algorithms

Restricting algorithms to limit their powers of discrimination

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From music suggestions to help with medical diagnoses, population surveillance, university selection and professional recruitment, algorithms are everywhere, and transform our everyday lives. Sometimes, they lead us astray. At fault are the statistical, economic and cognitive biases inherent to the very nature of the current algorithms, which are supplied with massive data that may be incomplete or incorrect. However, there are solutions for reducing and correcting these biases. Stéphan Clémençon and David Bounie, Télécom ParisTech researchers in machine learning and economics, respectively, recently published a report on the current approaches and those which are under exploration.

 

Ethics and equity in algorithms are increasingly important issues for the scientific community. Algorithms are supplied with the data we give them including texts, images, videos and sounds, and they learn from these data through reinforcement. Their decisions are therefore based on subjective criteria: ours, and those of the data supplied. Some biases can thus be learned and accentuated by automated learning. This results in the algorithm deviating from what should be a neutral result, leading to potential discrimination based on origin, gender, age, financial situation, etc. In their report “Algorithms: bias, discrimination and fairness”, a cross-disciplinary team[1] of researchers at Télécom ParisTech and the University of Paris Nanterre investigated these biases. They asked the following basic questions: Why are algorithms likely to be distorted? Can these biases be avoided? If yes, how can we minimize them?

The authors of the report are categorical: algorithms are not neutral. On the one hand, because they are designed by humans. On the other hand, because “these biases partly occur because the learning data lacks representativity” explains David Bounie, researcher in economics at Télécom ParisTech and co-author of the report. For example: the recruitment algorithm for the giant Amazon was heavily criticized in 2015 for having discriminated against female applicants. At fault, was an imbalance in the history of the pre-existing data. The people recruited in the previous ten years were primarily men. The algorithm had therefore been trained by a gender-biased learning corpus. As the saying goes, “garbage in, garbage out”. In other words, if the input data is of poor quality, the output will be poor too.

Also read Algorithmic bias, discrimination and fairness

Stéphan Clémençon is a researcher in machine learning at Télécom Paristech and co-author of the report. For him, “this is one of the growing accusations made of artificial intelligence: the absence of control over the data acquisition process.” For the researchers, one way of introducing equity into algorithms is to contradict them. An analogy can be drawn with surveys: “In surveys, we ensure that the data are representative by using a controlled sample based on the known distribution of the general population” says Stéphan Clémençon.

Using statistics to make up for missing data

From employability to criminality or solvency, learning algorithms have a growing impact on decisions and human lives. These biases could be overcome by calculating the probability that an individual with certain characteristics is included in the sample. “We essentially need to understand why some groups of people are under-represented in the database” the researchers explain. Coming back to the example of Amazon, the algorithm favored applications from men because the recruitments made over the last ten years were primarily men. This bias could have been avoided by realizing that the likelihood of finding a woman in the data sample used was significantly lower than the distribution of women in the population.

“While this probability is not known, we need to be able to explain why an individual is in the database or not, according to additional characteristics” adds Stéphan Clémençon. For example, when assessing banking risk, algorithms use data on the people eligible for a loan at a particular bank to determine the borrower’s risk category. These algorithms do not look at applications by people who were refused a loan, who have not needed to borrow money or who obtained a loan in another bank. In particular, young people under 35 years old are systematically assessed as carrying a higher level of risk than their elders. Identifying these associated criteria would make it possible to correct the biases.

Controlling data also means looking at what researchers call “time drift”. By analyzing data over very short periods of time, an algorithm may not account for certain characteristics of the phenomenon being studied. It may also miss long-term trends. By limiting the duration of the study, it will not pick up on seasonal effects or breaks. However, some data must be analyzed on the fly as they are collected. In this case, when the time scale cannot be extended, it is essential to integrate equations describing potential developments in the phenomena analyzed, to compensate for the lack of data.

The difficult issue of equity in algorithms

Other than the possibility of using statistics, researchers are also looking at developing algorithmic equity. This means developing algorithms which meet equity criteria according to attributes protected under law such as ethnicity, gender or sexual orientation. As for statistical solutions, this means integrating constraints into the learning program. For example, it is possible to impose that the probability of a particular algorithmic result will be equal for all individuals belonging to a particular group. It is also possible to integrate independence between the result and a type of data, such as gender, income level, geographical location, etc.

But which equity rules should be adopted? For the controversial Parcoursup algorithm for higher education applications, several incompatibilities were raised. “Take the example of individual equity and group equity. If we consider only the criterion of individual equity, each student should have an equal chance at success. But this is incompatible with the criterion of group equity, which stipulates that admission rates should be equal for certain protected attributes, such as gender” says David Bounie. In other words, we cannot give an equal chance to all individuals regardless of their gender and, at the same time, apply criteria of gender equity. This example illustrates a concept familiar to researchers: the rules of equity contradict each other and are not universal. They depend on ethical and political values that are specific to individuals and societies.

There are complex, considerable challenges facing social acceptance of algorithms and AI. But it is essential to be able to look back through the algorithm’s decision chain in order to explain its results. “While this is perhaps not so important for film or music recommendations, it is an entirely different story for biometrics or medicine. Medical experts must be able to understand the results of an algorithm and refute them where necessary” says David Bounie. This has raised hopes of transparency in recent years, but is no more than wishful thinking. “The idea is to make algorithms public or restrict them in order to audit them for any potential difficulties” the researchers explain. However, these recommendations are likely to come up against trade secret and personal data ownership laws. Algorithms, like their data sets, remain fairly inaccessible. However, the need for transparency is fundamentally linked with that of responsibility. Algorithms amplify the biases that already exist in our societies. New approaches are required in order to track, identify and moderate them.

[1] The report (in French) Algorithms: bias, discrimination and equity was written by Patrice Bertail (University of Paris Nanterre), David Bounie, Stephan Clémençon and Patrick Waelbroeck (Télécom ParisTech), with the support of Fondation Abeona.

