INTRODUCTION

“How to protect workers while at the same time garnering and reaping the benefits of change and innovation?”

Robert Rubin, former US Treasury secretary

The impact of computer technology on the society is constantly increasing. New instruments are periodically introduced in the market, contributing to change social habits and in particular modifying how interactions between humans are carried out. This innovative influence has extended with an evident focus on increasing productivity also in the industrial relations sector, revolutionizing the productive systems by changing the traditional workers’ identity. To boost the production capabilities, new technological instruments such as algorithms have been introduced to monitor the worker’s performance according to efficiency criteria. Due to its high results into collect, analyse and make decisions based on standardized information, the use of algorithmic management has extended from the original industrial sector to logistics. The use of algorithms is arising new challenges to national legislators, trade unions and to labour law scholars, which not always able to face this constant change are leaving workers without effective protection, with the risks of exposing new job positions to modern forms of worker exploitation. In particular, wage’s determination, dismissal power and accountability are topics under the lens due to their drastic change, opening several incertain prospectives for the future employments conditions.

In the first chapter, this article will describe the historical passage from Taylorism to algorithmic management and will be introduced to the reader the sector that more than other is exploiting the algorithm’s potential, namely the gig economy. The following will assess the risks and threats on workers’ fundamental rights arose both at the collective and individual level. The Amazon case discussed in chapter three due to its relevance will expose how the logistics sector has implemented algorithmic forms of control and how a multinational firm may manage the industrial relations in a context of legal uncertainty. Lastly, in the final chapter, some questions are posed regarding the possible social next step originated from an incisive use of algorithmic management and on the roles that public institutions and trade unions might have in this transnational phenomenon.

1. FROM SCIENTIFIC MANAGEMENT TO ALGORITHMIC MANAGEMENT

a. Taylorism: the born of scientific management:

In 1911, Frederick Winslow Taylor wrote the book that would have forged the North American industrial sector, revolutionizing the factories’ production-line organization. The book “Principle of Scientific Management” laid down a new series of principles for large-scale manufacturing industries. The main four are:

1) Replace rule-of-thumb work methods with methods based on a scientific study of the tasks.

2) Scientifically select, train, and develop each employee rather than passively leaving them to train themselves.

3) Provide "Detailed instruction and supervision of each worker in the performance of that worker's discrete task" (Montgomery, 1997, p. 250).

4) Divide work nearly equally between managers and workers, so that the managers will apply scientific management principles into work activities design and the workers perform the given tasks without providing any personal original contribution to its realisation.

Taylor who left Harvard to work as an apprentice for the Enterprise Hydraulic Works in Philadelphia, quickly climbed all the available job positions to become “chief engineer of the works”, this role permitted him to realize how slow and inefficient the labour’s organization inside the American factories was. Practices like the soldiering where a problem, the workers were not working hard as they should or fast as they should, generating monetary losses for the company. These discoveries pushed Taylor to study the human’s contribution to the industrial process; he monitored and recorded every stage of every job, calculating how many movements a worker should do to complete a task in the fastest and more efficient possible way. To guarantee the production-line efficiency he identified two different kinds of work, mental and manual, increasing the managerial control over each employees’ job. Since then, managers would have planned specific activities for the workers, assigning them specific and simple tasks and the way how to carry them out, according to detailed rules led by mathematical formulas to avoid any worker’s contribution.

Perhaps the most prominent single element in modern scientific management is the task idea […] this task specifies not only what is to be done but how it is to be done and the exact time allowed for doing it. (Taylor, 1911)

The excellent results achieved through the implementation of the scientific management approach in several factories during the “20s positively impressed Henry Ford. He was so inspired by this new way to organize the industrial production at the point to assimilate its principles and methodology, creating and implementing this standardized method of mass production and mass consumption in its factories. Nowadays the scientific management principles are still running the industrial sectors; the new instruments discovered by the technological development are used following Taylor’s principle. Robots, which are easier to monitor than men, have substituted human workers in many points of the production-line, assuring a more reliable and standardized execution of those actions programmed by technicians in line with the production’s efficiency objectives planned by managers.

