10.6092/issn.2421-2695/8798
Il paper presenta un’analisi interdisciplinare dell’utilizzo delle nuove tecnologie nei rapporti di lavoro. In particolare approfondisce come le imprese della gig-economy impieghino strumenti tecnologici, come algoritmi e sistemi di rating, per dirigere e controllare i lavoratori delle piattaforme.L’obiettivo dell’analisi è duplice: approfondire l’utilizzo da parte delle imprese di tali tecnologie nella gestione del rapporto di lavoro e proporre l’inserimento di clausole di previa informazione e di negoziazione collettiva che abbiano ad oggetto i parametri degli algoritmi e dei sistemi di rating. In particolare l’analisi approfondisce il caso Uber e le tecnologie impiegate nella gestione dei rapporti di lavoro, quali i sistemi di geolocalizzazione e gli algoritmi di assegnazione e classificazione.Lo studio si articola in due parti fondamentali. In una prima parte si formulano delle ipotesi sulle tecnologie utilizzate dall’impresa Uber e sul loro funzionamento. In una seconda parte, che adotta come punto di partenza i risultati dell’analisi tecnica, si presentano alcune proposte sui contenuti di un futuro contratto collettivo che possa definire i parametri e i valori da attribuire agli algoritmi, con l’obiettivo di migliorare le condizioni di lavoro applicate ai lavoratori.
application/pdf
2421-2695
8798
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en
Marialaura Birgillito
Labour & Law Issues
Algorithms and ratings: tools to manage labour relations. Proposals to renegotiate labour conditions for platform drivers
Text.Serial.Journal
https://labourlaw.unibo.it/
4
Comparative & International Overview
2018-12-17
2018-12-16
2018-12-17
2019-11-05
C. 25-50
LLI
Marialaura Birgillito
2018/12/17
10.6092/issn.2421-2695/8798
C. 25
University of Castilla-La Mancha
2421-2695
2
en
50
Valenduc G.- Vendramin P., Work on the digital economy: sorting the old from the new, in ETUC WP, 2016.
Algorithms and ratings: tools to manage labour relations. Proposals to renegotiate labour conditions for platform drivers
https://labourlaw.unibo.it/article/download/8798/8551
4
Algorithms and ratings: tools to manage labour relations.
       Proposals to renegotiate labour conditions
                  for platform drivers




   GIOVANNI BIRGILLITO                              MARIALAURA BIRGILLITO
Transport Planner, Tplan Consulting                     University of Castilla-La Mancha




                                vol. 4, no. 2, 2018
                                      ISSN: 2421-2695
Algorithms and ratings: tools to manage labour relations.
       Proposals to renegotiate labour conditions
                  for platform drivers




                                                           GIOVANNI BIRGILLITO
                                                                   Transport Planner
                                                            presso Tplan Consulting
                                                       giovanni.birgillito@gmail.com


                                                        MARIALAURA BIRGILLITO
                                                   University of Castilla-La Mancha
                                         Profesora Asociada en Derecho del Trabajo
                                                            Marialaura.B_@uclm.es




                                    ABSTRACT

         Workers of digital platforms are managed by algorithms and then rated by
customers about their work. The aim of the paper is thus to describe
how technological innovation is used to manage and control digital workers, and to
present to collective actors some proposals in order to negotiate algorithms and
technological innovation with the purpose of improving drivers’ working
conditions.
        In particular, the paper analyses Uber case-study and the technology
employed: geo-localisation techniques for smartphones and GPS, as well as
algorithms.
        The paper is structured in two parts. After a brief analysis of legal cases, in
the first part, it describes which technologies are (or could be) employed by the
company to estimate the geographic position of users, to match supply and demand
        G. BIRGILLITO - M. BIRGILLITO, Algorithms and ratings: tools to manage labour relations



and to control drivers in the fulfilment of their working activity. In particular, it goes
in depth into some technical issues and parameters used to manage working
conditions. In a second part, by adopting the results of the technical analysis as the
starting point, it presents some proposals on how collective bargaining can
intervene in the management of those kinds of technology to improve drivers’
working conditions.


Keywords: new-economy; employment relations; management of new types of
technology; algorithm and ratings; social protection.




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                    Algorithms and ratings:
               tools to manage labour relations.
Proposals to renegotiate labour conditions for platform drivers


SUMMARY: 1. Introduction. − 2. The management of labour force by algorithms. Misclassification
     in the case of the Uber platform. − 3. Technological challenges. Matching transportation
     demand and supply in Uber platform. − 4. Technological challenges for workers’
     representatives. − 5. Trade union strategies for the negotiation of algorithms and ratings . −
     5.1. Collective regulation of ratings. − 5.2. Transparency duties and examples of crossed
     data analysis. − 5.3. Collective regulation and trade-union implication to limit employer’s
     power to deactivate (dismiss) workers. − 6. Organisational challenges for trade-union
     representatives. − 6.1. Some examples of multi-level coordination. − 6.2. Dilemma and
     strategies for a universalization of labour rights. − 6.3 First attempts for an approximation
     of atypical workers to standard employees? − 7. Concluding remarks.