Article written for I’MTech by Anne-Sophie Boutaud

To learn more about this topic:

Ethics, an overlooked aspect of algorithms?

Ethical algorithms in health: a technological and societal challenge

cave paintings

The hidden secrets of the colors of cave paintings at prehistoric sites

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The colors of cave paintings are of great interest because they provide information about the techniques and materials used. Studying them also allows fewer sample to be taken from ancient paleolithic works. Research in colorimetry by Dominique Lafon-Pham at IMT Mines Alès provides a better definition of the colors used in paintings by our ancestors.

 

Mammoths, steppe lions and woolly rhinoceroses have been extinct for thousands of years, but they have by no means disappeared from paleolithic caves. Paintings of these animals still remain on the walls of the caves that our ancestors once lived in or travelled to. For archeologists, cave art specialists and paleo-anthropologists, these paintings are a valuable source of information. Cave art, found at various sites in different regions and dating from a long period that covers several tens of thousands of years, reflects the distribution and evolution of prehistoric wildlife. Analysis of the complex scenes sometimes depicted — such as hunting — and study of the artistic techniques used also bear valuable witness to paleolithic social practices. They are an expression of the symbolic world of our ancestors.

Scientists examine and handle these works with minute care. “Permission to take samples of the painted works is only granted after a strict application process and remains exceptional. Decorated caves can contain a wealth of information but are also be extremely restrictive due to the fragility of the information itself,” explains Dominique Lafon-Pham. The researcher at IMT Mines Alès is developing measurement methods that do not require contact with the color and which help characterize rock paintings. She has been carrying out her work for several years in close collaboration with the French National Center for Prehistory (CNP). She alternates field work and lab experiments in partnership with Stéphane Konik, geoarcheologist at the CNP attached to the PACEA[1] laboratory.

“Colorimetric analysis isn’t a replacement for chemical and mineralogical methods of analysis”, Dominique Lafon-Pham stresses. In certain cases it does, however, provide initial information on the nature of the colorant material. The color alone is not enough to accurately trace the constituents of the mixes, but it does provide a clue. Comparing the colors in different works is a way to avoid taking samples of the pictorial layer from the walls of prehistoric caves. The researcher’s work helps contribute to a “detective investigation” led by archaeologists at scenes dating from several tens of thousands of years ago, where even the smallest piece of evidence merits examination.

The color and, more generally, the appearance of the drawings observed by teams of scientists in caves such as Chauvet and Cussac tell us some of the history of the chosen colorant material that was prepared and applied and has been exposed to the passing of time. It is a way of entering into the work through analysis of the ancient material used. Data produced from this analysis may allow parietal archaeologists to approach the work from the perspective of its creation and even its purpose, whereas conservation specialists are more interested in its evolution over time.

Our visual ability does not allow us to compare subtle differences in color that do not fall within our visual range. We do not have perfect color memory. In addition, the impressions created by an area of color are influenced by the surrounding chromatic environment. “When we can measure the color of a mark without the problem of deterioration due to aging, we will be able to establish similarities between works of the same color, whether they are on the same rock wall or not,” indicates the researcher at IMT Mines Alès.

Objectifying the perception of colors

This comparative method may seem a simple one, but it is important not to underestimate the complexity of the site. Lighting — very often artificial — alters the perception of the human eye. A colored surface will not appear the same when lit in two different ways. The aging of the rock also has an impact. The calcite that forms in the caves sometimes covers the paintings and alters the optical performance of the material, dulling and modifying the color of the depictions. In addition, moisture conditions vary with the seasons and between different sectors at a single site, leading to reversible variation in the colors perceived and measured. All these different impacts require set procedures to be put in place to identify, in the most objective way possible, the color produced by the interaction between light and material.

Measuring the color of cave paintings is not an easy task. Researchers use spectroradiometry and a whole set of associated procedures to keep the lighting constant for each measurement, as seen here in the cave of Chauvet.

 

Researchers use a spectroradiometer, an instrument that measures the spectral power distribution of a luminous radiance in the range of visible light, which is a physical scale that has no correlation with the color perceived by the eye. “The advantage of working at an underground site is that we can control the lighting of rock paintings,” explains Dominique Lafon-Pham. “We always try and light the work in the same way.” The situation becomes more complex when the scientists need to work outside. “We are currently taking measurements at the site of the Cro-magnon rock shelter,” explains the researcher. This site, listed as UNESCO World Heritage, is located in Dordogne in France and was a shelter for Cro-magnon men approximately 30,000 years ago. “The analysis of potentially decorated rock walls which are exposed to the open air is much more complex due to changes in the natural light. It is a real challenge in this situation to try and distinguish between very similar colors using measurements.”

Towards virtual caves?

The use of mixed reality (part-way between augmented reality and virtual reality) at cultural sites is increasingly common. This technology opens up new possibilities for transmitting knowledge such as through the creation of remote guided tours in an entirely virtual environment. The quality of the cultural mediation depends on the realism and exactitude of the features and objects in the virtual world. Taking objectified measurements allows standardization of data collection on the optical characteristics of the parietal art at prehistoric sites. Data collected in this way can be processed using modelling and realistic simulation tools. It provides some of the information required for the construction of virtual facsimile.

The scientific community is also keeping a close eye on such devices which capitalize on new media technology. Highly accurate virtual replicas of prehistoric sites could offer considerable research opportunities by enabling researchers to access sites regardless of how easily accessible they are or not. For conservation and safety reasons — such as the presence of high levels of CO2 in the air at certain times of the year — it is only possible to access caves for very short periods of time and under strict control of movement. Although Dominique Lafon-Pham agrees that these are particularly promising prospects, she nevertheless tempers expectations: “For the moment, the image generators we have tested are a long way off being able to render the subtlety of light and color variations that we see in reality.”

