Archive for category: Digital

Romain Gola, Télécom École de Management – Institut Mines-Télécom

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[dropcap]I[/dropcap]n 2016, for the first time in France, online advertising investment exceeded that of television advertising. Algorithms now play an increasingly significant role in the purchase of advertising space on websites, raising many ethical and legal issues.

Algorithms rise to power

The digital advertising market in France is now estimated at €3.5 billion. Whereas up until now this advertising mainly involved displays on web sites, and the purchase of Google AdWords, the purchase of automated advertising space (called “programmatic buying”) has now emerged. The profiling of internet users is carried out using traces of their web activity, which makes it possible to predict their interest in an ad at any given time. Therefore, thanks to algorithms, it is possible to calculate, in real time, the value of the advertising space on a page the user is viewing.

The use of algorithms has the advantage of displaying banner ads that match our interests, but there are risks involved in their uncontrolled use. The lack of transparency in how these algorithms operate impacts internet users’ behavior without them realizing it. What is more, the algorithms sometimes benefit from exaggerated confidence, yet their results can be discriminatory. This raises the question of algorithms’ neutrality and ethical issues. The study of ethics in this area must be based on an understanding of how we are linked to these new technologies. This involves, on the one hand, how algorithms are covered by law and, on the other hand, the development of the digital advertising ecosystem.

In light of these new challenges, it would be wise to focus on the algorithms themselves, rather than on the data that is processed, by establishing systems capable of testing and controlling them, in order to prevent harmful consequences.

Law and algorithms: reforms in Europe

A new revolution is underway, based on data collection and processing that has reached an unprecedented scale, and stimulates the creation of new products and services. This increase in the amount and diversity of data is explained by the development of connected objects and the empowerment of consumers. Their ability to act has increased with the development of technology: businesses are becoming more and more dependent not only on the data consumers produce, but also on their opinion, and must therefore constantly ensure they maintain a good e-reputation.

In light of this situation, European institutions have begun the process of reforming personal data legislation. The new European General Data Protection Regulation (GDPR) will enter into force in all Member States in May 2018. It imposes increased transparency and the accountability of those who process data, based on a policy of compliance with the law, and it provides for severe penalties. Similarly, it affirms the right to data portability, and those in charge of processing personal data must ensure that their operations comply with personal data protection standards, starting at the design stages for a product or service (privacy by design).

The GDPR strives to implicitly regulate the algorithmic processing of data. We see a trend in the advertising sector: in general, all sites, services and products that use algorithms are careful not to refer to them. They hide the crucial role algorithms play, instead referring to “customization”. However, when there is customization, often there is “algorithmization”.

Legislation ill-suited to digital advertising

Laws pertaining to “traditional” advertising are based on the principle of receiving prior informed consent from individuals before processing their data. However, this concept of data protection is less relevant when it comes to digital advertising. Data collected in the context of traditional marketing often involves objective and relatively predictable information such as name, age, gender, address or marital status. Yet the concept of “data” radically changes when it comes to digital marketing. On social networks, the data is not only basic classification information (age, gender, address), but also includes data from everyday life: what I’m doing, what I’m listening to, etc.

Traces of web activity and individuals’ behavior on social networks make it possible to determine their profile. VisualHunt

This new situation questions the relevance of the distinction between personal and non-personal data. It also raises questions about the relevance of the principle of prior consent. It is often virtually impossible to use an application without accepting to be tracked. Consent therefore becomes mandatory in order to use the technology, and exactly how the data will be used by the data controller is completely unknown. Therefore, the problem is no longer related to prior consent, rather it is the automatic, predictive deductions made by the companies that collect this data.

Algorithms accentuate this trend by multiplying the collection and use of trivial and decontextualized data, likely to be used for specifically profiling individuals, and creating “knowledge” about them based on probabilities rather than certainties about their personal and intimate inclinations. In this situation, rather than examining the data feeding the algorithms, wouldn’t it be more relevant to examine the algorithms that process them and generate new data?

Legal and ethical challenges of online advertising

Influencing consumer choices, subliminal influence, submission that changes the perception of reality: behavioral targeting carries serious risks. Requirements for the accountability, transparency and verifiability of the actions caused by algorithms have become crucial in preventing potential excesses.

