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Audio and machine learning: Gaël Richard’s award-winning project

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Gaël Richard, a researcher in Information Processing at Télécom Paris, has been awarded an Advanced Grant from the European Research Council (ERC) for his project entitled HI-Audio. This initiative aims to develop hybrid approaches that combine signal processing with deep machine learning for the purpose of understanding and analyzing sound.

“Artificial intelligence now relies heavily on deep neural networks, which have a major shortcoming: they require very large databases for learning,” says Gaël Richard, a researcher in Information Processing at Télécom Paris. He believes that “using signal models, or physical sound propagation models, in a deep learning algorithm would reduce the amount of data needed for learning while still allowing for the high controllability of the algorithm.” Gaël Richard plans to pursue this breakthrough via his HI-Audio* project, which won an ERC Advanced Grant on April 26, 2022

For example, the integration of physical sound propagation models can improve the characterization and configuration of the types of sound analyzed and help to develop an automatic sound recognition system. “The applications for the methods developed in this project focus on the analysis of music signals and the recognition of sound scenes, which is the identification of the recording’s sound environment (outside, inside, airport) and all the sound sources present,” Gaël Richard explains.

Industrial applications

Learning sound scenes could help autonomous cars identify their surroundings. The algorithm would be able to identify the surrounding sounds using microphones. The vehicle would be able to recognize the sound of a siren and its variations in sound intensity. Autonomous cars would then be able to change lanes to let an ambulance or fire engine pass, without having to “see” it in the detection cameras. The processes developed in the HI-Audio project could be applied to many other areas. The algorithms could be used in predictive maintenance to control the quality of parts in a production line. A car part, such as a bumper, is typically controlled based on the sound resonance generated when a non-destructive impact is applied.

The other key applications for the HI-Audio project are in the field of AI for music, particularly to assist musical creation by developing new interpretable methods for sound synthesis and transformation.

Machine learning and music

“One of the goals of this project is to build a database of music recordings from a wide variety of styles and different cultures,” Gaël Richard explains. “This database, which will be automatically annotated (with precise semantic information), will expand the research to include less studied or less distributed music, especially from audio streaming platforms,” he says. One of the challenges of this project is that of developing algorithms capable of recognizing the words and phrases spoken by the performers, retranscribing the music regardless of its recording location, and contributing new musical transformation capabilities (style transfer, rhythmic transformation, word changes).

“One important aspect of the project will also be the separation of sound sources,” Gaël Richard says. In an audio file, the separation of sources, which in the case of music are each linked to a different instrument, is generally achieved via filtering or “masking”. The idea is to hide all other sources until only the target source remains. One less common approach is to isolate the instrument via sound synthesis. This involves analyzing the music to characterize the sound source to be extracted in order to reproduce it. For Gaël Richard, “the advantage is that, in principle, artifacts from other sources are entirely absent. In addition, the synthesized source can be controlled by a few interpretable parameters, such as the fundamental frequency, which is directly related to the sound’s perceived pitch,” he says. “This type of approach opens up tremendous opportunities for sound manipulation and transformation, with real potential for developing new tools to assist music creation,” says Gaël Richard.

*HI-Audio will start on October 1st, 2022 and will be funded by the ERC Advanced Grant for five years for a total amount of €2.48 million.

Rémy Fauvel

new heroism

New Heroism: a paradoxical model of success

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Today, the ideals of success cover multiple areas of society, such as work, cinema, and personal life. In his book Le Nouvel Héroïsme, Olivier Fournout, a sociologist and semiologist at Télécom Paris, analyzes the conditions that have allowed the emergence of a model of success that is riddled with paradoxes.

A hero is a someone capable of bravery; of feats that reveal extraordinary courage. That is the dictionary definition, in any case. Another dictionary entry defines a hero as an individual worthy of public esteem, for their strength of character, their genius, and total dedication to their work or cause. In terms of fiction, it relates to mythology; to legendary characters who accomplish great feats. The term can also refer to literary, dramatic and cinematographic works.

According to Olivier Fournout, a researcher in Sociology at Télécom Paris, the modern approach to the hero intersects all these definitions. In our society, a hero can be Arnaud Beltrame, the policeman who saved a hostage and defended republican values. At the singer’s funeral, Emmanuel Macron proclaimed that Johhny Hallyday was also a hero – a star who conveyed an imaginary of rebellion and freedom. It was Emmanuel Macron who then declared: “We must return to political heroism” in an interview in August 2017. “Right now, on the roads of France,” reports Olivier Fournout, “there are Carrefour delivery trucks with the slogan, ‘Thank you, heroes’ written on the side, and a photo of the supermarket’s employees”. For the sociologist, “the common use of the word hero to refer to such different people calls our most contemporary modernity into question”.

The matrix of heroism

The heroic model can be found in a myriad of paradoxical orders which seem appropriate to the present time, and are found in extremely diverse and heterogeneous fields. The whole imaginary of the tragic hero is found in paradoxes that abound today on a multitude of levels. According to the discourses and images in broad circulation, in order to be a hero today, one has to be both “with others and against others, respect the frameworks and shatter them, and to be good, both on the outside and in one’s inner dimension,” argues Olivier Fournout, based on numerous pieces of evidence. Individuals are pushed to strive for this ideal either by myths, or by examples with real people like bosses and artists.

The difficulty lies in having to be empathetic while also being in competition. The researcher illustrates this in his book Le Nouvel Héroïsme with a Nike advertisement that portrays a young hockey player who knocks over garbage cans in the street, slams doors in people’s faces, and destroys walls by hitting pucks at them. Yet he also carries a disabled person up the stairs. Here we see both violence and a concern for others in everyday life. “This must be seen both as a notion of empowerment, that can be positive for individuals, and an endangerment. This duality that characterizes the complexity of the matrix of heroism is what I analyze in my book,” explains Olivier Fournout.

“The pressure on individuals to succeed and to constantly surpass themselves can give rise to psychosocial risks such as burnout or depression,” says the sociologist. To strive for this heroic model that is presented as an ideal, a person can overwork themself. The difficulty in managing paradoxes like cooperation and competition with those in one’s milieu can lead an individual to endure psychological or cognitive stress. The discourse of surpassing oneself creates difficulties for people. Furthermore, the pressure weighing on each person is accompanied by a call for training or self-training, with the promise of an “increase in skills of self-expression, of creativity, and of management of social relations,” Olivier Fournout believes.  

To describe the matrix of heroism, which he also calls the “matrix of paradoxical injunctions”, the sociologist used more than 200 treaties on management and personal growth, advertisements, news articles portraying bosses, and a corpus of 500 Hollywood-style movies. The goal was to show the common structure of these extremely diverse fields. “Even though the word hero comes from cinema, I have seen it used by professors and consultants in the United States to illustrate management theories,” says the researcher.

Establishing an imaginary

In his book, Olivier Fournout indicates that the establishment of a dominant imaginary in our media spaces must first be incarnated into as wide a range of characters as possible. In the case of new heroism, this could be Arnaud Beltrame or Johnny Hallyday, but could also be representatives of Generation Z or the Start-up Nation, activists, or even a Sunday mountain biker. This imaginary must then be placed in a game of distorting mirrors in very heterogeneous universes, such as the world of work, individuals’ privacy, and great Hollywood myths. Thirdly, the matrix must be stabilized in the dominant editorial forms. In the end, the imaginary must pass through ‘portrait galleries’, i.e. role models conveyed in the press or in the world of management. These could be soccer players, artists, big bosses, or everyday heroes.   