It will be a little longer before it is possible to create identical virtual replicas of paleolithic caves and their art with sufficient realism to allow quality cultural and scientific mediation. Nevertheless, this doesn’t stop the researchers at Mines Alès continuing to study the colors of rock paintings and, in particular, the way they looked at the time of our ancestors. 30,000 years ago, our predecessors painted and viewed their art by firelight, which has been replaced in caves today by very different electric lighting. “The light cast by fire flickers: what does that mean for the way in which the painted or engraved work is seen and perceived?” wonders Dominique Lafon-Pham. Another question: if researchers today are able to detect multiple shades of red in a single drawing using these systems of measurement, were these different shades seen by our Homo sapiens ancestors? If so, were they accidental or deliberate and did they serve a purpose for the artist?

[1] “From Prehistory to Today: Culture, Environment and Anthropology” (PACEA) laboratory. A mixed research unit attached to the CNRS, the University of Bordeaux and the French Ministry of Culture and Communication.

Digital twins in the health sector: mirage or reality?

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Digital twins, which are already well established in industry, are becoming increasingly present in the health sector. There is a wide range of potential applications for both diagnosis and treatment, but the technology is mostly still in the research phase.

 

The health sector is currently undergoing digital transition with a view to developing “4P” treatment: personalized, predictive, preventive and participative. Digital simulation is a key tool in these changes. It consists in creating a model of an object, process or physical process on which different hypotheses can be tested. Today, patients are treated when they fall sick based on clinical studies of tens or, at best, thousands of other people, with no real consideration of the individual’s personal characteristics. Will each person one day have their own digital twin to allow prediction of the development of acute or chronic diseases based on their genetic profile and environmental factors, and anticipation of their response to different treatments?

“There are a lots of hopes and dreams built on this idea,” admits Stéphane Avril, Researcher at Mines Saint-Étienne. “The profession of doctor or surgeon is currently practiced as an art on the strength of experience acquired over time. The idea is that a program could combine the experience of a thousand doctors, but in reality there is a huge amount of work still to do to create equations for and integrate non-mathematical knowledge and skills in very different fields such as immunology and the cardio-vascular system.”  We are still a long way from simulating an entire human being. “But in certain fields, digital twins provide excellent predictions that are even better than those of a practitioner,” adds Stéphane Avril.

From imaging to the operating room

Biomechanics, for example, is a field of research that lends itself very well to digital simulation. Stéphane Avril’s research addresses aortic aneurysms. 3D digital twins of the affected area are developed based on medical images. The approach has led to the creation of a start-up called Predisurge, whose software allows the creation of individual endoprostheses. “The FDA [American Food and Drug Administration] is encouraging the use of digital simulation for validating the market entry of prostheses, both orthopedic and vascular,” explains the researcher from St Etienne.

Digital simulation also helps surgeons prepare for operations because the software provides predictions on the insertion and effects of these endoprostheses once in place, as well as simulating any pre-surgical complications that could arise. “This technique is currently still in the testing and validation phase, but it could have a very promising impact on reducing surgery time and complications,” stresses Stéphane Avril. The team at Mines Saint-Étienne is currently working on improving our understanding of the properties of the aortic wall using four-dimensional MRI and mechanical tests on aneurysm tissue removed during the insertion of prostheses. The idea is to validate a digital twin designed using 4D MRI images, which could predict the future rupture or stability of an aneurysm and indicate the need for surgery or not.

Read more on I’MTech: A digital twin of the aorta to prevent Aneurysm rupture

Catalin Fetita, a researcher at Télécom SudParis, also uses digital simulation in the field of imaging, but this time in the case of air-borne transmission alongside pulmonary parenchyma analysis. The aim of her work is to obtain biomarkers from medical images for a more precise definition of pathological phenomena in respiratory diseases such as asthma, chronic obstructive pulmonary disease (COPD) and idiopathic interstitial pneumonia (IIP). The model allows assessment of an organ’s functioning based on its morphology. “Digital simulation is used to identify the type of dysfunction and its exact location, quantify it, predict changes in the disease and optimize the treatment process,” explains Catalin Fetita.

Ethical and technical barriers

The problem of data security and anonymity is currently at the heart of ethical and legal debates. For the moment, researchers are having great difficulty accessing databases to “feed” their programs. “To obtain medical images, we have to establish a relationship of trust with a hospital radiologist, get them interested in our work and involve them in the project. We need images to be precisely labeled for the model to be relevant.” Especially since the analysis of medical images can vary from one radiologist to another. “Ideally, we would like to have access to a database of images that have been analyzed by a panel of experts with a consensus on their interpretation,” Catalin Fetita affirms.

The researcher also points to the lack of technical staff. Models are generally developed in the framework of a thesis, and rarely lead to a finished product. “We need a team of research or development engineers to preserve the skills acquired, ensure technology transfer and carry out monitoring and updates.” Imaging techniques are evolving and algorithms can encounter difficulties in processing new images that sometimes have different characteristics.

For Stéphane Avril, a new specialization in engineering and health with mixed skills is needed. “These tools will transform doctors’ and surgeons’ professions, but it’s still a bit like science fiction to practitioners at the moment. The transformation will take place tentatively, with restraint, because full medical training takes more than 10 years.” The researcher thinks that it will be another ten years or so before the tools to integrate the systemic aspect of physiopathology will be operational: “like for self-driving vehicles, the technology exists but there are still quite a few stages to go before it actually arrives in hospitals.”

 

Article written by Sarah Balfagon for I’MTech.

 

The TeraLab data machines.

TeraLab: data specialists serving companies

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Belles histoires, bouton, CarnotTeraLab is a Big Data and artificial intelligence platform that grants companies access to a whole ecosystem of specialists in these fields. The aim is to remove the scientific and technological barriers facing organizations that want to make use of their data. Hosted by IMT, TeraLab is one of the technology platforms proposed by the Carnot Télécom & Société Numérique. Anne-Sophie Taillandier, Director of TeraLab, presents the platform.