Although this role exchange in the production line, Taylor’s method is still applied to human workers, their tasks are still meticulously planned to guarantee the highest level possible of efficiency and surveillance cameras where possible have been switched with mobile apps. Tablets, wearable devices and other technologic instruments are continuously keeping under control every production-line step and providing to managers and big data engineers a constant flux of real-time information. These data analysed and processed by algorithms constitute the operative core element of the new-born algorithm management.

b. Algorithmic Management

Algorithms are essential for the correct working of several instruments considered vital elements for many. From simple program as the GPS navigator to complex like those who allow face recognition, manage monetary transactions or even enable self-driving cars, these containers of information are capable to elaborate data and to generate instructions to follow or tasks to accomplish. Computers and smartphones perform most of their actions using algorithms. The world leader internet’s search engine “Google” is nonetheless than an algorithm itself. One, however, is the subject of this article, a new type recently introduced, capable of self-learning and self-programming, abilities which are opening the way to a future where algorithms would not be anymore created by humans but by other algorithms. This last case, in particular, is of particular relevance considering the possible social implications, generated by what many consider bias-free decisional instruments.

According to their creators, algorithms are built to manage most of the commonly used objects or actions in our society, performing their tasks in the most efficient way possible. Companies are using these new instruments to manage an increasing number of processes, from production lines to customer’s data collection and monitoring or to control the employees’ behave and performance.

An increasing number of companies are managing their entire employees’ management system through the use of elaborated algorithms, applying what has been called by some academics of the Carnegie Mellon University Human-Computer Interaction Institute the “Algorithmic Management”. Famous North-American retail shops like 7-Eleven or even on-line stores like Amazon are, in fact, now using these powerful instruments to calculate what has been called the worker’s “true productivity”. Greg Tanaka, the Founder & CEO at Percolata, a Silicon Valley algorithms creator company, recently said: “What’s ironic is we’re not automating the sales associates’ jobs per se, but we’re automating the manager’s job, and [our algorithm] can actually do it better than them.” (O’Connor, 2016). What Tanaka emphasized is the efficiency of its product, which should avoid inefficient work activities organization and mismanagement conducts. According to him, bias or human preferences between colleagues are void limiting the manager’s discretion. Also, considering that the estimating capability of demand analysis performed by algorithms is higher than a human being, these can perform managerial tasks better than their human competitors, providing an improved service to customers and a more reliable working schedule to employees, guaranteeing to them secure stable rotas and the necessary flexibility requested by the contemporary labour market.

To “make transportation as reliable as running water” (Travis Kalanick, co-founder of Uber, 2016) the transportation network Uber is using a mobile app to manage its workers. Uber’s drivers, also called contractors, are around a million around the world and are managed by an algorithm. From their selection process to their evaluation, these drivers are selected and evaluated according to standardised parameters to ensure a foreseeable level of service provided to customers. Deleting also any need for face-to-face interaction between them and the company, their "employment" is realised through the app as well as all their working activities. When they decide to work, due to the flexibility offered by the company, one click is enough to log in them into the app, the algorithm then will select for them the customers in the base of their distance, showing in the drivers' app just a “trip request” without displaying any personal information regarding their customers nor their final destination. This request can be skipped up to three-time before being disconnected. To keep their "collaborators" under control and to monitor their performance the company receives weekly reports including average customer rating and confirmation rate and the same results are also shared with the drivers. This service level assessment is a quite common instrument used also by other gig economy companies such as Deliveroo. They use algorithms to compare the contractors’ performance estimating the medium average time necessary to complete a specific trip taking into account what the company has defined as “reasonable delay”. Another example of how to assess the workers’ performance using algorithmic management is given by Amazon, where according to real-time “pick rate” the algorithm sends systematic instructions to workers guiding them in the most efficient path to pick the requested objects from the shelves.

The idea that technological breakthroughs will necessarily imply progress for most of the actors involved is at the base of the Techno-deterministic approach (De Stefano, 2018), namely, the use of algorithms will consist in a step forward to management and would create new opportunities for flexible work seekers and those enterprises projected in the future. New business types of organizations and users are actually supporting this approach enjoying the algorithm’s potential. Gig economy, access economy or shared economy are terms frequently used to identify new technology enthusiast subjects of the economic market which based their business strategy on the use of Algorithmic Management through mobile apps. Algorithms are incredibly useful tools used to efficiently accomplish tasks and more importantly, they can aid the user to accomplish them in the most efficient way. Surely, their implementation is improving the lives of several workers in many ways. However, the use of their potential is in the hands of the developer and of the enterprise that required its creation, this if on one side is leading to economic efficiency on the other is arising serious threats to fundamental workers' rights because of the over-trusted algorithms' ability to efficiently control and manage human beings. Algorithmic management might push Taylor’s classic work division between mental and manual work to the next step, where algorithms will perform the first work and human beings the latter, leading to intrusive and dystopic work practices. The effects of algorithms employment in our economy needs particular attention because of the relation between human beings and apps. This must be managed wisely by national and international legislators to guarantee that worker's rights protection would not be considered as an obstacle that leads to economic losses for the company, but a vital social paradigm to do not shatter.