1. Introduction

       The impact of technology on the labour market is not a new topic in the
framework of the capitalist system. By technological innovation, companies enhance
workers’ productivity to compete in the global market; under (and in accordance
with) the conditions imposed by the international competition, technology is
employed in the process of production, suppressing or creating new jobs and
modifying the organisation of work, its procedures and the pace of work (Cardona
Rubert, 2003).
       Technology is a central (historically determined) pattern of the relation
between capital and labour. In spite of this, some recent analyses refer to a specific
technological tool, the digital platform, to delimit a (supposedly) “new” economic
and social sector: the “gig” economy (Huws U. and others, 2018; Aloisi, 2016; Prassl
and Risak, 2016). Two main assumptions support this hypothesis. The first one is
the supposed “new” way relations between platform-users are managed (Degryse,
2016; Parker and others, 2016). The second one is the rapid development of virtual
markets where users exchange information, goods and services, by web-applications
and Big Data (Rifkin, 2012).
       In that framework scholars distinguish two main types of work in the
platforms: crowd-work and work-on-demand via app (De Stefano, 2015; Valenduc
and Vendramin, 2016). Crowd-work is a form of job-sharing – employed for
freelancing (short translations, audio transcription) or software development –




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where a group of workers (named “crowd”) develops micro-tasks posted online and
is paid only after the platform’s review (Silberman and Irani, 2016; Howe, 2006).
Work-on-demand via app is a form of job-on-call in which workers fulfil a
traditional activity (transportation, cleaning, running errands, clerical work), by
means of platform intermediation between clients and available workers with mobile
applications and geo-localisation systems.
        Regardless of the classification proposed and its signifiers (that we reject),
what can be underscored is that companies employ new technologies to catch
clients in the market, match supply and demand and manage labour relations, in
order to compete in the global market under the conditions required (as all the
companies do).
        This paper focuses precisely on how technologies and algorithms are used to
manage labour relations, with the aim to stress how collective bargaining is the best
way to achieve better working conditions for platform workers. It particularly
focuses on two main issues. The first one regards the technological challenges
workers’ representatives face on when negotiations require going into the software
code and seek the negotiation of the algorithm parameters; it also implies a
consideration on how trade unions are coping with the need to master such skills
and how it affects their structure and mission. The second one is the role of
collective actors in representing digital workers and the organisational challenges
they face, deepening into the difficulties of organising workers which are dispersed
– some of them with no work-place – and recognised as employees in some places
and still independent in others.
        The starting point of the analysis is the case-study of a transport company,
Uber, which we have chosen for two main reasons: the central position in the
market, based on low wages and transaction costs (Rogers, 2015), and the judicial
cases of misclassification in which it was involved – in particular, the preliminary
ruling at the European Court of Justice1 and the case-law at the Employment
Tribunal of London2, whose statement was confirmed in the appeal decision3 –. A
brief analysis of this case-study can clarify how management powers are exercised by
using technologies and algorithms, as a preliminary step to intervene.




       1ECJ case C-434/15 (Asociación Profesional Elite Taxi v. Uber Systems Spain, SL.).
       2 Case-law n. 2202551/2015 (Mr. Y.Aslam, Mr.J.Farrar&others vs. Uber B.V., Uber
London and Uber Britannia).
      3   Judgement of the Employment Appeal Tribunal of London (Appeal n.
UKEAT/0056/17/DA).



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2. The management of labour force by algorithms. Misclassification in the
case of the Uber platform.
         Companies employ algorithms to manage and oversee workers on a large
scale. The exercise of employers’ power of control is indeed one of the major issues
that Courts have discussed in legal cases on drivers’ misclassification.
         After conceiving Uber as a transportation company (Górriz López, 2015),
the European Court of Justice4 and some national Courts have gone in depth into a
second question: whether drivers are independent contractors or they are
employees/workers. As we will see in Section 6.3, this element is particularly
relevant when it comes to the aim of achieving a collective agreement and applying
its regulation to all workers, considering that in many countries collective rights are
only recognised to employees.
         According to the Employment Tribunal of London, confirmed in appeal
(Trillo Párraga, 2017), drivers are not autonomously organised, being Uber who
places the service on the market and adopts business strategies (as the geographical
and catchment area, the time slots and the surge price)5 to get profits from it. Three
main points are analysed in the UK case.
         Firstly, through the platform, the company recruits drivers with specific
requirements which it defines: for example, a driving licence for at least one year, a
standard vehicle with previously defined usage conditions, a proof of insurance (and
in most cities, a car inspection).
         Secondly, throughout the labour relation, the company gives precise
instructions about time, place and fulfilment of the service, the route and the code
of conduct that drivers have to respect (Ginés I Fabrellas and Gàlvez Duran, 2016,
22; Todolì, 2017). Furthermore, it determines the working conditions (Prassl and
Risak, 2016) and fixes the wage according to criteria which are converted in
algorithm parameters, as covered distance, time, location and service demand
(Rosenblat and Stark, 2015).
         Thirdly, Uber has the exclusive control of the organisation of the service. All
interactions between clients and drivers are managed by the platform. By using GPS
data, it receives and controls the localisation of vehicles (and riders) and assigns the
trips, taking into account the star rating: it assigns better tasks to the drivers with the

        4   The question, referred to the ECJ by the Juzgado de Mercantil n. 3 de Barcelona, concerns the
classification of Uber’s activity in light of EU law. The ECJ determines whether such business
should be considered merely technological intermediaries or transportation service providers; in the
latter case, which prevailed, they would be subject to the national regulations of transport services
of each individual EU Member State.
          5 Uber Terms and Conditions, point 4 (last access 03.03.2018).