 

What is the role of the TeraLab platform?

Anne-Sophie Taillandier: We offer companies access to researchers, students and innovative enterprises to remove technological barriers in the use of their data. We provide technical resources, infrastructure, tools and skills in a controlled, secure and neutral workspace. Companies can prototype products or services in realistic environments with a view to technology transfer as fast as possible.

In what ways do you work with companies?

AST: First of all, we help them formalize the use case. Companies often come to us with a vague outline of the use case, so we help them with that and can provide specialist contributions if necessary. This is a crucial stage because our aim is also for companies to be able to assess the return on investment at the end of the research or innovation work. It helps them estimate the investment required to launch production, so the need must be clearly defined. We then help them understand what they have the right to do with the data. There again we can call upon expert legal advice if necessary. Lastly, we support them in the specification of the technical architecture.

How do you stand out from other Big Data and artificial intelligence service platforms?

AST: Firstly, by the ecosystem we benefit from. TeraLab is associated with IMT, so we have a number of specialist researchers in these fields as well as students we can mobilize to resolve technological challenges posed by companies. Secondly, TeraLab is a pre-competitive platform. We can also define a framework that brings together legal and technical aspects to meet companies’ needs in an individual way. We can strike a fairly fine balance between safety and flexibility to reassure the organizations who come to us and at the same time give researchers enough space to find solutions to the problems posed.

What level of technical security can you provide?

AST: We can reach an extremely high level of technical security, where the user of the data supplied, such as the researcher, can see it but never extract it. Generally speaking, a validation process involving the data supplier and the Teralab team must be followed in order to extract a piece of data from the workspace. During a project, data security is guaranteed by a combination of technical and legal factors. Moreover, we work in a neutral and controlled space which also provides a form of independence that reassures companies.

What does neutrality mean for you?

AST: The technical components we propose are open source. We have nothing against products under license, but if a company wants to use a specific tool, it must provide the license itself. Our technical team has excellent knowledge of the different libraries and APIs as well as the components required to set up a workspace. They adapt the tools to the company’s needs. We do not host the service beyond the end of the experimentation phase. Instead, we enter a new phase of technology transfer to allow the products or services to be integrated at the client’s end. We therefore have nothing to “sell” except our expertise. This also guarantees our neutrality.

What use cases do you work on?

AST: Since we started TeraLab, more than 60 projects have come through the platform, and there are currently 20 on the go. They can last between 3 months and 3 years. We have had projects in logistics, insurance, public services, energy, mobility, agriculture etc. At the moment, we are focusing on three sectors. The first is cybersecurity: we are interested in seeing what data access barriers there are, how to make a workspace compliant, and how to guarantee respect of personal data. We also work a lot in the health sector and industry. Geographically speaking, we are increasingly working at a European level in the framework of H2020 projects. The platform also benefits from growing recognition among European institutions with, in particular, the “Silver i-space” label awarded by the BDVA.

Physically, what does TeraLab look like?

AST: TeraLab comprises machines at Douai, a technical team in Rennes and a business team in Paris. The platform is accessible remotely, so there is no need to be physically close to it, making it different to other service platforms. We have recently also been able to secure client machines directly on site if the client has specific restrictions with regard to the movement of data.

 

User immersion, between 360° video and virtual reality

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I’MTech is dedicating a series of success stories to research partnerships supported by the Télécom & Société Numérique (TSN) Carnot Institute, which the IMT schools are a part of.

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To better understand how users interact in immersive environments, designers and researchers are comparing the advantages of 360° video and full-immersion virtual reality. This is also the aim of the TroisCentSoixante inter-Carnot project uniting the Télécom & Société Numérique and the M.I.N.E.S. Carnot Institutes. Strate Research, the research department at Strate School of Design which is a member of the Carnot TSN, is studying this comparison in particular in the case of museography mediation.  

 

When it comes to designing immersive environments, designers have a large selection of tools available to them. Mixed reality, in which the user is plunged into a more or less interactive environment, covers everything from augmented video to fully synthetic 3D images. To determine which is the best option, researchers from members of the TSN Carnot Institute (Strate School of Design) and the M.I.N.E.S Carnot Institute (Mines ParisTech and IMT Mines Alès) have joined forces. They have compared, for different use cases, the differences in user engagement between 360° video and full 3D modeling, i.e. virtual reality.

“At the TSN Carnot Institute we have been working on the case of a museum prototype alongside engineers from Softbank Robotics, who are interested in the project,” explains Ioana Ocnarescu, researcher at Strate. A room containing exhibits such as a Minitel, tools linked to the development of the internet, photos of famous researchers in robotics and robots has been created at Softbank Robotics to create mediation on science and technology. Once the object is in place, a 3D copy is made and a visit route is laid out between the different exhibits. This base scenario is used to film a 360° video guided by a mediator and to create a virtual guide in the form of a robot called Pepper, which travels around the 3D scene with the viewer. In both cases, the user is immersed in the environment using a mixed reality headset.

Freedom or realism: a choice to be made

Besides the graphics, which are naturally different between video and 3D modelling, the two technologies have one fundamental difference: freedom of action in the scenario. “In 360° video the viewer is passive,” explains Ioana Ocnarescu. “They follow the guide and can zoom in on objects, but cannot move around freely as they wish.” Their movement is limited to turning their head and deciding to spend longer on certain objects than others. To allow this, the video is cut in several places allowing a decision tree to be made that leads to specific sequences depending on the user’s choices.

Like the 3D mediation, the 360°-video trial mediation is guided by a robot called Pepper.

Like the 3D mediation, the 360°-video trial mediation is guided by a robot called Pepper.