c. Gig Economy

Hillary Clinton, in 2015, during her election campaign, introduced the expression “Gig Economy”, focusing on the flexibility of its job positions. According to the Oxford Dictionary, its labour market is characterized by the prevalence of short-term contracts or freelance work as opposed to permanent jobs. (OUP, 2019) She highlighted the existence of the gig workers and their labour issues taking them out from the black box of the unknown. In the show business “gig” means the payment or cachet given to an artist for its performance, usually, this is short, flexible and temporary.

The gig economy was born to identify all those works, which characterised by these same characteristics. Usually, students or people, which did not have the necessity for a full-time job, suit perfectly to this kind of works. Gig workers are usually technology enthusiast that regularly use apps based on algorithms. Therefore, to them, their use is natural and considered secure. The company’s app provides them jobs or “gigs” to do in real-time, without the necessity to human contact and if you are not fast enough to accept the job, you might lose it. They are not obliged to accept those works but the app will record the refusals lowering down their assignment priority in case of low acceptance rates. Therefore low rate means for the worker that he will receive fewer job offers in the future; this particular feature highlights how the performance's efficiency is the key aspect taken into account by the algorithm. Consequently, the app decides the price of the job in advance and selects the workers and the customers in a vertical relation where every single aspect is already calculated and planned. This characteristic is crucial in the distinction between companies that are parts of the gig economy and those part of the so-called shared economy; their approaches and thus their apps are also different. In the latter, is allowed for the parts, customer and provider, to arrange the work’s details autonomously. It's evident therefore that in this case, the algorithm leaves the freedom of choice to the users preserving a basic human relation. The notorious accommodation app Airbnb is a clear example of this other face of the algorithm’s base work reality.

Algorithms are revealing themselves as an incredible opportunity to boost the economic profits and to create new jobs thanks to their powerful elaborating data capacity. Also, the possibilities for their users are constantly increasing creating new types of business or working habits. Users can share their car, rent their house or offer their competences to the best offerors without the direct intervention of a human agency. Nevertheless, even if algorithms are created with the best intentions they may lead to unintended consequences. Considering the increasing numbers of gig workers, one-third of the U.S.A. workers (McCue, 2018) and around one million in Italy (Cottone, 2018) an analysis of their job positions and especially their fundamental rights' protection is essential. Spare time is what in the original idea of the gig economy creators was supposed to be exchanged for student's little works; however, the increasing notoriety of these job portals like Deliveroo, Uber(eats) or Foodora has attracted not only part-time job seekers but also regular unemployed people unable to find a full-time occupation. These are falling back on the easy job possibilities offered by these companies. It is not required in fact any particular skill but only the will (or necessity) to work and to possess a smartphone. These characteristics jointly with the economical crisis are increasing the number of full-time gig workers, changing the very essence of the notion of “gig”. Therefore mix between adverse economic conditions and the facility to become a gig worker has modified the nature of the gig economy. From part-time students, now a very big part of gig workers is working daily and full time, however, they’re still considered as “part-time collaborators”, earning basic low income with a very basic or mostly none trade unions representation.

2. ASSESSING THE PROTECTION OF WORKER’S FUNDAMENTAL RIGHTS

a. The lack of accountability

Algorithmic management might sound like the future but it has uncanny echoes from the past (O’Connor, 2016) and it is arising new serious social concerns. Some critics like Guys Standing, British professor of Development Studies at the School of Oriental and African Studies, University of London, and co-founder of the Basic Income Earth Network, are stressing the social impact of the Algorithmic Management in the labour market. His main critic is against the creation of a “precariat” class of workers by the Gig economy that does not share the same level of protection with the traditional workers. Some companies are accused to use the algorithms potential to opaquely over-control their “contractors”, to manage them with the smartphone in their pockets, to reduce or even to remove any kind of human relations in the job activities, in the end to dehumanizing them exploiting the current lack of specific legislation and the economic crisis.