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highest marks or deactivates the accounts of the workers with the lowest ones
(Rodríguez Fernández, 2017).
       Thereby, technology has a primary role in both, managing labour relations
and drivers’ working conditions. In the following section we will describe further
which technologies are employed to estimate users’ geographic position, to match
supply and demand and to control the performance of the service and of drivers. By
adopting the results of the technical analysis, in a second part, we will present some
proposals on how collective bargaining can intervene in the management of
technologies to improve drivers’ working conditions.


3. Technological challenges. Matching transportation demand and supply in
the Uber platform.
        New technologies are employed in platforms whose service strictly depends
on mobile devices, internet connection, data mobile network (3G, 4G/LTE, 5G,
etc.), and geo-localisation system. In order to estimate the geographic position of a
user owning a smartphone, outdoor wireless localisation of a mobile object is
generally needed. GPS (Global Positioning System)6 is the most efficient positioning
technology and it is generally included in a device in medium price range; since
smartphones usually have relatively low cost GPS chips, the performance of locating
accuracy is highly dependent on environmental and external factors (Hwang and Yu,
2012).
        Geo-localisation is a fundamental input for the Uber system too, in order to
match supply and demand. On the one hand, the customer who sends a request for
a ride also has to specify the pickup location, which is usually his current position.
On the other hand, the driver, who has a different kind of account, simply has to
tap Go Online in his Uber App on the smartphone in order to become available to
pick up customers, and he will automatically receive trip requests in his area. 7 In
both cases geo-localisation is needed, generally by means of GPS chip of the
smartphone. Then, in few seconds the platform matches the customer’s request for
a trip with the driver, who is chosen among the available ones in a local zone.



        6  It is a geo-localisation system that uses a network of artificial satellites, that is employed to
define the geographic coordinates (latitude, longitude, altitude) of any place on Earth. Anyway, GPS
measures are affected by errors that cause non-exact precision; according to the US Department of
Defense (2008), planimetric error has size of about 10 metres, while altitude error has size of about
15 metres, at 95th percentile and in standard conditions.
         7 Uber Website, http://www.uber.com (last access, 03.03.2018).




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        Anyway, Uber does not specify how the matching process exactly works,
partly because it is a company’s strategic asset. That is why we can only present
some hypothesis of how it can work.
        In the following figure it is reported an example of a potential situation (how
the matching process could work), with a trip request of a user who indicates also
his pickup location and the four available drivers nearby. Their positions are
displayed in the local graph – whose links are bidirectional – which represents the
local urban road network.




        Figure 1: example of local graph and locations of pickup and of available drivers

        A very easy way to match passenger and driver could be to identify the
closest driver, as the crow flies (driver 1 in figure 1). This method, however, can lead
to significant errors depending on the actual road network topology, that is not
taken into account, with a considerably higher waiting time for the customer as the
result. That is the case reported in the example of figure 1.
        Alternatively, since available drivers’ positions are always known by the
control system, the distance on the road network between the customer and each
driver can be reckoned.8 In the matching phase, calculation could be performed, for
example, by freezing drivers’ position only in the local area of the request (spatial


        8 For instance, with the availability of a local graph and of links’ cost – whose set

constitutes the network – a shortest path algorithm, like Dijkstra’s algorithm (Dijkstra, 1959) or A*
algorithm (Hart et al., 1968), can be used.



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simplification) in the time instant of the request (points’ steadiness requirement),
and by using, if necessary, a less complex road network (network simplification that
allows to reduce significantly computational time) in order to quicken this step.
Different attributes (distance, estimated travel time, etc.) can be taken into account
in link cost calculation, and consequently in path cost calculation too, whose value is
obtained by the sum of the cost of the links constituting the path. The driver chosen
for the pickup can be selected, for instance, as the one with the minimum cost path
from the customer, in terms of distance (driver 2 in figure 1), estimated time (driver
3 in figure 1, if he has the minimum travel time because driver 2 is stuck in a local
traffic jam), or a combination of these attributes, in order to decrease customer’s
waiting time.
        Nevertheless, we really don’t know how matching algorithm works. Some
Uber drivers seem to be reluctant to trust it, as they argue that it generates ride
requests by considering distance from one another and previous history of being
matched (Page et al., 2017). Lee et al. (2015) argue that other factors, such as
passenger-driver mutual rating and driver login time, could be factored into the
algorithm in addition to passenger-driver distance. Moreover, the assignment
algorithm penalises drivers’ rejection of passenger requests, which lowers drivers’
acceptance rates (Lee et al., 2015). Also in Rodríguez Fernández (2017) it is
highlighted that digital platforms rank their workers and benefit or disadvantage
them, by excluding them from the best opportunities, on the basis of their star
ranking. In these cases, some subjective elements should be introduced in matching
algorithm, for instance by inserting a penalty to the driver as an individual attribute
while his estimated distance from the customer, previously presented, is evaluated
by means of mathematical objective data only. Therefore, in the example presented
in figure 1, the requested trip could be assigned also to driver 4 even in the case of
an uncongested network.