Olivier Fournout uses a theatrical metaphor to articulate this. He speaks of scenes and counter-scenes to illustrate the succession of public and private moments, of great, exceptional feats, and heroism for everyone in everyday life. He thus highlights its heterogeneity, which forms part of the foundation of the heroic model. The sociologist uses the example of Shakespeare’s theater, which, in its historical plays, invites the spectator to observe the great official parades of power and to take a look behind the scenes. Some scenes portray the grand speeches of the future King Henry V, while others draw the spectator into the life of this Prince who, before becoming King, lived in taverns with thieves. “What I call counter-scenes are the gray areas, the sequences that are less official than those that take place in the spotlight,” says the researcher.

Applied to the professional world, counter-scenes refer to the personal investment in one’s work, everything related to, for example, passion, sweat, or emotions such as fear in the face of risks or changes. The scenes, on the other hand, portray the performance in social roles with a control over the outward signals that one conveys. “Another metaphor that can illustrate this heterogeneity of the scenes and counter-scenes is that of forging and counter-forging. When blacksmiths forge, they strike the metals to shape them, but they also hold back their blows at times to regain momentum, which they call counter-forging,” says Olivier Fournout.

A model that covers different spheres

 “In my corpus, there are management books written by professors from Harvard and MIT (Massachusetts Institute of Technology). These universities have a great power of dissemination that facilitates the propagation of an imaginary such as that of new heroism,” says the researcher. These universities also have a porosity with the world of consultants who participate in the writing of bestsellers in this field.

But universities and businesses are not the only environments covered by the heroic model. During the Covid-19 pandemic, Camille Étienne, an environmental activist, made a clip in which she referred to citizens as ‘heroes in pyjamas’, regarding the reduction in pollution. The matrix of success is highly malleable and is able to prepare for the world of tomorrow. This power of metamorphosis has been theorized by sociologists Ève Chiapello and Luc Boltanski in their work Le Nouvel Esprit du Capitalisme (The New Spirit of Capitalism). The strength of capitalism is to incorporate criticism in order to remain in a state of constant innovation. This could also apply to the model of new heroism. “Among the paradoxical orders of the modern hero is the lesson to follow rules and to break them. A bestselling management book advises: ‘Firstly, break all the rules’ – but of course, when you look closely, it is not all the rules. The art of the hero is there, hanging in a precarious balance, which can border on the tragic in times of crisis,” concludes Olivier Fournout.

Rémy Fauvel

CEM, champs électro-magnétiques, EMF, electromagnetic fields

How can we assess the health risks associated with exposure to electromagnetic fields?

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As partners of the European SEAWave project, Télécom Paris and the C2M Chair are developing innovative measurement techniques to respond to public concern about the possible effects of cell phone usage. Funded by the EU to the tune of €8 million, the project will be launched in June 2022 for a period of 3 years. Interview with Joe Wiart, holder of the C2M Chair (Modeling, Characterization and Control of Electromagnetic Wave Exposure).

Could you remind us of the context in which the call for projects ‘Health and Exposure to Electromagnetic Fields (EMF)’ of the Horizon Europe program was launched?

Joe Wiart – The exponential use of wireless communication devices, throughout Europe, comes with a perceived risk associated with electromagnetic radiation, despite the existing protection thresholds (Recommendation 1999/519/CE and Directive 2013/35/UE). With the rollout of 5G, these concerns have multiplied. The Horizon Europe program will help to address these questions and concerns, and will study the possible impacts on specific populations, such as children and workers. It will intensify studies on millimeter-wave frequencies and investigate compliance analysis methods in these frequency ranges. The program will look at the evolution of electromagnetic exposure, as well as the contribution of exposure levels induced by 5G and new variable beam antennas. It will also investigate tools to better assess risks, communicate, and respond to concerns.

What is the challenge of SEAWave, one of the four selected projects, of which Télécom Paris is a partner?

JW – Currently, there is a lot of work, such as that of the ICNIRP (International Commission on Non-Ionizing Radiation Protection), that has been done to assess the compliance of radio-frequency equipment with protection thresholds. This work is largely based on conservative methods or models. SEAWave will contribute to these approaches in exposure to millimeter waves (with in vivo and in vitro studies). These approaches, by design, take the worst-case scenarios and overestimate the exposure. Yet, for a better control of possible impacts, as in epidemiological studies, and without underestimating conservative approaches, it is necessary to assess actual exposure. The work carried out by SEAWave will focus on establishing potentially new patterns of use, estimating associated exposure levels, and comparing them to existing patterns. Using innovative technology, the activities will focus on monitoring not only the general population, but also specific risk groups, such as children and workers.

What scientific contribution have Télécom Paris researchers made to this project that includes eleven Work Packages (WP)?

JW – The C2M Chair at Télécom Paris is involved in the work of four interdependent WPs, and is responsible for WP1 on EMF exposure in the context of the rollout of 5G. Among the eleven WPs, four are dedicated to millimeter waves and biomedical studies, and four others are dedicated to monitoring the exposure levels induced by 5G. The last three are dedicated to project management, but also to tools for risk assessment and communication. The researchers at Télécom Paris will mainly be taking part in the four WPs dedicated to monitoring the exposure levels induced by 5G. They will draw on measurement campaigns in Europe, networks of connected sensors, tools from artificial neural networks and, more generally, methods from Artificial Intelligence.

What are the scientific obstacles that need to be overcome?

JW – For a long time, assessing and monitoring exposure levels has been based on deterministic methods. With the increasing complexity of networks, like 5G, but also with the versatility of uses, these methods have reached their limits. It is necessary to develop new approaches based on the study of time series, statistical methods, and Artificial Intelligence tools applied to the dosimetry of radio frequency fields. Télécom Paris has been working in this field for many years; this expertise will be essential in overcoming the scientific obstacles that SEAWave will face.

The SEAWave consortium has around 15 partners. Who are they and what are your collaborations?

JW – These partners fall into three broad categories. The first is related to engineering: in addition to Télécom Paris, there is, for example, the Aristotle University of Thessaloniki (Greece), the Agenzia Nazionale per le Nuove Tecnologie, l’Energia e lo Sviluppo Economico Sostenibile (Italy), Schmid & Partner Engineering AG (Switzerland), the Foundation for Research on Information Technologies in Society (IT’IS, Switzerland), the Interuniversity Microelectronics Centre (IMEC, Belgium), and the CEA (France). The second category concerns biomedical aspects, with partners such as the IU Internationale Hochschule (Germany), Lausanne University Hospital (Switzerland), and the Fraunhofer-Institut für Toxikologie und Experimentelle Medizin (Germany). The last category is dedicated to risk management. It includes the International Agency for Research on Cancer (IARC, France), the Bundesamt für Strahlenschutz (Germany) and the French National Frequency Agency (ANFR, France).