 

3D modeling, on the other hand, grants a large amount of freedom to the viewer. They can move around freely in the scene, choose whether to follow the guide or not, walk around the exhibits and look at them from any angle, which is where 360° video is limited by the position of the camera. “User feedback shows that certain content is better suited to one device or the other,” the Strate researcher reports. For a painting or a photo, for example, there is little use in being able to travel around the object, and the viewer prefers to be in front of the exhibit in it its surroundings with as much realism as possible. “360° video is therefore better adapted for museums with corridors and paintings on the walls,” she points out. On the other hand, 3D modeling is particularly adapted to looking at and examining 3D artefacts such as statues.

These experiments are extremely useful to researchers in design, in particular because they involve real users. “Knowing what people do with the devices available is at the heart of our reflection,” emphasizes Ioana Ocnarescu. Strate has been studying user-machine interaction for over 5 years to develop more effective interfaces. In this project, the people in immersion can give their feedback directly to the Strate team. “It is the most valuable thing in our work. When everything is controlled in a laboratory environment, the information we collect is less meaningful.”

The tests must continue to incorporate a maximum amount of feedback from as many different types of audience as possible. Once finished, the results will be compared with those of other use cases explored by the M.I.N.E.S Carnot Institute. “Mines ParisTech and IMT Mines Alès are comparing the same two devices but in the case of self-driving cars and exploration of the Chauvet cave,” explains the researcher.

 

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Carnot TSN, a guarantee of excellence in partnership-based research since 2006

The Télécom & Société numérique (TSN) Carnot Institute has partnered companies in their research to develop digital innovations since 2006. On the strength of over 1,700 researchers and 50 technology platforms, it offers cutting-edge research to resolve complex technological challenges produced by digital, energy and environmental and industrial transformations within the French production fabric. It addresses the following themes: Industry of the Future, networks and smart objects, sustainable cities, mobility, health and security.

The TSN Carnot Institute is composed of Télécom ParisTech, IMT Atlantique, Télécom SudParis, Institut Mines-Télécom Business School, Eurecom, Télécom Physique Strasbourg, Télécom Saint-Étienne, École Polytechnique (Lix and CMAP laboratories), Strate School of Design and Femto Engineering.

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Since the enthusiasm for AI in healthcare brought on by IBM’s Watson, many questions on bias and discrimination in algorithms have emerged. Photo: Wikimedia.

Ethical algorithms in health: a technological and societal challenge

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The possibilities offered by algorithms and artificial intelligence in the healthcare field raise many questions. What risks do they pose? How can we ensure that they have a positive impact on the patient as an individual? What safeguards can be put in place to ensure that the values of our healthcare system are respected?

 

A few years ago, Watson, IBM’s supercomputer, turned to the health sector and particularly oncology. It has paved the way for hundreds of digital solutions, ranging from algorithms for analyzing radiology images to more complex programs designed to help physicians in their treatment decisions. Specialists agree that these tools will spark a revolution in medicine, but there are also some legitimate concerns. The CNIL, in its report on the ethical issues surrounding algorithms and artificial intelligence, stated that they “may cause bias, discrimination, and even forms of exclusion”.

In the field of bioethics, four basic principles were announced in 1978: justice, autonomy, beneficence and non-maleficence. These principles guide research on the ethical questions raised by new applications for digital technology. Christine Balagué, holder of the Smart Objects and Social Networks chair at the Institut Mines-Télécom Business School, highlights a pitfall, however: “the issue of ethics is tied to a culture’s values. China and France for example have not made the same choices in terms of individual freedom and privacy”. Regulations on algorithms and artificial intelligence may therefore not be universal.

However, we are currently living in a global system where there is no secure barrier to the dissemination of IT programs. The report made by the CCNE and the CERNA on digital technology and health suggests that the legislation imposed in France should not be so stringent as to restrict French research. This would come with the risk of pushing businesses in the healthcare sector towards digital solutions developed by other countries, with even less controlled safety and ethics criteria.

Bias, value judgments and discrimination

While some see algorithms as flawless, objective tools, Christine Balagué, who is also a member of CERNA and the DATAIA Institute highlights their weaknesses: “the relevance of the results of an algorithm depends on the information it receives in its learning process, the way it works, and the settings used”. Bias may be introduced at any of these stages.

Firstly, in the learning data: there may be an issue of representation, like for pharmacological studies, which are usually carried out on 20-40-year-old Caucasian men. The results establish the effectiveness and tolerance of the medicine for this population, but are not necessarily applicable to women, the elderly, etc. There may also be an issue of data quality: their precision and reliability are not necessarily consistent depending on the source.

Data processing, the “code”, also contains elements which are not neutral and may reproduce value judgments or discriminations made by their designers. “The developers do not necessarily have bad intentions, but they receive no training in these matters, and do not think of the implications of some of the choices they make in writing programs” explains Grazia Cecere, economics researcher at the Institut Mines-Télécom Business School.

Read on I’MTech: Ethics, an overlooked aspect of algorithms?

In the field of medical imaging, for example, determining an area may be a subject of contention: A medical expert will tend to want to classify uncertain images as “positive” so as to avoid missing a potential anomaly which could be cancer, which therefore increases the number of false-positives. On the contrary, a researcher will tend to maximize the relevance of their tool in favor of false-negatives. They do not have the same objectives, and the way data are processed will reflect this value judgment.

Security, loyalty and opacity

The security of medical databases is a hotly-debated subject, with the risk that algorithms may re-identify data made anonymous and may be used for malevolent or discriminatory purposes (by employers, insurance companies, etc.). But the security of health data also relies on individual awareness. “People do not necessarily realize that they are revealing critical information in their publications on social networks, or in their Google searches on an illness, a weight problem, etc.”, says Grazia Cecere.