4.     Technological challenges for workers’ representatives
        By using Uber as case-study, we propose some points on possible strategies
and actions of trade unions on technological challenges, aimed at negotiating an
agreement on algorithm parameters, especially at company level, as well as at
improving drivers’ working conditions. In particular, the following points are
analysed: deactivation by low rating, deactivation by cancellation and acceptance
rate, time to accept the request and time to wait for the passenger, earnings, and
surge pricing. The point is that all elements are now decided by the company, which
defines the parameters of the algorithms, whereby in our proposal they have to be



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negotiated in a collective agreement since they are directly or indirectly linked to
workers’ income.
        - Deactivation by low rating. After every trip, drivers and riders rate each other
on a scale of one to five stars, as a feedback on the trip. The driver’s rating is based
on the average of the number of post-trip stars evaluation, up to the last 500 rated
trips or the total number of rated trips, if less than 500. 9 There is a minimum
average rating to reach, that is unknown to drivers, under which the driver’s account
can be deactivated. The minimum average rating is different in each city, officially
because «there are cultural differences in the way people in different cities rate each
other».10
        We suppose that the minimum threshold could be a pre-set value (for
instance 4.0 stars) or a value depending on local drivers’ ratings, such as the average
value of the average ratings – which would be a tough requirement to meet – or a
low percentile of the statistical distribution of the average ratings (for instance the
5th percentile, which means that the 5% of the drivers with the lower rating can be
logged out of the app). In both cases, however, identifying a threshold is a highly
subjective choice, not only because of the human nature of the decision-maker (the
manager of the company), but also because the rating itself is expressed by other
human beings and affected by race, religion and gender biases (Kullmann, 2018;
Rogers, 2015); in this way the algorithms are exactly as racist or sexist as the
customers doing the rating (Dzieza, 2015). Some workers’ complaints in ratings
involve transparency about what they are being rated down for, the ability to protest
ratings they felt were unfair and better education for customers about ratings
meaning (Dzieza, 2015, Lee et al., 2015).
        Moreover, when the average rating falls below the minimum, drivers are
asked to successfully attend training classes, named quality improvement courses, in
order to regain access to the account.11 The same considerations about threshold
value and subjective evaluation, presented in the previous issue, can be expressed.
        - Deactivation by cancellation rate (number of cancelled trips divided by the total number of
accepted trips). A cancellation is when a trip request is first accepted and then
cancelled by the driver. Each city has a maximum cancellation rate, based on the
average cancellation rate of drivers in that area. It is specified that high-quality
drivers typically have a cancellation rate lower than 5%. 12 Since cancellation is a very
harmful occurrence for the company’s reputation, two different criteria, both

        9 Uber Community Guidelines, http://www.uber.com (last access 03.03.2018).
        10 Uber Community Guidelines, http://www.uber.com (last access 03.03.2018).
        11 Uber Community Guidelines, http://www.uber.com (last access 03.03.2018).
        12 Uber Community Guidelines, http://www.uber.com (last access 03.03.2018).




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effective, are explicit: the local average cancellation rate and a maximum value of
5%.
        - Deactivation by acceptance rate (number of accepted trips divided by the total number of
assigned trips). Although it is explained that not accepting trip requests does not lead
to permanent loss of a driver’s account, it is also specified that declining trip
requests in a consistent way leads to being logged out of the app, assuming that the
driver just does not want to accept more trips.13 Some interviewed drivers confirm
that declining too many trips leads to the threat of having their accounts closed
(Page et al., 2017). Sometimes, if a driver has an acceptance rate below a certain
threshold, he is encouraged to raise his acceptance rate through occasional
promotions that offer a guaranteed hourly pay (Lee et al., 2015) in this case the
threshold value is not indicated either, thus leading drivers to accept as many
assignments as possible (Lee et al., 2015).
        - Available time to accept the request. It is about ten seconds, although an
interviewed driver revealed it really was about 6 seconds (Page et al., 2017). And yet,
it should be highlighted that the driver is not aware of the passenger destination,
and therefore of the possible earning, until he accepts the request and picks up the
rider.
        - Available time to wait for the rider. It is about 5 minutes, but in the case of
UberPOOL drivers, admittedly they would wait for 1-2 minutes maximum, in order
not to hurt the customer who is already on board (Page et al., 2017).
        - Earnings. Earning system is based on a rate depending on time spent on the
trip [€/hour] and distance travelled [€/mile], with a minimum base rate for a ride.
All rates are different in each city and widely varying, but fixed by the company.
        - Surge pricing and demand-supply management. Sometimes, especially during large
events, pricing of specific local areas is dynamically modified in order to match
travel supply and demand. This is known as surge pricing when there is an extreme
shortage of drivers compared to the number of riders making requests in that area
(Page et al., 2017). It is confirmed by the Uber website too.14 Surge-priced areas are
showed in-App by using different colours in the map (Lee et al., 2015).
        Furthermore, when the surge is particularly high, drivers receive text
messages urging them to sign on and go to the surge area (Page et al., 2017). This
process probably involves market analysis, live-monitoring and forecasting of travel
demand, and economical considerations on demand-supply equilibrium. In
particular, in Uber platform live monitoring is made possible by controlling trip
requests and drivers available in a local zone.
        13   Uber Community Guidelines, http://www.uber.com (last access 03.03.2018).
        14   Uber Website, http://www.uber.com (last access 03.03.2018)



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        For what concerns travel demand estimation, two different techniques are
usually applied: either direct estimation or model estimation. The former involves
surveys presented to a representative random sample of users and inferential statistic
tools; historical analysis of travel demand data concerning Uber customers can also
be a helpful tool. The latter involves mathematical relationships, based on socio-
economic and transportation system attributes, weighted by parameters. Models
need to be specified, calibrated and validated, and be able to forecast future demand
too. Transportation supply is usually arranged on the basis of estimated travel
demand.
        However, some drivers complain that the ride request algorithm often works
against the incentives and surge pricing: they believed they could determine the price
and location where they wanted to drive, but are unable in reality (Page et al., 2017).
Furthermore, being in the surge area does not guarantee requests from within the
surge area (Lee et al., 2015). These statements could imply a subjective modification
on matching algorithm, depending on the individual characteristics of the driver, like
his rating, as already expressed in Section 3.