We will mainly collaborate with partners such as the Aristotle University of Thessaloniki, the CEA, the IT’IS Foundation and the IMEC, but also with the IARC and the ANFR.

The project will end in 2025. In the long run, what are the expected results?

JW – First of all, tools to better control the risk and better assess the exposure levels induced by current and future wireless communication networks. All the measurements that will have been carried out will provide a good characterization of the exposure for specific populations (e.g. children, workers) and will lay the foundations for a European map of radio frequency exposure.

Interview by Véronique Charlet

MP4 for Streaming

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Streaming services are now part of our everyday life, and it’s all thanks to MP4. This computer standard allows videos to be played online and on various devices. Jean-Claude Dufourd and Jean Le Feuvre, researchers in Computer Science at Télécom Paris, have been recognized by the Emmy Awards Academy for their work on this computer format amongst other things.

In 2021 the File Format IT working group of the MPEG Committee received an Emmy Award for its work in developing ISOBMFF. Behind this term lies a computer format that was used as the basis for the development of MP4, the famous video standard we have all encountered when saving a file in the ‘.mp4’ format. “The Emmy’s decision to give an award to the File Format group is justified; this file format has had a great impact on the world of video by creating a whole ecosystem that brings together very different types of research,” explains Jean-Claude Dufourd, a computer scientist at Télécom Paris and a member of the File Format group.

MP4, which can capture sound and also video, “is used for live or on-demand media broadcasting, but not for the real-time broadcasting needed to stream games or video conferences,” explains Jean Le Feuvre, also a computer scientist at Télécom Paris and member of the File Format group. There are several features of this format that have contributed to its success, including the ability to capture long videos like movies, while still remaining very compact.

The smaller the file size, the easier they are to circulate on networks. The compactness of MP4 is therefore an advantage for streaming movies and series.  Another explanation for its success is its adaptability to different types of devices. “This technology can be used on a wide variety of everyday devices such as telephones, computers, and televisions,” explains Jean-Claude Dufourd. The reason that MP4 is playable on different devices is because “the HTTP file distribution protocol has been reused to distribute video,” says the researcher.

Improving streaming quality

The HTTP (Hypertext Transfer Protocol), which has been prevalent since the 1990s, is typically used to create websites. Researchers have modified this protocol so that it can be used to broadcast video files online. Their studies led to the development of HTTP streaming, and then to an improved version called DASH (Dynamic Adaptive Streaming over HTTP), a protocol that “cuts up the information in the MP4 file into chunks of a few seconds each,” says Jean-Claude Dufourd. The segments obtained at the end of this process are successfully retrieved by the player to reconstruct the movie or the episode of the series being watched.

This cutting process allows the playback of the video file to be adjusted according to the connection speed. “For each time range, different quality encoding is provided, and the media player is responsible for deciding which quality is best for its conditions of use,” explains Jean Le Feuvre. Typically, if a viewer’s connection speed is low, the streaming player will select the video file with the least amount of data in order to facilitate traffic. The player will therefore select the lowest streaming quality. This feature allows content to continue playing on the platform with minimal risk of interruption.

In order to achieve this ability to adapt to different usage scenarios, tests have been carried out by scientists and manufacturers. “Tests were conducted to determine the network profile of a phone and a computer,” explains Jean-Claude Dufourd. “The results showed that the profiles were very different depending on the device and the situation, so the content is not delivered with the same fluidity,” he adds.

Economic interests

“Today, we are benefiting from 15 years of technological refinement that have allowed us to make the algorithms efficient enough to stream videos,” says Jean-Claude Dufourd. Since the beginning of streaming, one of the goals has been to broadcast videos with the best possible quality, while also reducing loading lag and putting as little strain on the network capacity as possible.

The challenge is primarily economic; the more strain that streaming platforms put on network capacity to stream their content, the more they have to pay. Currently, people are studying how to reduce the broadcaster’s internet bill. One solution would be to circulate video files mainly among users, thereby creating a less centralized streaming system. This is what file sharing systems allow between users (P2P or Peer-to-Peer networks). This alternative is currently being considered by streaming companies, as it would reduce the cost of broadcasting content.  

Rémy Fauvel

values conception, collective design, valeurs

Learning to incorporate values in collective design

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Designing projects implies that individuals or groups must pool their values to collaborate effectively. But the various parties involved may be guided by diverging value systems, making it difficult to find compromises and common solutions. Françoise Détienne and Michael Baker, researchers at Télécom Paris, explain how the role of values in collective design can be understood.

How is a value defined in the field of collective design?

Françoise Détienne: In general, the concept of values refers to principles or beliefs that guide individuals’ actions and choices. Put that way, any preference might be seen as a value, so we must limit the definition to the ethical dimension in choices, connected to social and human aspects. The notions of inclusion or privacy protection are examples of these kinds of values.

Michael Baker: Certain notions may be considered absolute values in broad terms – like freedom for example – but they can be divided into different nuances, such as freedom of expression or freedom of choice.  And some terms or expressions are subject to implicit value judgments. For example, the word “business” may, in certain contexts, express a negative value judgment, although it refers to something neutral from a values perspective. In order to identify the underlying values in interactions produced in collective design situations, we must therefore go beyond language by taking into account the context in which statements are made.

How can we understand the role of values in the design process?

FD: Most of the current approaches are based on the concepts of Value Sensitive Design (VSD), which consider values to be discrete and independent criteria that must simply be added to the other types of design criteria.  Most of the time, however, individual and collective values are organized into systems that we refer to as ideologies. Here, ideologies mean the set of values underlying individual and collective  viewpoints. We have proposed a new approach called Ideologically Embedded Design (IED), which differentiates between several levels at which values (systems) operate: the form of participation and its underlying principles, the evolution of the design and decision-making process, the group or community involved in the process and its production. This approach also emphasizes the interactions and the possible co-evolution between these levels.

How has the understanding of the role of values in design evolved?

MB : Up to now, values in design have been analyzed based on the objects or physical infrastructure resulting from projects, which reflect certain political and social choices. The analyses carried out based on these objects allowed us to extract values through an ex-post deconstruction. But the current design ergonomics movement seeks instead to analyze how values come into play in the design process and how to deal with value conflicts.

What are some organizations where thinking about values in advance is a priority?

FD: In general, the design of collaborative organizations is rooted in strong values. Participatory housing, which aims to implement shared governance systems, is a good example. The considerations of the individuals involved focus primarily on how they must be organized, based on values that are in line with sharing, such as respect, tolerance and equity in decision-making. In communities like these, the stakes of such values are high, since the goal is to live together successfully.

MB: Many online communities give significant thought to values. One of the best examples is Wikipedia. The Wikipedia community is based on values such as open access to knowledge, free participation of contributors, and neutrality of point of view. Should disagreements rooted in opposing value systems arise, there is not any real way to “resolve” the conflict. In this case, to represent the diversity of viewpoints, the conflict may be handled by dividing the text into different sections, each of which reflects a different viewpoint. For example, an article on “Freud” may be divided into sections that represent the topic from the viewpoint of behavioral psychologists, neuropsychologists, psychoanalysts etc.

Are there discrepancies at times between the values promoted or upheld by an organization and the way they are applied on a concrete level?