Applications labeled for “health” purposes are often intrusive and gather data which could be sold on to potentially malevolent third parties. But the data collected will also be used for categorization by Google or Facebook algorithms. Indeed, the main purpose of these companies is not to provide objective, representative information, but rather to make profit. In order to maintain their audience, they need to show that audience what it wants to see.

The issue here is in the fairness of algorithms, as called for in France in 2016 in the law for a digital republic. “A number of studies have shown that there is discrimination in the type of results or content presented by algorithms, which effectively restricts issues to a particular social circle or a way of thinking. Anti-vaccination supporters, for example, will see a lot more publications in their favor” explains Grazia Cecere. These mechanisms are problematic, as they get in the way of public health and prevention messages, and the most at-risk populations are the most likely to miss out.

The opaque nature of deep learning algorithms is also an issue for debate and regulation. “Researchers have created a model for the spread of a virus such as Ebola in Africa. It appears to be effective. But does this mean that we can deactivate WHO surveillance networks, made up of local health professionals and epidemiologists who come at a significant cost, when no-one is able to explain the predictions of the model?” asks Christine Balagué.

Researchers from both hard sciences and social sciences and humanities are looking at how to make these technologies responsible. The goal is to be able to directly incorporate a program which will check that the algorithm is not corrupt and will respect the principles of bioethics. A sort of “responsible by design” technology, inspired by Asimov’s three laws of robotics.

 

Article initially written in French by Sarah Balfagon, for I’MTech.

An example of the micro-structures produced using a single-beam laser nano printer by the company Multi-Photon Optics, a member of the consortium.

Nano 3D Printers for Industry

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Projets européens H2020The 3-year H2020 project PHENOmenon, launched in January 2018, is developing nano 3D printers capable of producing micro and nano-structures (particularly those with an optical function), while adhering to limited production times. Kevin Heggarty is a researcher at IMT Atlantique, one of the project partners along with three other European research institutes and eight industrial partners, including major groups and SMEs. He offers a closer look at this project and the scientific challenges involved.

 

What is the goal of the H2020 PHENOmenon project?

Kevin Heggarty: The goal of this project is to develop nano 3D printers for producing large, high-resolution objects. The term “large” is relative, since here we are referring to objects that only measure a few square millimeters or centimeters with nanometric resolution—one nanometer measures one millionth of a millimeter. We want to be able to produce these objects within time frames compatible with industry requirements.

What are the scientific obstacles you must overcome?

KH: Currently there are nano 3D printers that work with a single laser beam. The manufacturing times are very long. The idea with PHENOmenon is first to project hundreds of laser beams at the same time. We are currently able to simultaneously project over one thousand. The long-term goal is to project millions of laser beams to significantly improve production speeds.

What inspired the idea for this project?

KH: Parallel photoplotting is an area of expertise that has been developed in IMT Atlantique laboratories for over 15 years. This involves using light beams to trace patterns on photosensitive materials, like photographic film. Up until now, this was done using flat surfaces. The chemistry laboratory of ENS Lyon has developed highly sensitive material used to produce 3D objects. It was in our collaboration with this laboratory that we decided to test an idea—that of combining parallel photoplotting with the technology from ENS Lyon to create a new manufacturing process.

After demonstrating that it was possible to obtain hundreds of cubic microns by simultaneously projecting a large number of laser beams on highly sensitive material, we reached out to AIMEN, an innovation and technology center specialized in advanced manufacturing materials and technologies located in Vigo, Spain. Their cutting-edge equipment for laser machining is well-suited to the rapid manufacturing of large objects. With its solid experience in applying for and leading European projects, AIMEN became the coordinator of PHENOmenon. The other partners are industrial stakeholders, the end users of the technology being developed in the context of this project.

What expectations do the industrial partners have?

KH: Here are a few examples: The Fábrica Nacional de Moneda y Timbre, a public Spanish company, is interested in manufacturing security holograms on bank notes. Thalès would like to cover the photovoltaic panels it markets with micro and nano-structured surfaces produced using nano-printers. The PSA Group wants to equip the passenger compartment of its vehicles with holographic buttons. Design LED will introduce these micro-structured 3D components in its lighting device, a plastic film used to control light…

What are the next steps in this project?

KH: The project partners meet twice a year. IMT Atlantique will host one of these meetings on its Brest campus in the summer of 2020. In terms of new developments in research, the chemistry laboratory of ENS Lyon is preparing a new type of resin. At IMT Atlantique, we are continuing our work. We are currently able to simultaneously project a large number of identical laser beams. The goal is to succeed in project different types of laser beams and then produce prototype nano-structures for the industrial partners.

 

 

Quelles sont les bonnes pratiques à adopter en matière de mixité pour inclure les femmes dans les milieux technologiques ?

In IT professions, diversity is all about inclusion, not exclusion

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Ideas about diversity are often fallacious. Sociology has shown that women are seen as being responsible for their own inclusion in places where they are a minority. Chantal Morley is conducting research in this field at Institut Mines-Télécom Business School. She is especially interested in diversity in technological fields, whether they be companies, universities, engineering schools, etc. In this interview for I’MTech, she goes over the right approaches in promoting diversity, but also the wrong ones. 

 

You suggest that we should no longer approach the issue of diversity through a filter of exclusion, and instead through inclusion. What is the difference?

Chantal Morley: This idea comes from seeing the low impact of the measures taken in the last 20 years. They are aimed at women solely in the form of: “you must keep informed, you have to make an effort to be included”.  But men don’t have to make these efforts, and history tells us that at one point in time, women didn’t have to either. These calls and injunctions target women outside working spaces or territories: this is what we call the exclusion filter. The idea is that women are excluded and should solve the problem themselves. Thinking in terms of inclusion means looking at practices in companies, discussion spaces and education. It is about questioning equality mechanisms, attitudes and representations.

Read on I’MTech: Why women have become invisible in IT professions

In concrete terms, what difference will this make?