5.     Trade-union strategies for the negotiation of algorithms and ratings

        In this section we present some proposals on how collective bargaining can
intervene to improve drivers’ working conditions, by adopting as starting point the
results of the technical analysis.
        We analyse three main points related to Uber case: rating, transparency and
technical and procedural contents that could be included in a collective agreement.


5.1    Collective regulation of ratings

        The importance of rating is huge, since it probably defines the trips
assignment and the allocation work, the price of each rate (i.e. the wages), and also
the possible deactivation of worker’s account. Thus the issue is not only its
computation, but also how algorithms are used to dismiss or deactivate workers; and
that is why we propose to intervene in technical outputs in order to protect drivers
from discrimination and automatic dismissal (as we will see also in Section 5.3).
Indeed, technology is designed by the humans behind it.
        In order to prevent drivers from discrimination, a first challenge could be to
identify and remove those ratings that can be considered clearly discriminatory.



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        In cases of very low rates, Uber App could ask the customer to explain them
in a written motivation. In addition, some simple statistical tools can be involved in
an automated process. The average rating could be calculated by considering only
ratings in a confidence interval, so as to exclude extremely low rates that, in relation
to the average rating of the driver, most likely are intentionally discriminatory.
        In order to explain it, in figure 2 we report a hypothetical set of a driver’s star
ratings, collecting 500 ratings. In our scenario, two third of passengers have been
fully satisfied and they rate 5 stars, while almost everyone rates 4 or 5 stars (91%).
Only a small percentage of customers (5%) rates 1 star, an extremely negative
judgment compared to the large majority of the riders, and likely intentionally
penalising for discrimination reasons.




       Figure 2: hypothetical set of star ratings of a driver

        Statistics are reported in table 1. The average rating x is 4.47 stars, with a
standard deviation σ of 0.99 stars, calculated on the basis of n=500 star ratings. A
corrected average rating could be calculated by taking into account only ratings that
are internal to the interval x±2σ (or x±3σ) and excluding ratings which are very far
from the mean value in relation to the standard deviation of the rating set. In the
former case, ratings of 1 or 2 stars are excluded and the corrected average rating is
4.68 stars (+0.21 compared to the average rating x); in the latter one, ratings of 1
star are excluded and the corrected average rating is 4.65 stars (+0.18). Alternatively,
a trimmed mean can be calculated with the same purpose, by cutting off a
percentage α of the star ratings, assuming that α is the percentage of ratings affected
by biases in a bad or good way. The (α/2)·n lowest ratings and the (α/2)·n highest




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ratings are cut off for symmetry reason. For instance, with α=10% and n=500 (total
number of star ratings), 50 ratings are cut off (the 25 lowest ones and the 25 highest
ones), and the trimmed mean is calculated by taking into account the remaining
ratings. In the reported example, the trimmed mean with α=10% is 4.63 stars (+0.16
compared to the average rating x), while the trimmed mean with α=20% is 4.71
stars (+0.24).

              Total number of star ratings n                                                              500
              Average rating x                                                                            4.47
              Standard deviation σ                                                                        0.99
              x - 2σ                                                                                      2.50
              x + 2σ                                                                                      6.44
              Corrected Average Rating (2σ)                                                               4.68
              x - 3σ                                                                                      1.51
              x + 3σ                                                                                      7.43
              Corrected Average Rating (3σ)                                                               4.65
              Trimmed mean (α = 10%)                                                                      4.63
              Trimmed mean (α = 20%)                                                                      4.71
       Table 1: statistics of the star rating set

        These easy techniques can prove helpful in order to obtain more truthful
indicators when we encounter some extremely negative (or positive) judgments that
are totally conflicting with the large majority of the customers, but heavily weighing
on the driver’s rating. Indeed, they both give a rating that is significantly higher (or
lower) than the simple average rating, when there are many high (or low) star ratings
and few low (or high) ones. Obviously, a large enough set of data, statistically
significant, is needed. Instead, it is much more difficult to statistically identify biased
ratings when they are not so extreme in relation to the whole set of ratings for the
same driver.


5.2    Transparency duties and examples of crossed data analyses

        Information on the whole process, on the data collected during the labour
relation and their possible usage (Alvino, 2016) should be accessible to workers and
to collective actors.
        Although companies may be unwilling to share the mechanisms of their
assignment algorithms – as they might be patented or included in proprietary assets