Algorithms are used to monitor and to pay the workers only what the company want to pay them according to indicators that recall those used by Taylor hundreds of years ago. An army of workers coming from the bottom of the labour market, attracted by promises like “being your own boss”, by the increasing amount of available jobs and by the security of regular payments, is contracting themselves to companies that have huge opportunities to greedy exploit them and very few legal limitations.

The algorithmic management seems like a contemporary or futuristic phenomenon by is potentially dangerous as or even more than the past scientific management. The implementation of algorithms-based apps in the labour market is affecting the way how workers are managed and their possibilities to effectively communicate with the company, creating concerns on the increasing the lack of human contact in working relations. These workers are managed by an algorithm, not by human beings and the app is their only instrument to communicate with the company. What in the intentions of the app’s developers should reduce the risk of management misbehave or biases is, on the other hand, increasing the risks of algorithmic discrimination and workers’ legal misrepresentation. A clear example of algorithms’ questionable judgment is given by the food riders selection process. Those, which have been reviewed too many times badly face the risk to be excluded from the most profitable jobs or directly from the access to the platform. To guarantee a high level of customer satisfaction is essential for business but what happens when the bad or negative reviews come from fussy customers, or because of medical reasons is not possible to complete a task according to what the algorithm has calculated as the acceptable range of efficiency? Customer’s bias definitely influences the worker’s evaluation as analysed by the algorithm, lowering down the performance score without any human supervisor double-check. Considering that the possibility to login into the apps for the workers is affected by the customer’s judgement, this lack of double evaluation filtered by human interaction generates a serious threat for their employment opportunities, algorithmic limiting the possibility to work and leaving the workers without the possibility to protect their fundamental rights through a trade unions intervention.

Gig economy companies and governments are creating algorithms capable to collect, store and analyse a massive amount of data, instruments fully capable to be the new managers of human workers, arising a serious question regarding the accountability of their actions in the decision-making process. To Marc Rotenberg, executive director of the Electronic Privacy Information Center, based in Washington D.C.:

The core problem with algorithmic-based decision-making is the lack of accountability. Machines have literally become black boxes – even the developers and operators do not fully understand how outputs are produced. The problem is further exacerbated by ‘digital scientism’ – an unwavering faith in the reliability of big data. ‘Algorithmic transparency’ should be established as a fundamental requirement for all AI-based decision-making. There is a larger problem with the increase of algorithm-based outcomes beyond the risk of error or discrimination – the increasing opacity of decision-making and the growing lack of human accountability. We need to confront the reality that power and authority are moving from people to machines. That is why Algorithmic Transparency is one of the great challenges of our era. (Rainie and Anderson, 2017)

Judith Donath of Harvard Berkman Klein Centre for Internet & Society has found in the design and it the information sharing of the app, two important elements to counterbalance the power of the algorithms in the decision-making process, design and easily accessible information:

Data can be incomplete, or wrong, and algorithms can embed false assumptions. The danger in increased reliance on algorithms is that the decision-making process becomes oracular: opaque yet unarguable. The solution is design. The process should not be a black box into which we feed data and outcome an answer, but a transparent process designed not just to produce a result, but to explain how it came up with that result. The systems should be able to produce clear, legible text and graphics that help the users – readers, editors, doctors, patients, loan applicants, voters, etc. – understand how the decision was made. The systems should be interactive so that people can examine how changing data, assumptions, and rules would change outcomes. The algorithm should not be the new authority; the goal should be to help people question authority. (Rainie and Anderson, 2017)

Considering how fast algorithm-based systems presence and relevance is growing in the labour sector, a challenge must be faced. Assessing the problem of data’s reliability and decision-making accountability, supporting the idea of an ethical algorithm’s design. It is not possible to leave the full control of the design process to companies’ engineers nor to self-programming algorithms when workers’ fundamental rights are at stake. Therefore, governments should legislate on this important issue, regulating algorithmic management implementation, defying the limits in the criteria used to elaborate data, increasing the transparency and the human accountability behind algorithms’ decisions.