CM: The reason women do not enter IT professions is not because they are not interested or that they don’t make the effort, but because the field is a highly masculine one. By looking at what is going on inside an organization, we see that technical professions, from which women have long been excluded, affirm masculinity. Still today, there is a latent norm, often subconsciously activated, which tells us that a man will be more at ease with technical issues. Telling women that they are foreign to these issues, through small signs, contributes to upholding this norm. This is how we have ended up with a masculine technical culture in companies, schools and universities. This culture is constantly reinforced by everyday interactions – between students, with teachers, between teachers, in institutional communication. The impact of these interactions is even stronger when their socio-sexual nature goes unquestioned. This is why practices must be questioned, which implies looking at what is going on inside organizations.

What are the obstacles to diversity in technological fields?

CM: Organizations send out signals, marking their territory. On company websites, it is often men who are represented in high-responsibility jobs. In engineering schools, representations are also heavily masculine, from school brochures to gala posters produced by student associations. The masculine image dominates. There is also a second dimension, that of recognition. In technology professions, women are often suspected of illegitimacy, they are often required to prove themselves. Women who reach a high status in the hierarchy of a company, or who excel in elite courses, feel this discreet suspicion and it can make them doubt themselves.

What does a good inclusion policy involve?

CM: We carried out a sociological study on several inclusion policies used in organizations. A successful example is that of Carnegie-Mellon University in the United States. They were first asked to undertake an analysis of their practices. They realized that they were setting up barriers to women entering technology courses. For example, in selecting students, they were judging applicants on their prior experience in IT, things that are not taught in schools. They expected students to have skills inherited from a hacker culture or other social context favoring the development of these skills. However, the university realized that not only are these skills usually shared in masculine environments, but also that they are not a determining factor in successful completion of studies. They reviewed their admission criteria. This is a good example of analyzing the space and organization in terms of inclusion. In one year, the percentage of female students in IT rose from 7% to 16%, reaching a stable level of 40% after four years. The percentage of female applicants accepted who then chose to enroll more than doubled in a few years.

Read on I’MTech: Gender diversity in ICT as a topic of research

Once women have joined these spaces, is the problem solved?

CM: Not at all. Once again, Carnegie-Mellon University is a good example. On average, female students were giving up their IT studies twice as often as men. This is where notions of culture and relations come in. New students were subject to rumors about quotas. The men believed the women were only there to satisfy statistics, because they themselves had been conditioned by clichés on the respective skills of men and women in IT. The university’s response was a compulsory first-year course on gender and technologies, to break down preconceived ideas.

How necessary is it to use compulsory measures?

CM: There are two major reasons. On the one hand, stereotypes are even stronger when they are activated subconsciously: we therefore have to create conditions under which we can change the views of people within a group. In this case, the compulsory course on gender or the differentiated first-year courses enable all students to take the same courses in the second year, boost self-confidence and create a common knowledge base. The measure improved the group’s motivation and their desire to move forward. Cultural change is generally slow, especially when the non-included population is strongly in a minority. This is why we have to talk about quotas. Everyone is very uneasy with this idea, but it is an interim solution, which will lead to rapid progress in the situation. For example, the Norwegian University of Science and Technology (NTNU), another case of successful inclusion, decided to open additional places for women only. Along with a very clear communication strategy, this approach saw female student numbers rise from 6% to 38% in one year and saw the creation of a “community” of female engineers.  The percentage of women admitted stabilized, and the quotas were abandoned after three years. The issue of separate spaces is also interesting. Carnegie-Mellon, for example, launched an association for female IT student which it still supports. With help from the school’s management, this association organizes events with professional females, as women felt excluded from the traditional alumni networks. It has become the largest student association on campus, and now that the transition period is over, they are gradually opening up to other forms of diversity, such as ethnic.

Is there such a thing as bad inclusion measures?

CM: Generally speaking, all measures aimed at promoting women as women are problematic. The Norwegian University of Science and Technology is an example of this. In 1995, it launched an inclusion program attracting women by taking the “difference” approach, the idea that they were complementary to men. This program was statistically successful: there was an increase in the number of women in technology courses. Sociological studies also showed that women felt wanted in these training spaces. But the studies also showed that these women were embarrassed, the notion of complementarity implied that the university considered that women’s strong points were different from men’s. This is not true, and here we see the fundamental difference with Carnegie-Mellon, which attracted women by breaking down this type of cliché.

Since 1995, has this stance on complementarity changed?

CM: At the Norwegian University of Science and Technology, yes. After the reports from female students, the approach was largely modified. Unfortunately, the idea of complementarity is still too present, especially in companies. All too often, we hear things like “having a woman in a team improves communication” or “a feminine presence softens the atmosphere”. Not only is there no sociological reality behind these ideas, but also they impose qualities women are expected to have. This is the performative side of gender: we conform to what is considered appropriate and expected of us. A highly talented woman in a role which does not require any particular communication skills will be judged preferentially on these criteria rather than on her actual tasks. This representation must be broken down. Including women is not important because they improve the atmosphere in a team. It is important because they represent as large a talent pool as men.

 

digital Innovation Hub

Artificial Intelligence: TeraLab becomes a European “Digital Innovation Hub”

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TeraLab becomes one of the 30 Digital Innovation Hubs (DIH) selected by the European Union in artificial intelligence. This new recognition consolidates the place of the IMT’s TeraLab platform in the field of AI as well as its impact on business transformation.

 

On February 28 last year, the European Commission, via the AI DIH Network project, recognized TeraLab as one of the 30 Digital Innovation Hubs specializing in artificial intelligence in Europe (along with DigiHall – of which IMT is a member – and DigiWest in France).

What is a DIH?

DIHs are unique entry points which help businesses become more competitive in their production process or the way that their services use digital technologies. DIHs offer direct access to knowledge, expertise and the most recent technologies, in order to help their partners try, test and experiment with digital innovations.

