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- Trend snippet: Algorithms are actively used in many private sectors all over Europe
Trends in Security Information
The HSD Trendmonitor is designed to provide access to relevant content on various subjects in the safety and security domain, to identify relevant developments and to connect knowledge and organisations. The safety and security domain encompasses a vast number of subjects. Four relevant taxonomies (type of threat or opportunity, victim, source of threat and domain of application) have been constructed in order to visualize all of these subjects. The taxonomies and related category descriptions have been carefully composed according to other taxonomies, European and international standards and our own expertise.
In order to identify safety and security related trends, relevant reports and HSD news articles are continuously scanned, analysed and classified by hand according to the four taxonomies. This results in a wide array of observations, which we call ‘Trend Snippets’. Multiple Trend Snippets combined can provide insights into safety and security trends. The size of the circles shows the relative weight of the topic, the filters can be used to further select the most relevant content for you. If you have an addition, question or remark, drop us a line at info@securitydelta.nl.
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Algorithms are actively used in many private sectors all over Europe
3.1.3 Examples of use of algorithms in the private sector Just as in the public sector, algorithms are often relied on in the private sector. As this subsection illustrates, the purposes of algorithmic applications are more varied than in the public sector, where they are mostly used as aids to decision making and as tools for risk assessment, detection and efficient allocation of resources. Particularly noticeable is the use, in two cases, of so-called emotional AI, that is, systems that are able to detect human emotions through analysing facial expressions and traits. 3.1.3.1 Employment and platform work Algorithms can be very convenient in making employment decisions, and indeed, there appears to be a growing trend in their use by private companies for this purpose. In Finland, for example, it was estimated in 2017 that some form of AI was used in 40 000 recruitments per year, and this number is assumed to be increasing quickly.335 Similarly, in Poland, the Panoptykon Foundation has presented an analysis according to which more and more recruitment processes are becoming automated.336 This means that a machine is responsible for pre-selecting and rejecting some of the candidates who do not meet the pre-defined criteria, e.g. in terms of language skills, type of education or experience counted by years of work. The company Amazon Fulfillment Poland has been reported to use various algorithmic tools in this respect.337 In terminating employment contracts, for example, Amazon relied on an algorithmic indication of the percentage share of sickness absence in the company’s working time.338 Another employment-related use of algorithms in Poland concerns the practice adopted by the (statecontrolled) bank PKO BP of ‘collecting smiles’.339 This is a system incorporating individual sensors, combined with an advanced algorithm, to count a consultant’s smiles during conversations with clients. The system’s creators assume that the more an employee smiles at work, the more satisfied the customer will be, which in return would motivate the employee. In relation to platform work, there is currently a case pending in Italy on multinational food delivery companies using the ‘Frank’ algorithm. According to the trade unions that have brought the case, ‘the algorithm, in elaborating the reputation rankings of the cyclists, which in fact determine future job opportunities and booking priorities for deliveries, marginalises, to the point of excluding them […] those who do not manage to be available to log into the work slots assigned to them; riders who do not adapt to the logic of the algorithm are gradually excluded from work opportunities, leading in some cases to their being logged out by the system’.340 In other words, riders whose availability does not allow them to accept all the rides proposed by the algorithm are disadvantaged in subsequent allocation of work and are at risk of being completely excluded from work opportunities. 3.1.3.2 Banking and insurance Predictive algorithms can be helpful in making creditworthiness assessments and defining actuarial risk groups, which makes them an attractive tool in the banking and insurance sectors. For example, in Lithuania, there are experiments with a peer-to-peer insurance platform, Ooniq.341 According to its privacy policy, ‘based on information provided to the system’, this platform may engage in automated, algorithm-driven calculation of the amount of payment for lost or damaged devices such as mobile phones or tablets. The data that is used to give input to the system includes the model of the device and the amount of damages suffered. In the banking sector, it has been reported that many banks in Belgium, 342 Estonia, 343 France, 344 Germany, 345 Poland, and Spain346 use profile setting for access to bank credits and similar services. 3.1.3.3 Targeted advertising, price-setting and retail The use of algorithms is increasingly popular in the sectors of retail and advertising. In particular, predictive algorithms and profiling can be used for distributing personalised or targeted advertisements, especially on social media or websites, and they can also be relied on to engage in individualised or group-based price setting. In Denmark, for example, RockCrew, an organisation supplying temporary workers to stage crews, showed their ad on Facebook to men only.347 In Germany, the Berlin Public Transport Company offered targeted discounts to women on International Women’s Day using facial recognition.348 RIMI, one of the two largest food retailers in Latvia, allows a client to swipe his/her client’s card in an information machine to get personalised promotion offers.349 In Lithuania, an algorithm is currently in use in a fully automated store called Pixevia, where artificially intelligent systems can tell precisely which customer has taken what product in real time.350 Subsequently, this can result in targeted marketing. Finally, in Norway, pizza chain Peppe’s Pizza was shown to use facial recognition and algorithmic analyses to personalise the photos of available offers and options on the restaurant’s digital advertising screen.351 Data used to personalise the offers made on the screen were, amongst others, gender, age, appearance and mood (e.g. smiling). The result was that men were often shown pictures of pizza with steak/meat, while women were shown healthy salads.