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and property interests – more detailed knowledge about it can create favourable
workarounds (for example to avoid less economical rides for drivers) and can also
improve workers’ trust towards the algorithmic information (Lee et al., 2015; Donini
2015).
        In particular, we consider absolutely critical to know how the matching
algorithm exactly works and if the driver-passenger proximity criteria, in terms of
road-network distance or estimated time, is actually respected. Furthermore,
minimum values of rating and acceptance rate, maximum value of cancellation rate
and earning rates should be accessible. In addition, information on passenger
destination should be opened to workers in order to better evaluate whether to
accept or not the trip request.
        Company database anonymously including drivers’ rating, cancellation and
acceptance rates should also be opened to collective actors. Its availability could
allow in-depth and crossed data analyses to estimate threshold values and negotiate
the fairest ones: for example, statistical distribution of rating and rates, possible
relationships among them and their effect on deactivation. It should be highlighted
that while star rating is a subjective value, depending on passengers’ judgment,
cancellation and acceptance rates are objective data, depending on the driver’s
decisions, although the driver has not acute awareness of all the information about
the requested trip.
        Under the hypothesis – deducible from Uber website and from some works
reporting drivers’ interviews or opinions (Page et al., 2017, Lee et al., 2015, Dzieza,
2015) – that rating, acceptance and cancellation rates are for each driver the
determiners of the deactivation of his account, and in particular, considering that a
low rating or a high cancellation rate leads to deactivation, the following table can be
extracted. It is symmetric and the conflicting inputs have been cut off, with the
symbol [X]; low/high values are related to the respective thresholds. We calculate
that, as a result of the values of each driver, the driver can be deactivated [Fired] or
not [Ok], while with the symbol [?] we indicate that the output cannot be
determined.




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                                           Rating            Acceptance rate        Cancellation rate

                                     Low         High        Low         High        Low          High

                       Low           Fired          X        Fired       Fired       Fired        Fired
      Rating
                       High                         ?          ?           ?          Ok          Fired

                       Low                                     ?           X           ?          Fired
 Acceptance rate
                       High                                                ?           ?          Fired

                       Low                                                             ?           X
 Cancellation rate
                       High                                                                       Fired

       Table 2: probable effects of rating, acceptance rate and cancellation rate on deactivation

        Examining probable effects of ratings and rates on deactivation, we consider
that rating and cancellation rate have the main impact on the company’s decision,
while the acceptance one seems to have a less significant role for deactivation. Thus,
in a collective bargaining we suggest to focus on threshold values, especially for
rating (minimum threshold) and cancellation rate (maximum threshold) in order to
negotiate more favourable values for workers. In addition, with the aim of a more
precise evaluation and a better negotiation, we propose to compare the percentages
of deactivation for each combination of issues (rating, acceptance rate, cancellation
rate), to discover their relative weight and the one which would be most relevant.
        For instance, let it be y the percentage of deactivation with low rating, but
high acceptance rate and low cancellation rate. Let it be z the percentage of
deactivation with high rating and high acceptance rate, but high cancellation rate. If
y > z in a considerable way, the first case is more significant and the main issue is to
discuss the minimum rating to achieve, which is too high for the drivers; so that, a
collective bargaining should aim at lowering it.




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5.3. Collective regulation and trade-union implication to limit employer’s
power to deactivate (dismiss) workers

        Collective agreements should guarantee to trade unions the control of
technological paths in order to improve drivers’ working conditions. We emphasize
the importance of establishing a stable presence of a work council (or trade unions
representatives) in order to guarantee a permanent control on the exercise of the
employer’s power and on the respect of the conditions previously established in the
collective agreement15.
        In accordance to ILO Conventions n. 87 and 98, workers should exercise the
fundamental rights to previous information and consultation and the right to open
negotiations for a collective bargaining. Collective actors should have a guaranteed
access to all information on technical procedures and company database, for
example when it comes to matching algorithm parameters, deactivation thresholds,
acceptance, cancellation and earning rates; asymmetries in information between
labour and management produce inefficient social outcomes and must be overcome.
        According to the contents of a collective bargaining in the matter of
technological innovation and working conditions, we remark the importance of a
negotiation on some threshold values (deactivation, acceptance and cancellation
rates, as well as the time to accept the request of a trip), as they affect drivers’
working conditions and represent the most significant restrictions for them.
        Moreover, we consider that collective agreements should also intervene in
dismissal and disciplinary powers, in order to establish compelling procedural rules
to make the right to a fair trial effective and to protect workers from unfair or
discriminatory dismissals. On this sense, the right to be heard and the respect of the
adequacy and proportionality of disciplinary measures, according to workers’
conduct, could guarantee workers from illegal employer’s measures.
        In case of a potential driver’s conduct which could be charged by a
disciplinary measure, we propose the introduction of a compelling clause to have
previously informed both, the worker and trade union representatives (or,
alternatively, the work council) about the low rating, before putting it into action. A
special committee, composed by the company and workers representatives, could be
constituted in order to verify potential disciplinary responsibilities, guaranteeing a

        15  Collective agreements should also introduce a specific regulation of working hours, in
accordance with the legal framework, and specific limits to maximum weekly working time, night
work and minimum rest periods, guaranteeing to workers the right to disconnect. They should also
include the matter of occupational health and safety, with rules on the prevention of occupational
risks, the protection of health and safety, the elimination of risk and accident factors in the specific
sector.



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worker’s right to be heard and the assistance of a trade union representative.
Additionally collective agreements could establish procedural rules in the interest of
guaranteeing a written notice with the relative claim and the worker’s audience
before the application of the disciplinary measure, a special committee composed by
representatives of CEO and trade unions (or of the work council) and a negotiated
code of conduct specifying infractions and correlative sanctions and inspired to the
principle of proportionality.
       Finally, trade union pressure could intervene also on salaries and earnings
which should be as objective as possible, for instance negotiating better wages and
earning rates (to miles and time).


