarity, a shared vision of the future, the ability to coexist with technology in a proactive way and not succumb to it. I’ll briefly talk about these crucial challenges, from the privileged point of view of a scientist, in charge of a research Institute that deals just with complex systems, data science and analytics, AI algorithms, … and the human brain.


NYU and TwoSigma
Predictability and other Predicaments in Machine Learning Applications

In the context of building predictive models, predictability is usually considered a blessing. After all - that is the goal: build the model that has the highest predictive performance. The rise of 'big data' has in fact vastly improved our ability to predict human behavior thanks to the introduction of much more informative features. However, in practice things are more differentiated than that. For many applications, the relevant outcome is observed for very different reasons: One customer might churn because of the cost of the service, the other because he is moving out of coverage. In such mixed scenarios, the model will automatically gravitate to the one that is easiest to predict at the expense of the others. This even holds if the predictable scenario is by far less common or relevant. We present a number of applications where this happens: clicks on ads being performed 'intentionally' vs. 'accidentally', consumers visiting store locations vs. their phones pretending to be there, and finally customers filling out online forms vs. bots defrauding the advertising industry. The implications of this are effect are significant: the introduction of highly informative features can have significantly negative impact on the usefulness of predictive modeling and potentially create second order biased in the predictions.

Marco Lamieri

Intesa Sanpaolo

Andrea Prampolini

Banca IMI
Artificial Intelligence Laboratory: a collaborative model between industry and fundamental research

The AI Lab of the Innovation Center of Intesa Sanpaolo, one of the largest banking groups in Italy, together with ISI Foundation, a private foundation whose primary mission is to conduct fundamental research in the areas of Complex Systems and Data Science, collaborate on industrial researches for specific real-world challenges mainly focused, but not exclusively, on the financial domain. For each industrial research challenge, a team is settled, composed of the top-level researchers by ISI Foundation and of the specialists of of Intesa Sanpaolo Group, each of them bringing the valuable experience of his domain of expertise. In this talk we will describe this successful collaborative model between (seemingly) distant worlds, while providing an overview of the initial challenges and early success stories.

Mario D’Almo

Intesa Sanpaolo

Daniele Amberti

Intesa Sanpaolo
Data Science in large corporations, a real-world experience in the financial sector

Data Science is about the proactive use of data and Advanced Analytics to drive better decision making. Successfully implementing Data Science is a challenge in terms of organizational and process impacts, cultural change, compliance, privacy, operationalization and impact assessment of the findings. Making data driven decisions through Data Science should be the aiming of all departments. One should not expect all parts of a large corporation to be at the same level of maturity, there are parts historically used to these methodologies (e.g. Marketing and Financial Engineering) and there are others used to rely on their intuition therefore the objective of a Data Science department of moving the organization up in the analytics maturity curve while properly managing all related aspects is even more challenging.


NYU and Detectica
The Predictive Power of Data on Consumers' Fine-Grained Behavior

Massive ultra-fine-grained data on individuals' behaviors holds remarkable predictive power. I examine several applications, including targeting marketing offers to bank customers, showing that machine learning methods using such data create remarkably predictive models. I then dig deeper and discuss how one can explain the predictions made from such complex models. This analysis shows that the fine-grained behavior data incorporate various sorts of information that we traditionally have sought to capture by other means. For example, for marketing, the models built from behavior data effectively incorporate demographics, psychographics, category interest, and purchase intent. Finally, I discuss the flip side of the coin: the remarkable predictive power based on fine-grained information on individuals raises new privacy concerns. I conclude by presenting one possible mechanism for addressing concerns about the inferences drawn by models using data about people's behavior. Hint: it hinges on the ability to explain model decisions.

V R Ferose

Intelligent Technologies – Apocalypse or Genesis

The ability for businesses to adapt to country specific legal compliance aspects and communicate across languages and cultures is fast becoming an increasingly great priority for many organizations worldwide. The advancements in technologies like artificial intelligence and machine learning has enhanced the ease of global expansion drives of businesses. AI powered legal compliance tracking and machine translation tools are already paving the pathway of next gen localization services. The talk will focus on the rise of Intelligent technologies and its impact on global businesses. It will also touch upon the need of an ethical code of governance around the usage of these intelligent technologies and how the human-machine combination leads up in defining the future of humanity.


Modeling (Human) Bias in Deep Learning

Human data brings with it human biases. Algorithms trained on that data can effectively perpetuate and amplify these biases, creating feedback loops that deepen social division. In this talk, I walk through how human bias is at play in the end-to-end machine learning cycle, and the effects this can have within society.


NTENT and Northeastern University
Challenges of {BIG, small, Right} Data

Big data is trendy, but there are many possible interpretations of its real impact, as well as the opportunities, risks and technological challenges. We will start with two key questions: can a company use big data? If so, should it? The opportunities are clear, while the challenges are many, including scalability, bias, and privacy on the problem side, as well as transparency, explainability, and ethics on the machine learning side. So we perform an analysis that includes all the data pipeline process. At the end we will conclude that what is important is the right data, not big data. In fact, the real challenge today, is machine learning for small data.


UC Berkeley
Saving Governance-By-Design

Governing through technology has proven irresistibly seductive. Technologists, system designers, advocates, and regulators increasingly seek to use the design of technological systems for the advancement of public policy—to protect privacy, advance fairness, or ensure law enforcement access, among others. Designing technology to “bake in” values offers a seductively elegant and potentially effective means of control. Technology can harden fundamental norms into background architecture, and its global reach can circumvent jurisdictional constraints, sometimes out of public view. As technology reaches into the farthest corners of our public and private lives it's power to shape and control human behavior, often imperceptibly, makes it an important locus for public policy. Yet while “Governance-by-design”—the purposeful effort to use technology to embed values—is becoming a central mode of policy-making, our existing regulatory systems are ill-equipped to prevent that phenomenon from subverting public governance. Far from being a panacea, governance-by-design has undermined important governance norms and chipped away at important rights. In administrative agencies, courts, Congress, and international policy bodies, public discussions about embedding values in design arise in a one-off, haphazard way, if at all. Constrained by their structural limitations, these traditional venues rarely explore the full range of values that design might affect, and often advance, a single value or occasionally pit one value against another. They seldom permit a meta-discussion about when and whether it is appropriate to enlist technology in the service of values at all. And policy discussions almost never include designers, engineers, and those that study the impact of socio-technical systems on values. When technology is designed to regulate without such discussion and participation the effects can be insidious. The resulting technology may advance private interests at the price of public interests, protect one right at the expense of another, and often obscures government and corporate aims and the fundamental political decisions that have been made. This talk proposes a detailed framework for saving governance-by-design. It examines recent battles to embed policy in technology design to identify recurring dysfunctions of governance-by-design efforts in existing policy making processes and institutions. It closes by offering a framework to guide "governance-by-design" that surfaces and resolves value disputes in technological design, while preserving rather than subverting public governance and public values.

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