3. THE AMAZON CASE

The company born in Seattle in 1994, is probably the enterprise who most of the others has embedded Taylorism management principles and has been able to apply for the first time the algorithmic management in the logistics system. As Henry Ford did before him, Jeff Bezos, founder of Amazon, does large use of algorithms, merging strict tasks and worker’s control with the use of highly technological instruments. Every single step on the logistics-chain, from inbound warehouses to outbound delivery services are strictly monitored and planed by sophisticated algorithms, keeping records of every data monitored during the working process in a central database. Every step is composed of simple actions: picking, packing, delivery. All these actions are carefully monitored and standardised with the aim to achieve equal efficient performances globally. The control of workers’ behave is realized through sophisticated items. Scanners, for example, are provided to all the “pickers” and all of them have active microphone and camera. As in the Fordist production line, every step of Amazon’s logistics chain is oversimplified and detailed planned by engineers so that to the worker is required only to follow simple instructions shown in their personal display without performing any discretional action. All the tasks are measured and the performance of each “picker” is compared with an ideal efficient reference in order to assess their efficiency. Every manager has the task to monitor and recall those workers that do not complete their tasks in the expected time provided by the app and above them, higher ranks of manager control their performance in a pyramidal vertical structure that does not require nor accept bottom-up contributes. Algorithms in digitalised services factories are used has instruments to command and control every workers’ action limiting any kind of autonomy or freedom of action, in a logic where the lust for monetary efficiency is self-evident. Next step to increase workers performances, an electronic bracelet has been already patented and tested by Amazon. Soon it will be introduced to free the hand of pickers from their scanner, allowing them to complete their tasks smoothly and faster, increasing their personal and so the general efficiency of the picking process. In the panoptic Amazon’s warehouses, algorithmic management it’s pushed to the limit in the extend that all the human workers, from those who perform a basic task as the pickers to managers, are controlled and managed by algorithms. This process of human-automation it’s not only limited to the Amazon’s employees but it’s extended to the courier companies through the app “Relay”, enacting was has been described as a process of Uberatization of the logistic sector. (Cunnane, 2017)

Another important aspect, which Amazon is embedding from the Fordism, is the attempt to avoid any kind of collective bargaining. The above-mentioned problem arose in the gig economy is enhanced by Amazon’s corporate architecture. Each warehouse is managed by an autonomous firm limiting the application of national labour representation laws. One example is given by a recent German case.

In Deutschland, the law imposes to any factory with more than 2000 employees a series of workers representation bodies, the company, however, is limiting the number of its full-time employees below that limit value to avoid any kind of collective bargaining or effective workers representation. Temporary employees are in fact employed with an easily solvable contract to limit their right to strike within the warehouse. The areas where Amazon decide to open a new warehouse it’s also strategic, the company choices regions underdeveloped or affected by the economic crisis. Workers can be national or foreigners, without any specialization or particular talent. What pool them is to find in Amazon the best and secure job possibility in their area because of the security granted by regular payslips and planned schedule. Permitting them to plan their lives and to secure them a fixed income at the cost of reduced fundamental personal rights. In Italy, Amazon applies the collective contract, which provides to workers a not so different protection from the one granted to their German colleagues. In the warehouse located in Piacenza, heavy shifts and the non-acceptance of negotiation with the local trade unions have backlashed in 2017 during the Black Friday. In that occasion, a strike joined by a number between 50 and 10% of the employees obtained the attention of the Italian government and can be considered the first step toward an increased worker’s collective consciousness in the belpaese. The outcome of this act of protest is however uncertain, considering that the company has not changed its working policy and it has increased the hostility against the local trade unions. These cases are useful to understand which might be the future perspective of many other companies in the logistics sector and how to face this new form of digital-Fordism. Currently in Germany as in other parts of the world, the exclusion of trade unions and the non-acceptance of negotiations have resulted in a legal case won by the Bündnis 90/Die Grünen or Grüne (German Green Party) on the behalf of workers.

To win the challenges arose by Amazon as well as other gig economy companies the trans-nationalization of the conflict seems the best way to tackle the misuse of algorithmic management, informing the gig workers regarding their fundamental rights and giving them an effective trade union representation. On the meanwhile at the national level, collective legal actions appear as the only viable solution to stop or reduce workers’ rights restriction.