MB: There is, indeed, a disconnect at times between the values advanced by an organization and the way they are actually implemented. For example, the notion of “collaboration” may be put forth as a positive value, with various rhetorical uses. For the last decade or so, this term has had a positive connotation and is sometimes used for image and marketing purposes, along the same lines as  greenwashing.  Research is also being carried out on the possible differences and tensions between an organization’s institutional discourse and how groups actually work within the organization.

Are there conflicting values within the same organization at times?

FD: At a certain level of definition of values, this is often the case.  An important issue is clarity in the definition of values during discussions and debates, since each individual may have a different interpretation. So it’s important to support the co-construction of the meaning of values through dialogue, and identify whether or not there are truly competing values.

MB: In discussions about a design, viewpoints must evolve in order to reach a compromise, but that does not mean that each individual’s ideologies will change drastically over time. Almost by definition, it seems, values are stable and typically change only very slowly (except through a radical “conversion”).  So we must understand each individual’s underlying ideologies and frame discussions about the decision-making process by taking them into account. For example, it’s helpful to set out in advance the ways in which the process is collaborative or participative, and if there must be equitable participation between the various stakeholders. The organizational framework is also very  value-oriented.

What are some concrete methods that can help improve collaboration?

FD: Various methods can be applied to improve the alignment and compromise of values within a group. While approaches such as VSD help identify values, ensuring that debates are constructive is not easy. We propose methods from constructive ergonomics such as role playing, organizational simulation and imagining use situations, as well as reflective methods. For example, self-confrontation techniques can be put in place by filming a working group and then having the group members watch the video. This gives them the opportunity to think in a structured way about the  respective underlying values that guided their collective activity. Visualization tools can also help resolve such debates.

How can conflicts be resolved in the event of disagreements about values?

FD : In order to resolve conflicts that may arise, the use of a debate moderator who has been trained in advance for this role can prove to be very helpful. What are referred to as “avoidance” strategies may also be used, such as momentarily redirecting the discussion toward more practical questions, to avoid crystallizing conflicts and opposing viewpoints.

MB: It’s also important to redirect discussion toward compromises that allow different values to coexist. To do so, it can be helpful to bring the debate back to a level focusing on more general values. Sometimes, the more individuals specify what they mean by a value, the more viewpoints may oppose and lead to conflict. 

FD: And last but not least, this leads us to rethink the timeframe for design activity to allow time for co-construction and evolution —which will in all likelihood be slow— of values, negotiation and, possibly, to leave conflict resolution open. The emphasis is then not on producing a solution but on the process itself.

By Antonin Counillon

Gouvernance des données

Data governance: trust it (or not?)

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The original version of this article (in French) was published in the quarterly newsletter no. 20 (March 2021) of the Values and Policies of Personal Information (VP-IP) Chair.

On 25 November 2020, the European Commission published its proposal for the European data governance regulation, the Data Governance Act (DGA) which aims to “unlock the economic and societal potential of data and technologies like artificial intelligence “. The proposed measures seek to facilitate access to and use of an ever-increasing volume of data. As such, the text seeks to contribute to the movement of data between member states of the European Union (as well as with States located outside the EU) by promoting the development of “trustworthy” systems for sharing data within and across sectors.

Part of a European strategy for data

This proposal is the first of a set of measures announced as part of the European strategy for data presented by the European Commission in February 2020. It is intended to dovetail with two other proposed regulations dated on 15 December 2020: the Digital Services Act (which aims to regulate the provision of online services, while maintaining the principle of the prohibition of a surveillance obligation) and the Digital Market Act (which organizes the fight against unfair practices by big platforms against companies who offer services through their platforms). A legislative proposal for the European Health Data Space is expected for the end of 2021 and possibly a “data law.”

The European Commission also plans to create nine shared European data spaces in strategic economic sectors and public interest areas, from the manufacturing industry to energy, or mobility, health, financial data and green deal data. The first challenge to overcome in this new data ecosystem will be to transcend national self-interests and those of the market.  

The Data Governance Act proposal does not therefore regulate online services, content or market access conditions: it organizes “data governance,” meaning the conditions for sharing data, with the market implicitly presumed to be the paradigm for sharing. This is shown in particular by an analysis carried out through the lens of trust (which could be confirmed in many other ways).

The central role of trust

Trust plays a central and strategic role in all of this legislation since the DGA “aims to foster the availability of data for use, by increasing trust in data intermediaries and by strengthening data-sharing mechanisms across the EU.” “Increasing trust”, “building trust”, ensuring a “higher level of trust”, “creating trust”, “taking advantage of a trustworthy environment”, “bringing trust” – these expressions appearing throughout the text point to its fundamental aim.

However, despite the fact that the proposal takes great care to define the essential terms on which it is based (“data“, “reuse”, “non-personal data”, “data holder”, “data user”, “data altruism” etc.), the term “trust,” along with the conditions for ensuring it, are exempt from such semantic clarification – even though “trust” is mentioned some fifteen times.

As in the past with the concept of dignity, which was part of the sweeping declarations of rights and freedoms in the aftermath of the Second World War but was nevertheless undefined –  despite the fact that it is the cornerstone of all bioethical texts, the concept of trust is never made explicit. Lawmakers, and those to whom the obligations established by the legal texts are addressed, are expected to know enough about what dignity and trust are to implicitly share the same understanding. As with the notion of time for Saint Augustine, everyone is supposed to understand what it is, even though they are unable to explain it to someone else.

While some see this as allowing for a certain degree of “flexibility” to adapt the concept of trust to a wide range of situations and a changing society, like the notion of privacy, others see this vagueness – whether intentional or not – at best, as a lack of necessary precision, and at worst, as an undeclared intention.

The implicit understanding of trust

In absolute terms, it is not very difficult to understand the concept of trust underlying the DGA (like in the Digital Services Act in which the European Commission proposes, among other things, a new mysterious category of “trusted flaggers“). To make it explicit, the main objectives of the text must simply be examined more closely.

The DGA represents an essential step for open data. The aim is clearly stated: to set out the conditions for the development of the digital economy by creating a single data market. The goal therefore focuses on introducing a fifth freedom: the free movement of data, after the free movement of goods, services, capital and people.  

While the GDPR created a framework for personal data protection, the DGA proposal intends to facilitate its exchange, in compliance with all the rules set out by the GDPR (in particular data subjects’ rights and consent when appropriate).

The scope of the proposal is broad.

The term data is used to refer to both personal data and non-personal data, whether generated by public bodies, companies or citizens. As a result, interaction with the personal data legislation is particularly significant. Moreover, the DGA proposal is guided by principles for data management and re-use that were developed for research data. The “FAIR” principles for data stipulate that this data must be easy to find, accessible, interoperable and re-usable, while providing for exceptions that are not listed and unspecified at this time.

To ensure trust in the sharing of this data, the category of “data intermediary” is created, which is the precise focus of all the political and legal discourse on trust. In the new “data spaces” which will be created (meaning beyond those designated by the European Commission), data sharing service providers will play a strategic role, since they are the ones who will ensure interconnections between data holders/producers and data users.