6. Organisational challenges for trade-union representatives

6.1     Some examples of trade-unions multi-level coordination

        Negotiating coding in a hypothetical collective bargaining requires collective
actors with the bargaining powers to define it. A preliminary question is therefore
the state of trade union representation: while the buds of restating the status of
Uber drivers as employees appear in the last two years, organising and
representation are even more embryonic.
        In this regard, it could be useful to firstly describe some collective initiatives
in the framework of atypical forms of work, and particularly in gig-work, as well as
analysing forms of coordination between different levels of workers’ representation,
at local, sectorial and international level, in response to capital movements and
transnational companies.
        On the one hand, new independent trade unions are arising: IWGB (the
Independent Workers Union of Great Britain), which organised Uber drivers’
protests, IWA (the International Web Association), the Italian ACTA (Associazione
Consulenti Terziario Avanzato), and Teamster Union in the US16 (Lassandari, 2017). On
the other hand, traditional trade unions are approaching gig-workers at sectorial
level. For instance, two German trade unions, IG Metall and Ver.di., created two

        16  Teamster Local 117 represents 16500 men and women at 200 employers across
Washington State. Its campaign “Raise Up. Make Uber Jobs good jobs” focuses on earning a living
wage and minimum standards. Information is available on its official website
https://www.teamsters117.org.
        Trade-off between worker-run organisation and local unions is carried out; for example, in
transportation the Teamster Union has been supported by the App-Based Driver’s Association
(ABDA) and the Western Washington Taxicab Operator’s Association in expressing workers’ voice.



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platforms17 where crowd-workers exchange information on working conditions and
enter into a critical dialogue.
        In transport sector examples of social dialogue and collective bargaining are
developing. The Swedish transport trade union Svenka Transportarbetareforbundet and
the company Bzzt signed a new collective agreement in transport services. The
Austrian transport and service union Vida18 supported the constitution of a work
council, called Betriebstrat, to represent Foodora riders in Vienna, with the main goal
to negotiate an agreement on couriers’ working conditions.
        At general level, trade union confederations are still working on proposals to
better approach gig-workers, although they are still far from this goal; for instance,
the trade union confederations CGIL and CCOO are developing Projects 4.0 on the
regulation of gig works and the development of social dialogue 19 (Faioli, 2017).
        At European and global level, some trade unions are also compelling
transnational cooperation and social dialogue with gig-economy platforms. ETUC
impels towards social dialogue with trade unions, especially worried for precarious
work of crowd-workers, and endorses the regulation for gig-work in the framework
of EU initiative on the European Social Pillar20, especially in the field of social
protection and the employer’s obligation to inform employees of the conditions
applicable to the contract or employment relationship (European Commission,
2017)21.



        17 The German Metalworkers’ Union (IG Metall), the Austrian Chamber of labour (AK,
Arbeiterkammern), the Austrian Trade Union Confederation (OGB) and the Swedish white collar
union Unionen have created a platform, called Fair CrowdWork, which collects information about
crowd work, app-based work, and other platform-based-work from the perspective of workers and
unions. The site also offers rating of working conditions on different online labour platforms based
on surveys with workers. The web-site of the platform is http://faircrowd.work.
        18 In Austria works councils have legal rights to information and consultation on major

business decisions. The information is available on the trade union platform Fair Crowd Work,
where OGB also takes part, the Austrian Trade Union Federation, to which “Vida”, the Transport
and Service Union, is affiliated.
        19 CGIL “Progetto lavoro 4.0. Primo report di attività. Italia 2030. Per un’innovazione

socialmente sostenibile”, cit. CCOO Report “Resumen propuestas de actuación sindical. Industria
4.0.          Una              apuesta          colectiva”            is         available         at
http://industria.ccoo.es/3726499875c9feb2f83c5e2d866a4a0d000060.pdf
        20 More information is available at https://ec.europa.eu
        21 The possible revision of Directive 91/553/ ECC on an employers’ obligation to inform

employees on the conditions applicable to the employment contract, known as the Written
Statement Directive, is one of the initiatives announced in the framework of the Pillar. The
proposal to revise the Directive aims at improving workers’ clarity on their contractual relations
and, by ensuring this protection to all workers, irrespective of the type of employment relationship,
including those in atypical and new forms of work.



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        UNI Global Union22 – representing more than 20 million workers from over
900 trade unions in skills and services – and a network of Central Europe and North
America trade unions are pursuing this goal too. In particular, in the Frankfurt
Declaration23 they impel towards a union strategy of universalization of some core-
labour standards to all workers, independently of their contractual status: the right
to negotiate collective agreements, in accordance with ILO Labour Standards; to
receive at least a minimum wage according to the applicable jurisdiction (or, in
jurisdiction with no minimum wage, the wage specified in the relevant collective
agreement) for their work; the access to social protection (such as unemployment
insurance, disability insurance, health insurance, pension, and compensation in the
event of work-related illness or injury).


6.2 Dilemma and strategies for a universalization of labour rights

        The initiatives previously described have faced nevertheless many obstacles
thus far for many reasons.
        Firstly, atypical and precarious jobs make workers’ representation much more
difficult: the fragmentation of the workforce (Rogers, 2015), its rapid turnover and
the difficulties for trade unions to establish sustained relationships and to reach and
organise gig-workers according to traditional collective strategies have to be
considered (European Parliament – Directorate General for internal policies, 2017,
72). That is why this workforce has low level of unionisation and is less likely to
have the bargaining power to negotiate working conditions, rights and income levels
(ILO, 2016b).
        Secondly, union organisation in the workplace is extremely fragmented in
gig-works. On the one hand, in many countries collective rights are recognised only
to employees (European Parliament – Directorate General for internal policies,