4. FUTURE PERSPECTIVE: TRIUMPH OF NEO-TAYLORISM OR HARMONISED INCLUSIVE SOCIETY?

“Regulation and collective governance of these processes will not be built in a day, but they are essential to ensure that the benefits of technological advancements improve our societies inclusively and as a whole”(De Stefano, 2018)

Algorithmic management is posing new challenges to the society, when data and predictive modelling become paramount its overall impact is presently incalculable because is mostly hidden from public view. Barry Chudakov, founder and principal at Sertain Research and StreamFuzion Corp, U.S.A. said:

The main negative changes come down to a simple but now quite difficult question: How can we see, and fully understand the implications of the algorithms programmed into everyday actions and decisions? The rub is this: Whose intelligence is it, anyway? […] Our systems do not have, and we need to build in, what David Gelernter called ‘topsight,’ the ability to not only create technological solutions but also see and explore their consequences before we build business models, companies and markets on their strengths, and especially on their limitations. (Rainie and Anderson, 2017)

Designing human-oriented algorithms is, therefore, an answer to their lack of accountability. They must be written not only seeking economic efficiency but also guaranteeing the respect of fundamental rights. Experts in canvassing noted that algorithms are primarily written to optimize efficiency and profitability without taking seriously into consideration the possible societal impacts of data modelling and analysis. Arguing that humans are considered to be an “input” to the process and they are not seen as real, thinking, feeling, human beings. They say this is creating a flawed, logic-driven society and that as the process evolves – that is, as algorithms begin to write the algorithms – humans may get left out of the loop, letting “the robots decide.” (Rainie and Anderson, 2017) Jeremias Prassl, a law professor at Oxford University, said: “Algorithms are providing a degree of control and oversight that even the most hardened Taylorists could never have dreamt of” His words confirm that the techno-deterministic approach has until now focused only on the quantity of the results not on its quality, in fact, not only performances are controlled by algorithms but also employees’ dismissals. To challenge this approach is essential to foster the “Human-in-command” approach advocated by the European Economic and Social Committee’s Opinion on Artificial Intelligence, limiting the power of the algorithmic management on dismissal decisions by subjecting this process, affecting human beings lives, to human being review, the only one which is still legally accountable. Eliminating thus the possibility for the companies to exclude their legal and personal liability for the limitations or violation of worker’s fundamentals rights.

Considering the Amazon case as the leading one of an industry-type of management applied to the logistics, the next step in the employment of algorithmic management is to apply it in the traditional service sector. Companies like Subway or McDonald are already using the scientific managements principles and it would not take too long for them to switch to algorithmic management.

Since the scientific management to the algorithmic one, enterprises have focused their attention to production efficiency sacrificing fundamental worker’s rights, reducing their freedom of action and monitoring each step of the production-chain, avoiding any “deviance”. Trade unions and national legislators enacted different strategies to affect the equilibrium between performances and workers protection with different results. In the U.S.A., traditionally the role of trade unions has been reduced limiting the collective bargains, meanwhile, in the north of Europe, several nations have enacted a different approach arbitrating this interest’s clash. Supporting agreements between the parts and fostering a constructive dialogue between workers and enterprises their governments are managing directly the problem rather than ignoring it. The legislator’s capacity is, in the end, a key factor to limit aggressive forms of management in the gig economy sector as much as in others. Not only by limiting the use of an intrusive form of technologic surveillance but more importantly implementing new legal and contemporary conceptions of employment or modifying the already existing to guarantee to de facto employees their fundamental labour rights protection. Regulating the legal position of full-time gig workers on the base of the “primacy of fact” principle it is therefore vital as expressed by the Italian scholar Ludovico Barassi, which hundred years ago based the existence of employment status on the relation of subordination between the employer and the employee, a link that is self-evident in the gig economy.

Algorithmic management is creating new employment opportunities, better and cheaper consumer services, transparency and fairness in those sections of the labour market usually characterised by inefficiency, opacity and capricious human bosses. These new opportunities are flexible and in accordance with our contemporary liquid society, but it is essential to pose some questions regarding its evolution: How it will affect the legal protection of the involved workers and what national governments will do in order to avoid the violation of fundamentals rights? Will the employment/self-employment status be legally redefined? Will worker’s rights be extended to a hypothetical new form of employment? Are we moving toward a “Taylorism on steroids” form of society or it will evolve into a synergetic relation between human beings and algorithms?

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Notes

Author notes

Alternative link

http://www.revistarfjpuce.edu.ec/index.php/rfj/article/view/252/152 (pdf)