The “trust” which the text seeks to increase works on three levels:

  1. Trust among data producers (companies, public bodies data subjects) to share their data
  2.  Trust among data users regarding the quality of this data
  3. Trust among trustworthy intermediaries in the various data spaces

Data intermediaries

This latter group emerges as organizers for data exchange between companies (B2B) or between individuals and companies (C2B). They are the facilitators of the single data market. Without them, it is not possible to create it from a technical viewpoint or make it work. This intermediary position allows them to have access to the data they make available; it must be ensured that they are impartial.

The DGA proposal differentiates between two types of intermediaries: “data sharing service providers,” meaning those who work “against remuneration in any form”  with regard to both personal and non-personal data (Chapter III) and “data altruism organisations” who act “without seeking a reward…for purposes of general interest such as scientific research or improving public services” (Chapter VI).

For the first category, the traditional principle of neutrality is applied.

To ensure this neutrality, which “is a key element to bring trust, it is therefore necessary that data sharing service providers act only as intermediaries in the transactions, and do not use the data exchanged for any other purpose”. This is why data sharing services must be set up as legal entities that are separate from other activities carried out by the service provider in order to avoid conflicts of interest. In the division of digital labor, intermediation becomes a specialization in its own right. To create a single market, we fragment the technical bodies that make it possible, and establish a legal framework for their activities.

In this light, the real meaning of “trust” is “security” – security for data storage and transmission, nothing more, nothing less. Personal data security is ensured by the GDPR; the security of the market here relates to that of the intermediaries (meaning their trustworthiness, which must be legally guaranteed) and the transactions they oversee, which embody the effective functioning of the market.

From the perspective of a philosophical theory of trust, all of the provisions outlined in the DGA are therefore meant to act on the motivation of the various stakeholders, so that they feel a high enough level of trust to share data. The hope is that a secure legal and technical environment will allow them to transition from simply trusting in an abstract way to having trust in data sharing in a concrete, unequivocal way.

It should be noted, however, that when there is a conflict of values between economic or entrepreneurial freedom and the obligations intended to create conditions of trust, the market wins. 

In the impact assessment carried out for the DA proposal, the Commission declared that it would choose neither a high-intensity regulatory intervention option (compulsory certification for sharing services or compulsory authorization for altruism organizations), nor a low-intensity regulatory intervention option (optional labeling for sharing services or voluntary certification for altruism organizations). It opted instead for a solution it describes as “alternative” but which is in reality very low-intensity (lower even, for example, than optional labeling in terms of guarantees of trust). In the end, a notification obligation with ex post monitoring of compliance for sharing services was chosen, along with the simple possibility of registering as an “organisation engaging in data altruism.”

It is rather surprising that the strategic option selected includes so few safeguards to ensure the security and trust championed so frequently by the European Commission champion in its official communication.

An intention based on European “values”

Margrethe Vestager, Executive Vice President of the European Commission strongly affirmed this: “We want to give business and citizens the tools to stay in control of data. And to build trust that data is handled in line with European values and fundamental rights.”

But in reality, the text’s entire reasoning shows that the values underlying the DGA are ultimately those of the market – a market that admittedly respects fundamental European values, but that must entirely shape the European data governance model. This offers a position to take on the data processing business model used by the major tech platforms. These platforms, whether developed in the Silicon Valley ecosystem or another part of the world with a desire to dominate, have continued to gain disproportionate power in light of their business model. Their modus operandi is inherently based on the continuous extraction and complete control of staggering quantities of data.

The text is thus based on a set of implicit reductions that are presented as indisputable policy choices. The guiding principle, trust, is equated with security, meaning security of transactions. Likewise, the European values as upheld in Article 2 of the Treaty on European Union, which do not mention the market, are implicitly related to those that make the market work. Lastly, governance, a term that has a strong democratic basis in principle, which gives the DGA its title, is equated only with the principles of fair market-based sharing, with the purported aim, among other things, to feed the insatiable appetite of “artificial intelligence”.

As for “data altruism,” it is addressed in terms of savings in transaction costs (in this case, costs related to obtaining consent), and the fact that altruism can be carried out “without asking for remuneration” does not change the market paradigm: a market exchange is a market exchange, even when it’s free.

By choosing a particular model of governance implicitly presented as self-evident, the Commission  fails to recognize other possible models that could be adopted to oversee the movement of data.  Just a few examples that could be explored and which highlight the many overlooked aspects of the text, are:

  1.  The creation of a public European public data service
  2. Interconnecting the public services of each European state (based on the eIDAS or Schengen Information System (SIS) model; see also France’s public data service, which presently applies to data created as part of public services by public bodies)
  3. An alternative to a public service: public officials, like notaries or bailiffs, acting under powers delegated by a level of public authority
  4. A market-based alternative: pooling of private and/or public data, initiated and built by private companies.

What kind of data governance for what kind of society?

This text, however, highlights an interesting concept in the age of the “reign of data”: sharing. While data is trivially understood as being the black gold of the 21st century, the comparison overlooks an unprecedented and essential aspect: unlike water, oil or rare metals, which are finite resources, data is an infinite resource, constantly being created and ever-expanding.

How should data be pooled in order to be shared?

Should data from the public sector be made available in order to transfer its value creation to the private sector? Or should public and private data be pooled to move toward a new sharing equation? Will we see the emergence of hybrid systems of values that are evenly distributed or a pooling of values by individuals and companies? Will we see the appearance of a “private data commons”? And what control mechanisms will it include?

Will individuals or companies be motivated to share their data? This would call for quite a radical change in economic culture.

The stakes clearly transcend the simple technical and legal questions of data governance. Since the conditions are those of an infinite production of data, these questions make us rethink the traditional economic model.

It is truly a new model of society that must be discussed. Sharing and trust are good candidates for rethinking the society to come, as long as they are not reduced solely to a market rationale.

The text, in its current form, certainly offers points to consider, taking into account our changing societies and digital practices. The terms, however, while attesting to worthwhile efforts for categorization adapted to these practices, require further attention and conceptual and operational precision.   

While there is undoubtedly a risk of systematic commodification of data, including personal data, despite the manifest wish for sharing, it must also be recognized that the text includes possible advances.  The terms of this collaborative writing  are up to us – provided, of course, that all of the stakeholders are consulted, including citizens, subjects and producers of this data.

Claire Levallois-Barth, lecturer in Law at Télécom Paris, coordinator of the VP-IP chair, co-founder of the VP-IP chair.

Mark Hunyadi, professor of moral and political philosophy at the Catholic University of Louvain (Belgium), member of the VP-IP chair.

Ivan Meseguer, European Affairs, Institut Mines-Télécom, co-founder of the VP-IP chair.

Data visualization

Understanding data by touching it

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Reading and understanding data is not always a simple task. To make it easier, Samuel Huron is developing tools that allow us to handle data physically. The Télécom Paris researcher in data visualization and representation seeks to make complex information understandable to the general public.

Before numbers were used, merchants used clay tokens to perform mathematical operations. These tokens allowed them to represent numerical data in a graphical, physical way, and handle it easily. This kind of token is still used in schools today to help young children become familiar with complex concepts like addition and cardinality. “This very simple tool can open the door to highly complex representations, such as the production of algorithms,” says Samuel Huron, a researcher at Télécom Paris in the fields of data visualization and interactive design.