TeraLab has more than 60 projects in research, innovation and education

The label thus rewards the quality of TeraLab, a “trusted third-party” platform which was developed at IMT.  It proposes state of the art tools for collaborations between businesses, startups, SMEs and researchers, with the aim of supporting and accelerating projects in AI and big data.

Since January 2014, TeraLab has been involved in more than 60 projects in research, innovation and education. It boasts significant European recognition with BDVA’s iSpace label and also with European projects such as BOOST 4.0 (connected factories), MIDIH (Manufacturing Industry Digital Innovation Hubs) or AI4EU (European Platform for AI on Demand).

To know more about TeraLab

TeraLab: data specialists serving companies

Energysquare: charging your telephone has never been so simple!

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Start-up company Energysquare has created a wireless charging device for tablets and cellphones. Using a simple mechanism combining a sticker and a metal plate, devices can be charged by conduction. Energysquare, which is incubated at Télécom ParisTech, will soon see its technology tested in hotels. The company now also aims to export and adapt its product to other smart objects.

 

Are you fed up of jumbles of wire in the house or on your desk? You probably never deliberately knotted your phone charger, and yet, when you want to use it, the wire is all tangled up! This article brings you good news: your fight against electric cables has come to an end! Start-up company Energysquare, incubated at Télécom ParisTech since 2015, has revolutionized electrical charging for mobile devices such as smartphones and tablets by disposing with current chargers. Your devices can now all be charged together on a single pad plugged into the mains.

“We based our work on the fact that the devices we use spend a lot of time on the surfaces around us, such as a desk or bedside table. Our idea was to be able to charge them over a whole surface and no longer with a cable at a single point,” explains Timothée Le Quesne, one of the designers of the Energysquare concept. We took a closer look at this vital accessory for future smart houses.

Easy-to-use conductive charging

The first question that comes to mind is how does it work? The technology is composed of two parts. Firstly, the pad, which is a 30×30-centimetre metal plate with independent conductive squares. It is plugged into the mains and can be placed on any surface as desired. The second part is a sticker comprising a flexible conductor with two electrodes and a connector adapted to the charging socket of your device, whether Android or IOS. The sticker is stuck directly on the back of your telephone. No surprises so far… but it is when the two parts come into contact that the magic happens.

When the electrodes come in contact with the charging surface, the system detects the device and sends it a signal to check that it is a battery. An electrical potential of 5 volts is produced between the two squares connected to the electrodes, allowing conductive charging. “The geometric format of the pad has been designed so that the two squares are automatically in contact with the electrodes. That way, there is no need to check anything and the device charges automatically without emitting any electromagnetic waves. Conversely, when no devices are detected, the pad automatically goes on standby,” explains Timothée Le Quesne.

But what happens if another device is placed on the pad? “The surface is naturally inert. The cleverness of the system lies in the fact that it can detect the object and identify whether it is a battery to be charged or not. Even if you spill water on it, it won’t have any effect. It is water resistant and protected against infiltration,” explains the young entrepreneur. No electric current is transmitted to your cup of coffee placed absentmindedly on the surface either. Although the system uses conductive charging, it does not emit any heat when it is running. Heat is dispersed across the surface like in a radiator, even if several devices are charging at once at the same speed as when plugged into the mains. Charging a device becomes so practical you could easily forget your phone lying on the surface. But this is not a problem, because the system goes back into standby once the device is fully charged.

Hotels soon to be using this technology

“We most need electricity when we’re away. We often have low battery in airports, cafes etc… This is a B2B market that we aim to invest in,” explains Timothée Le Quesne. For the moment, Energysquare is addressing the hospitality sector with tests to be carried out in France over the coming weeks. The principle is simple: the pad is installed on a bedside table and the stickers are provided at reception.

But the start-up aims to go even further. Why place the pad on a surface when it could be directly integrated into the furniture it sits on? “Our only limitation is preserving the metal surface of the pad to allow it to charge. We can still add a bit of style though by giving it a brushed effect, for example. Working with furniture manufacturers offers us good prospects. We can already imagine surfaces in meeting rooms covered with our device! We can also give the pad any form we like, with larger or smaller sections according to the device it is designed for,” Timothée Le Quesne continues.

With such a universal system, we can reasonably ask what the start-up’s aims are for the international market. “In January we will be participating in CES, an electronics show in the USA, where we will have a stand to display and demonstrate our technology.” This welcome overseas publicity is hardly a surprise since the start-up saw positive interest in its technology during a fundraiser on Kickstarter in June 2016, with 1/3 of purchasers in Asia and 1/3 in America. “As soon as we have finished validating our tests in hotels in France, we will turn to the foreign market,” affirms Timothée Le Quesne. But don’t worry, Energysquare hasn’t forgotten private individuals, and will launch the online sale of its technology in 2017.

Smart objects: a promising market to conquer

“One of our aims is to become a standard charging device for all smart objects,” admits Timothée Le Quesne. This is a promising future market, since 20 billion smart objects are forecast to be manufactured between now and 2020… All the more technology for us to spend time plugging in to charge! The start-up has already carried out tests with positive results on smart speakers and e-cigarettes, but the shape of certain objects, such as smart headphones, prevents the Energysquare system adapting to them. “For some devices, the electrodes will have to be integrated directly by the manufacturer.”

Nevertheless, there is one item that we use every day which would definitely benefit from this sort of charging system: laptops! The main difficulty, unlike other objects, is the power that needs to be generated by the system. “We need to change certain components to obtain more power through the pad and adapt it to laptops. It is something that is scheduled for 2017,” affirms Timothée Le Quesne. This is the first obstacle to overcome, especially since, when we asked the young entrepreneur what the future for Energysquare looked like 5 to 10 years from now, he replied: “we would like to be able to not only charge devices, but also power household appliances directly. We want to get rid of electric cables and replace them with surfaces that will power your kettle and charge your phone.”