This situation calls into question the relationship between law and ethics, which is unfortunately often confused. Laws are established to regulate behavior—what is allowed, forbidden, or required from a legal perspective—whereas ethics refers more broadly to the distinction between good and bad, independent of and beyond any compliance with the law. Ethics applied to algorithmic processing would need to focus on two major principles: transparency, and the establishment of tests to check the algorithms’ results in order to prevent possible damage.

Transparency and accountability of algorithms

The activities of online platforms are essentially based on the selection and classification of information, as well as on offers for goods or services. They design and activate various algorithms that influence consumption behavior and how users think. This customization is sometimes misleading, since it is based on the machine’s concept of how we think. This concept is not based on who we are, but rather on what we have done and looked at. This observation reveals the need for transparency: the people impacted by an algorithm should first of all be informed of the existence of the algorithmic processing, as well as what it implies, the type of data it uses and its end purpose, so that they may file a claim if needs be.

Tests for algorithms?

In advertising, algorithms can lead to a differentiation in the price of a product or service or can even establish typologies of high-risk policyholders in order to calculate the insurance premium based on criteria that is sometimes illegal, by cross-checking “sensitive” information. Not only is the collection and the processing of this data (racial and ethnic origins, political and religious opinions) generally prohibited, the results of these algorithmic methods can be discriminatory. For example, the results of the first international beauty contest based entirely on algorithms led to the selection of only white candidates.

To avoid this type of abuse, urgent steps must be taken to establish tests for the results produced by algorithms. In addition to the legislation and the role played by the protection authorities (CNIL), codes of conduct are also beginning to appear: advertising professionals belonging to the Digital Advertising Alliance (ACD) have introduced a protocol represented by a visible icon next to a targeted ad that explains how it works.

It is in companies’ interests to adopt more ethical behavior in order to maintain a good reputation, and hence a competitive advantage. Internet users are weary of advertising deemed too intrusive. If the ultimate goal of advertising is to better anticipate our needs to “consume better”, it must occur in an environment that complies with legislation and is responsible and ethical.  This could be a vector for a new industrial revolution that is mindful of fundamental rights and freedoms, in which citizens are invited to take their rightful place and take ownership of their data.

Romain Gola, Professor of Business Law, Télécom École de Management – Institut Mines-Télécom

The original version of this article (in French) was published on The Conversation.

Also read on I’MTech

Anonymization is still sometimes criticized as a practice that supposedly makes data worthless, as it deletes important information. The CNIL decided to prove the contrary through the Cabanon project conducted in 2017. It received assistance from the IMT big data platform, TeraLab, for anonymizing the data of New York taxis and showing the possibility of creating a transportation service.

On 10 March 2014, an image published on Twitter by the New York taxi commission sparked Chris Whong’s curiosity. It wasn’t the information on the vehicle occupancy rate during rush hour that caught the young urban planner’s interest. Rather, what caught his eye was the source of the data, cited at the bottom, that had allowed New York City’s Taxi and Limousine Commission (NYC TLC) to create the image. Through a tweet comment, he joined another Twitter user, Ben Wellington, in asking if the raw data was available. What ensued was a series of exchanges that enabled Chris Whong to retrieve the dataset through a process that is tedious, yet accessible to anyone with enough determination to cut through all the red tape. Once he had the data in his possession, he put it online. This allowed Vijay Pandurangan, a computer engineer, to demonstrate that the identity of the drivers, customers, and their addresses could all be found using the information stored on the taxi logs.

Problems in anonymizing open datasets are not new. They were not even new in 2014 when the story emerged about NYC TLC data. Yet this type of case still persists. One of the reasons is that anonymized datasets are deemed less useful than their unfiltered counterparts. Removing any possibility of tracing the identity would amount to deleting the information. In the case of the New York taxis, for example, this would mean limiting the information on the taxis’ location to geographical areas, rather than indicating the coordinates to the nearest meter. For service creators who want to build applications, and data managers who want the data to be used as effectively as possible, anonymizing means losing value.