His work aims to use this kind of simple representation tool to make data understandable to non-experts. “The best way to visualize data is currently programming, but not all of us are computer engineers,” says Samuel Huron. And while providing the entire population with training in programming may be a commendable idea, it is not very realistic. This means that we must trust experts who, despite their best intentions, may provide a subjective interpretation of their observation data.

In an effort to find an alternative, the researcher has taken up the idea of clay tokens. He organizes workshops for people with little or no familiarity with handling data, and proposes using tokens to represent a data set. For example, to represent their monthly budget. Once they have the tokens in their hands, the participants must invent graphical models to represent this data based on what they want to get out of it. “One of the difficult and fundamental things in graphical data analysis is choosing the useful representation for the task, and therefore targeting the visual variables to understand your batch of data,” explains Samuel Huron. “The goal is to teach the participants the concept of visual mapping.”

Video: how does physical representation of data work:

The visualization is not only intended to represent this data, but to develop the capacity to read and make sense of it. Participants must find a way to structure the data themselves. They are then encouraged to think critically by observing the other productions, in particular to see whether they can be read and understood. “In certain workshops with many different data sets, such as the budget of a student, an employed individual, or a retiree, participants can sometimes identify a similar profile just by looking at the representations of other participants,” adds the researcher.

Citizen empowerment 

This transmission method poses real challenges for democracy in our era of digitization of knowledge and the rise of data. To understand the important issues of today and respond to the major challenges we face, we must first understand the data from various fields.  Whether related to budgets, percentage of votes, home energy consumption, or the daily number of Covid-19 cases, all of this knowledge and information is provided in the form of data, either raw or processed to some extent. And to avoid dealing with abstract figures and data, it is represented visually.  Graphs, curves and other diagrams are provided to illustrate this data. But these visual representations are not always understandable to everyone. “In a democracy, we need to understand this data in order to make informed decisions,” says Samuel Huron.

Citizen empowerment is based on the power to make decisions, taking into account complex issues such as climate change or the national budget breakdown. Likewise, to tackle the coronavirus, an understanding of data is required in order to assess risk and implement health measures of varying strictness. It was this societal issue that pushed Samuel Huron to look for data visualization methods that can be used by everyone, with a view to data democratization. This approach includes open data policies and transparency, of course, as well as useful and user-friendly tools that allow everyone to understand and handle this data.

Thinking about the tools

“A distinctive characteristic of human beings is producing representations to process our information,”  says the researcher. “The alphabet is one such example: it’s a graphical encoding to store information that other people can find by reading it.”  Humankind has the capacity to analyze images to quickly identify and examine a set of diagrams, without even thinking at times. These cognitive capacities enable operations in visual space that are otherwise very difficult and allow them to be carried out more quickly than with another kind of encoding, such as numbers.

This is why we tend to illustrate data graphically when we need to explain it. But this is time-consuming and graphs must be updated with each new data set. On the virtual side, there is no shortage of software spreadsheet solutions that allow for dynamic, efficient updates. But they have the drawback of limiting creativity. “Software programs like Excel are great, but all of the possible actions are predefined. Expressiveness of thought is limited by the models offered by the tool,”  says Samuel Huron.

Far from considering tokens to be the ideal solution, the researcher says that they are above all a tool for teaching and raising awareness. “Tokens are a very simple format that make it possible to get started quickly with data visualization, but they remain quite limited in terms of representation,” he says. He is working with his colleagues to develop more complicated workshops with larger data sets that are more difficult to interpret.  In general, these workshops also aim to think about ways to promote the use of data physicalization, with more varied tools and data sets, and therefore more diverse representations. Other studies intend to consider the value of the data rather than that resulting from its handling.

By proposing these data physicalization kits, the researchers can study participants’ thinking. They can therefore better understand how individuals understand, format, handle and interpret data. These observations in turn help the researchers improve their tools and develop new ones that are even more intuitive and user-friendly for different groups of individuals. To go further, the researchers are working on a scientific journal devoted to the topic of data physicalization planned for late 2021. It should  assess the state of the art on this topic, and push research in this area even further. Ultimately, this need to understand digital data may give rise to physical tools to help us grasp complex problems – literally. 

By Tiphaine Claveau.

Eclairer boites noires, algorithms

Shedding some light on black box algorithms

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In recent decades, algorithms have become increasingly complex, particularly through the introduction of deep learning architectures. This has gone hand in hand with increasing difficulty in explaining their internal functioning, which has become an important issue, both legally and socially. Winston Maxwell, legal researcher, and Florence d’Alché-Buc, researcher in machine learning, who both work for Télécom Paris, describe the current challenges involved in the explainability of algorithms.

What skills are required to tackle the problem of algorithm explainability?

Winston Maxwell: In order to know how to explain algorithms, we must draw on different disciplines. Our multi-disciplinary team, AI Operational Ethics, focuses not only on mathematical, statistical and computational aspects, but also on sociological, economic and legal aspects. For example, we are working on an explainability system for image recognition algorithms used, among other things, for facial recognition in airports. Our work therefore encompasses these different disciplines.

Why are algorithms often difficult to understand?

Florence d’Alché-Buc: Initially, artificial intelligence used mainly symbolic approaches, i.e., it simulated the logic of human reasoning. Logical rules, called expert systems, allowed artificial intelligence to make a decision by exploiting observed facts. This symbolic framework made AI more easily explainable. Since the early 1990s, AI has increasingly relied on statistical learning, such as decision trees or neural networks, as these structures allow for better performance, learning flexibility and robustness.

This type of learning is based on statistical regularities and it is the machine that establishes the rules which allow their exploitation. The human provides input functions and an expected output, and the rest is determined by the machine. A neural network is a composition of functions. Even if we can understand the functions that compose it, their accumulation quickly becomes complex. So a black box is then created, in which it is difficult to know what the machine is calculating.

How can artificial intelligence be made more explainable?

FAB: Current research focuses on two main approaches. There is explainability by design where, for any new constitution of an algorithm, explanatory output functions are implemented which make it possible to progressively describe the steps carried out by the neural network. However, this is costly and impacts the performance of the algorithm, which is why it is not yet very widespread. In general, and this is the other approach, when an existing algorithm needs to be explained, it is an a posteriori approach that is taken, i.e., after an AI has established its calculation functions, we will try to dissect the different stages of its reasoning. For this there are several methods, which generally seek to break the entire complex model down into a set of local models that are less complicated to deal with individually.

Why do algorithms need to be explained?

WM: There are two main reasons why the law stipulates that there is a need for the explainability of algorithms. Firstly, individuals have the right to understand and to challenge an algorithmic decision. Secondly, it must be guaranteed that a supervisory institution such as the  French Data Protection Authority (CNIL), or a court, can understand the operation of the algorithm, both as a whole and in a particular case, for example to make sure that there is no racial discrimination. There is therefore an individual aspect and an institutional aspect.

Does the format of the explanations need to be adapted to each case?