As a fervent advocate for the protection of personal data, the French National Commission for Information Technology and Civil Liberties (CNIL) decided to confront this misconception. The Cabanon project, led by the CNIL laboratory of digital innovation (LINC) in 2017, took on the challenge of anonymizing the NYC TLC dataset and using specific scenarios for creating new services. “There are several ways to anonymize data, but there is no miracle solution that fits every purpose,” warns Vincent Toubiana, in charge of anonymizing the datasets for the project, which has since transferred from the CNIL to the ARCEP. The Cabanon team therefore had to think of a dedicated solution.

Spatial and temporal degradation

First step: the GPS coordinates were replaced by the ZCTA code, the U.S. equivalent of postal codes in France. This is the method chosen by Uber to ensure personal data security. This operation degrades the spatial data; it drowns the taxi’s departure and arrival positions in areas composed of several city blocks. However, this may prove insufficient in truly ensuring the anonymity of the customers and drivers. At certain times during the night, sometimes only one taxi made a trip from one area of the city to another. Even if the GPS positions are erased, it is still possible to link the geographical position and identity.

“Therefore, in addition to the spatial degradation, we had to introduce a temporal degradation,” Vincent Toubiana explains. The time slots are adapted to avoid the single customer problem. “In each departure and arrival area, we look at all the people who take a taxi in time slots of 5, 15, 30 and 60 minutes,” he continues. In the data set, the time calibration is adjusted so that no time slot has fewer than ten people. If, despite these precautions, a single customer is within the largest time slot of 60 minutes, the data is simply deleted. According to Vincent Toubiana, “the goal is to find the best mathematical compromise for keeping a maximum amount of data with the smallest possible time intervals.”

In the 2013 data used by the CNIL, the same data made public by Chris Whong, NYC TLC made over 130 million trips. The double degradation operations therefore demanded significant computing resources. The handling of the data to be processed using different temporal and spatial slicing required assistance from TeraLab, IMT’s big data platform. “It was essential for us to work with TeraLab in order to query the database to see the 5-minute intervals, or to test the minimum number of people we could group together,” Vincent Toubiana explains.

Read more on I’MTech: Teralab, a big data platform with a European vision

Data visualization assisting data usage

Once the dataset has been anonymized in this way, it must be proven useful. To facilitate its reading, a data visualization in the form of a choropleth map was produced—a geographical representation associating a color with each area based on the amount of trips. “The visual offers a better understanding of the differences between anonymized data and that which is not, and facilitates the analysis and narration of this data,” says Estelle Hary, designer at the CNIL who produced the data visualization.

To the left: a map representing the trips using non-anonymized data. To the right: choropleth map representing the journeys with a granularity that ensures anonymity.

Based on this map, they began reflection on the kinds of services that could be created using anonymized data. The map helped identify points in Brooklyn where people order taxis to complete their journey home. “We started thinking about the idea of a private transportation network that would complement public transport in New York,” says Estelle Hary. Since they would be cheaper than taxis, this private public transport could cover areas neglected by buses. “This is a typical example of a viable service that anonymized data can be used to create,” she continues. In this case, the information that was lost to protect the personal data had no impact. The processed data set is just as effective. And this is only one example of a potential use. “By linking anonymized datasets with other public data, the possibilities are multiplied,” the designer explains. In other words, the value of an open dataset depends on our capacity for creativity.

There will certainly always be cases in which the degradation of raw data limits the creation of a service. This is the case for more personalized services. But perhaps anonymity should be seen, not as a binary value, but as a gradient. Instead of seeing anonymity as a characteristic that is present or absent from datasets, wouldn’t it be more appropriate to consider several accessible degrees of anonymity according to the exposure of the data set and the control over the use? That is what is the CNIL proposed in the conclusion of the Cabanon project. The data could be publicly accessible in fully anonymized form. In addition, the same dataset could be accessible in versions that are less and less anonymized, with, in exchange, a more significant level of control over the use.

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TeraLab, big data service for researchers

Teralab is a big data and artificial intelligence platform serving research, innovation and education. It is led by Institut Mines-Télécom (IMT) and the Group of National Schools of Economics and Statistics (GENES). Teralab was founded in 2014 through a call for projects by the Investments for the Future program called “Cloud Computing and Big Data”. The goal of the platform is to aggregate the demand for software and infrastructure for projects involving large volumes of data. It also offers security and sovereignty, enabling stakeholders to entrust their data to the researchers with confidence. [/box]