WM: The formats depend on the entity to which it needs to be explained: for example, some formats will be adapted to regulators such as the CNIL, others to experts and yet others to citizens. In 2015, an experimental service to deploy algorithms that detect possible terrorist activities in case of serious threats was introduced. For this to be properly regulated, an external control of the results must be easy to carry out, and therefore the algorithm must be sufficiently transparent and explainable.

Are there any particular difficulties in providing appropriate explanations?

WM: There are several things to bear in mind. For example, information fatigue: when the same explanation is provided systematically, humans will tend to ignore it. It is therefore important to use varying formats when presenting information. Studies have also shown that humans tend to follow a decision given by an algorithm without questioning it. This can be explained in particular by the fact that humans will consider from the outset that the algorithm is statistically wrong less often than themselves. This is what we call automation bias. This is why we want to provide explanations that allow the human agent to understand and take into consideration the context and the limits of algorithms. It is a real challenge to use algorithms to make humans more informed in their decisions, and not the other way around. Algorithms should be a decision aid, not a substitute for human beings.

What are the obstacles associated with the explainability of AI?

FAB: One aspect to be considered when we want to explain an algorithm is cyber security. We must be wary of the potential exploitation of explanations by hackers. There is therefore a triple balance to be found in the development of algorithms: performance, explainability and security.

Is this also an issue of industrial property protection?

WM: Yes, there is also the aspect of protecting business secrets: some developers may be reluctant to discuss their algorithms for fear of being copied. Another counterpart to this is the manipulation of scores: if individuals understand how a ranking algorithm, such as Google’s, works, then it would be possible for them to manipulate their position in the ranking. Manipulation is an important issue not only for search engines, but also for fraud or cyber-attack detection algorithms.

How do you think AI should evolve?

FAB: There are many issues associated with AI. In the coming decades, we will have to move away from the single objective of algorithm performance to multiple additional objectives such as explainability, but also equitability and reliability. All of these objectives will redefine machine learning. Algorithms have spread rapidly and have enormous effects on the evolution of society, but they are very rarely accompanied by instructions for their use. A set of adapted explanations must go hand in hand with their implementation in order to be able to control their place in society.

By Antonin Counillon

Also read on I’MTech: Restricting algorithms to limit their powers of discrimination

 

Facial recognition: what legal protection exists?

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Over the past decade, the use of facial recognition has developed rapidly for both security and convenience purposes. This biometrics-based technology is used for everything from video surveillance to border controls and unlocking digital devices. This type of data is highly sensitive and is subject to specific legal framework. Claire Levallois-Barth, a legal researcher at Télécom Paris and coordinator of the Values and Policies of Personal Information Chair at IMT provides the context for protecting this data.

What laws govern the use of biometric data?

Claire Levallois-Barth: Biometric data “for the purpose of uniquely identifying a natural person” is part of a specific category defined by two texts adopted by the 27 Member States of the European Union in April 2016, the General Regulation Data Protection Regulation (GDPR) and the Directive for Police and Criminal Justice Authorities. This category of data is considered highly sensitive.

The GDPR applies to all processing of personal data in both private and public sectors.

The Directive for Police and Criminal Justice Authorities pertains to processing carried out for purposes of prevention, detection, investigation, and prosecution of criminal offences or the execution of criminal penalties by competent authorities (judicial authorities, police, other law enforcement authorities). It specifies that biometric data must only be used in cases of absolute necessity and must be subject to provision of appropriate guarantees for the rights and freedoms of the data subject. This type of processing may only be carried out in three cases: when authorized by Union law or Member State law, when related to data manifestly made public by the data subject, or to protect the vital interests of the data subject or another person.

What principles has the GDPR established?

CLB: The basic principle is that collecting and processing biometric data is prohibited due to significant risks of violating basic rights and freedoms, including the freedom to come and go anonymously. There are, however, a series of exceptions. The processing must fall under one of these exceptions (express consent from the data subject, protection of his or her vital interests, conducted for reasons of substantial public interest) and respect all of the obligations established by the GDPR. The key principle is that the use of biometric data must be strictly necessary and proportionate to the objective pursued. In certain cases, it is therefore necessary to obtain the individual’s consent, even when the facial recognition system is being used on an experimental basis. There is also the minimization principle, which systematically asks, “is there any less intrusive way of achieving the same goal?” In any case, organizations must carry out an impact assessment on people’s rights and freedoms.

What do the principles of proportionality and minimization look like in practice?

CLB: One example is when the Provence-Alpes-Côte d’Azur region wanted to experiment with facial recognition at two high schools in Nice and Marseille. The CNIL ruled that the system involving students, most of whom were minors, for the sole purpose of streamlining and securing access, was not proportionate to these purposes. Hiring more guards or implementing a badge system would offer a sufficient solution in this case.

Which uses of facial recognition have the greatest legal constraints?

CLB: Facial recognition can be used for various purposes. The purpose of authentication is to verify whether someone is who he or she claims to be. It is implemented in technology for airport security and used to unlock your smartphone. These types of applications do not pose many legal problems. The user is generally aware of the data processing that occurs, and the data is usually processed locally, by a card for example.

On the other hand, identification—which is used to identify one person within a group—requires the creation of a database that catalogs individuals. The size of this database depends on the specific purposes. However, there is a general tendency towards increasing the number of individuals. For example, identification can be used to find wanted or missing persons, or to recognize friends on a social network. It requires increased vigilance due to the danger of becoming extremely intrusive.

Facial recognition has finally provided a means of individualizing a person. There is no need to identify the individual–the goal is “simply” to follow people’s movements through the store to assess their customer journey or analyze their emotions in response to an advertisement or while waiting at the checkout. The main argument advertisers use to justify this practice is that the data is quickly anonymized, and no record is kept of the person’s face. Here, as in the case of identification, facial recognition usually occurs without the person’s knowledge.

How can we make sure that data is also protected internationally?

CLB: The GDPR applies in the 27 Member States of the European Union which have agreed on common rules. Data can, however, be collected by non-European companies. This is the case for photos of European citizens collected from social networks and news sites. This is one of the typical activities of the company Clearview AI, which has already established a private database of 3 billion photos.

The GDPR lays down a specific rule for personal data leaving European Union territory: it may only be exported to a country ensuring a level of protection deemed comparable to that of the European Union. Yet few countries meet this condition. A first option is therefore for the data importer and exporter to enter into a contract or adopt binding corporate rules. The other option, for data stored on servers on U.S. territory, was to build on the Privacy Shield agreement concluded between the Federal Trade Commission (FTC) and the European Commission. However, this agreement was invalidated by the Court of Justice of the European Union in the summer of 2020. We are currently in the midst of a legal and political battle. And the battle is complicated since data becomes much more difficult to control once it is exported. This explains why certain stakeholders, such as Thierry Breton (the current European Commissioner for Internal Market), have emphasized the importance of fighting to ensure European data is stored and processed in Europe, on Europe’s own terms.

Despite the risks and ethical issues involved, is facial recognition sometimes seen as a solution for security problems?

CLB: It can in fact be a great help when implemented in a way that respects our fundamental values. It depends on the specific terms. For example, if law enforcement officers know that a protest will be held, potentially involving armed individuals, at a specific time and place, facial recognition can prove very useful at that specific time and place. However, it is a completely different scenario if it is used constantly for an entire region and entire population in order to prevent shoplifting.

This summer, the London Court of Appeal ruled that an automatic facial recognition system used by Welsh police was unlawful. The ruling emphasized a lack of clear guidance on who could be monitored and accused law enforcement officers of failing to sufficiently verify whether the software used had any racist or sexist bias. Technological solutionism, a school of thought emphasizing new technology’s capacity to solve the world’s major problems, has its limitations.

Is there a real risk of this technology being misused in our society?

CLB: A key question we should ask is whether there is a gradual shift underway, caused by an accumulation of technology deployed at every turn. We know that video-surveillance cameras are installed in public roads, yet we do not know about additional features that are gradually added, such as facial recognition or behavioral recognition.  The European Convention of Human Rights, GDPR, the Directive for Police and Criminal Justice Authorities, and the CNIL provide safeguards in this area.

However, they provide a legal response to an essentially political problem. We must prevent the accumulation of several types of intrusive technologies that come without prior reflection on the overall result, without taking a step back to consider the consequences. What kind of society do we want to build together? Especially within the context of a health and economic crisis. The debate on our society remains open, as do the means of implementation.

Interview by Antonin Counillon

The Alicem app: a controversial digital authentication system

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Laura Draetta, Télécom Paris – Institut Mines-Télécom and Valérie Fernandez, Télécom Paris – Institut Mines-Télécom

[dropcap]S[/dropcap]ome digital innovations, although considered to be of general interest, are met with distrust. A responsible innovation approach could anticipate and prevent such confidence issues.

“Alicem” is a case in point. Alicem is a smartphone app developed by the State to offer the French people a national identity solution for online administrative procedures. It uses face recognition as a technological solution to activate a user account and allow the person to prove their digital identity in a secure way.

After its authorization by decree of May 13, 2019 and the launch of the experimentation of a prototype among a group of selected users a few months later, Alicem was due to be released for the general public by the end of 2019.

However, in July of the same year, La Quadrature du Net, an association for the defense of rights and freedoms on the Internet, filed an appeal before the Council of State to have the decree authorizing the system annulled. In October 2019, the information was relayed in the general press and the app was brought to the attention of the general public. Since then, Alicem has been at the center of a public controversy surrounding its technological qualities, potential misuses and regulation, leading to it being put on hold to dispel the uncertainties.

At the start of the summer of 2020, the State announced the release of Alicem for the end of the autumn, more than a year later than planned in the initial roadmap. Citing the controversy on the use of facial recognition in the app, certain media actors argued that it was still not ready: it was undergoing further ergonomic and IT security improvements and a call to tender was to be launched to build “a more universal and inclusive offer” incorporating, among other things, alternative activation mechanisms to facial recognition.

Controversy as a form of “informal” technology assessment

The case of Alicem is similar to that of other controversial technological innovations pushed by the State such as the Linky meters, 5G and the StopCovid app, and leads us to consider controversy as a form of informal technology assessment that defies the formal techno-scientific assessments that public decisions are based on. This also raises the issue of a responsible innovation approach.

Several methods have been developed to evaluate technological innovations and their potential effects. In France, the Technology Assessment – a form of political research that examines the short- and long-term consequences of innovation – is commonly used by public actors when it comes to technological decisions.

In this assessment method, the evaluation is entrusted to scientific experts and disseminated among the general public at the launch of the technology. The biggest challenge with this method is supporting the development of public policies while managing the uncertainties associated with any technological innovation through evidence-based rationality. It must also “educate” the public, whose mistrust of certain innovations may be linked to a lack of information.

The approach is perfectly viable for informing decision-making when there is no controversy or little mobilization of opponents. It is less pertinent, however, when the technology is controversial. A technological assessment focused exclusively on scholarly expertise runs the risk of failing to take account of all the social, ethical and political concerns surrounding the innovation, and thus not being able to “rationalize” the public debate.

Participation as a pillar of responsible innovation

Citizen participation in technology assessment – whether to generate knowledge, express opinions or contribute to the creation and governance of a project – is a key element of responsible innovation.

Participation may be seen as a strategic tool for “taming” opponents or skeptics by getting them on board or as a technical democracy tool that gives voice to ordinary citizens in expert debates, but it is more fundamentally a means of identifying social needs and challenges upstream in order to proactively take them into account in the development phase of innovations.

In all cases, it relies on work carried out beforehand to identify the relevant audiences (users, consumers, affected citizens etc.) and choose their spokespersons. The definition of the problem, and therefore the framework of the response, depends on this identification. The case of Linky meters is an emblematic example: anti-radiation associations were not included in the discussions prior to deployment because they were not deemed legitimate to represent consumers; consequently, the figure of the “affected citizen” was nowhere to be seen during the discussions on institutional validation but is now at the center of the controversy.

Experimentation in the field to define problems more effectively

Responsible innovation can also be characterized by a culture of experimentation. During experimentation in the field, innovations are confronted with a variety of users and undesired effects are revealed for the first time.

However, the question of experimentation is too often limited to testing technical aspects. In a responsible innovation approach, experimentation is the place where different frameworks are defined, through questions from users and non-users, and where tensions between technical efficiency and social legitimacy emerge.

If we consider the Alicem case through the prism of this paradigm, we are reminded that technological innovation processes carried out in a confined manner – first of all through the creation of devices within the ecosystem of paying clients and designers and then through the experimentation of the use of artifacts already considered stable – inevitably lead to acceptability problems. Launching a technological innovation without participation in its development by the users undoubtedly makes the process faster, but may cost its legitimization and even lead to a loss of confidence for its promoters.

In the case of Alicem, the experiments carried out among “friends and family”, with the aim of optimizing the user experience, could be a case in point. This experimentation was focused more on improving the technical qualities of the app than on taking account of its socio-political dimensions (risk of infringing upon individual freedoms and loss of anonymity etc.). As a result, when the matter was reported in the media it was presented through an amalgamation of face recognition technology use cases and anxiety-provoking arguments (“surveillance”, “freedom-killing technology”, “China”, “Social credit” etc.). Without, however, presenting the reality of more common uses of facial recognition which carry the same risks as those being questioned.

These problems of acceptability encountered by Alicem are not circumstantial ones unique to a specific technological innovation, but must be understood as structural markers of the contemporary social functioning. For, although the “unacceptability” of this emerging technology is a threat for its promoters and a hindrance to its adoption and diffusion, it is above all indicative of a lack of confidence in the State that supersedes the reality of the intrinsic qualities of the innovation itself.

This text presents the opinions stated by the researchers Laura Draetta and Valérie Fernandez during their presentation at the Information Mission on Digital Identity of the National Assembly in December 2019. It is based on the case of the biometric authentication app Alicem, which sparked controversy in the public media sphere from the first experiments.

Laura Draetta, a Lecturer in Sociology, joint holder of the Responsibility for Digital Identity Chair, Research Fellow Center for Science, Technology, Medicine & Society, University of California, Berkeley, Télécom Paris – Institut Mines-Télécom and Valérie Fernandez, Professor of Economics, Holder of the Responsibility for Digital Identity chair, Télécom Paris – Institut Mines-Télécom

This article was republished from The Conversation under the Creative Commons license. Read the original article here.