        22    UNI Global Union “Report on the future world of work”, available at
www.thefutureworldofwork.org.
         23 The Frankfurt Paper on Platform-based work (or Frankfurt Declaration) is a joint

declaration regarding Proposals for platform operators, clients, policy makers, workers, and worker
organisations. Participating organisations are the same trade unions which signed the platform Fair
Crowd Work: Austrian Chamber of Law (Arbeiterkammer), Austrian Trade Union Federation
(OGB), Danish Union of Commercial and Clerical Workers (HK), German Metalworkers’ Union
(IG Metall), International Brotherhood of Teamster Local 117 (USA), Service Employees
International Union, and Unionen. The text of the Declaration, signed on the 6 th December 2016,
is available at IG Metall web-site.
         https://www.igmetall.de/docs_20161214_Frankfurt_Paper_on_Platform_Based_Work_E
N_b939ef89f7e5f3a639cd6a1a930feffd8f55cecb.pdf.



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2017, 72). This makes much more difficult to create connections between works
councils and trade unions, which should be strengthened to organise collective
actions, negotiation and conflict. The workplace is indeed a strategic level to
mobilise workers and support industry collective action (Rogers and Streeck, 1995).
On the other hand, the lack of collective guarantees creates the conditions for
paternalistic unions hampering genuine collective actors, as the Uber case
demonstrates24.
        Lastly, international markets and competition on a global scale, based on
cutting labour costs, make local initiatives ineffective and impel towards a global
approach to workers’ rights. Indeed, if the platforms act at supranational level
(Uber, for example, operates in 81 countries25), cooperation between trade unions at
national and international level is critically needed in order to build a framework of
workers’ rights at global level (Lassandari, 2017). On this sense, a first step could be
impelling for an international framework agreement (IFA) with management, as a
collective tool to recognise core-labour standards to all drivers (at least the pillars of
decent work). The constitution of World Works Council at transnational level, as a
workers body with rights to be informed and consulted on the progress of the
business and any significant decision for their working conditions, is most worthy.
Despite this, the impediments to extend core-labour standards to all atypical
workers, independent contractors, workers of subcontractors or temporal agency
workers are simply huge. To the obstacles previously described, it has to be added
that international framework agreements only bound the signing parties, both on the
worker and on the company side (Baylos Grau, 2005). So that, they cannot be
extended neither to that subcontractors which refuse to adhere to them, nor to
independent contractors and to some forms of atypical workers, making the control
of the global supply-chain extremely hard (ILO, 2016a).
        The recognition of legal and collective labour rights to atypical workers (and
particularly to gig-workers) is thus hampered by their contractual status, according
to the applicable legal systems, to Court statements and arbitration clauses avoiding
misclassification sentences. Social protection and labour rights have been designed

        24  Uber recently entered into a Settlement, under whose terms it will continue classifying its
drivers as independent contractors and it agrees to help create an Uber “drivers association” in
both California and Massachussetts. The settlement makes clear that the company won’t view the
association as a labour union, nor will it grant it “the right or capacity to bargain collectively with
Uber. But drivers will elect leaders of the association, who can discuss drivers’ concerns “in good
faith” with Uber management. The settlement also establishes that “Uber will provide some
funding to the Driver Association to pay for incidental expenses (phones, printing, meeting space)
and to carry out its basic functions”.
         25 As reported in Uber website: https://www.uber.com/en-ES/country-list/ (last access

5.05.2018).



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on the grounds of standard form of employment, as a concrete activity of a worker
defined in a precise society by a specific mode of production, and this has left non-
standard workers with patchy coverage (Riesco-Sanz, 2012). The major obstacles to
the universalization of labour rights remit thus lastly to the same labour law
architecture.


6.3 First attempts for an approximation of atypical workers to standard
employees?

        Despite the obstacles to the universalization of labour rights, some attempts
to overcome it and its main obstacle (the recognition of labour rights on the
grounds of employment contractual status) have to be registered, although in this
paper they can be treated only as conclusive remarks.
        For instance, the same definition of employee has been submitted to broader
interpretations in many legal systems (Spain and Italy for example) in order to
enlarge the scope of the application of employees’ rights and guarantees. The same
European Parliament underlines the precariousness and insecurity risks of atypical
jobs and “the risk that new forms of employment emerging in the context of
digitalisation and new technologies might be blurring the boundary between
dependent employment and self-employment”; that is why it calls on member States
to take into account ILO indicators to determine the existence of an employment
relationship (European Commission, 2017).
        At the same time, in some European countries some of the guarantees
recognised to standard forms of employment have been progressively extended to
some atypical workers (in Spain to TRADE workers, in Italy to “lavoratori
parasubordinati” and recently to “collaborazioni coordinate”, introduced by article 2
dlgs. n. 81/2015), jointly with social and collective rights, for example for freelancer,
workers in UK, economically dependent, self-employed workers in Spain (European
Parliament – Directorate General for internal policies, 2017; Perulli, 2015; Santoro
Passarelli, 2013; Goerlich Peset 2009; Trillo Párraga, 2008).
        At European level, the revision of EU law in the framework of the European
Social Pillar proposes to enlarge social protection to all workers, regardless of the
type of employment relationship, including those in atypical and new forms of work
(European Commission, 2017).
        Finally, at international level, a recent ILO Report (ILO, 2016b) proposes
different approaches to include non-standard workers in the collective bargaining
process; for instance, by recognising them legally the right to organise or bargaining



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collectively, facilitating the capacity of unions to be considered representative in
sectors employing a high proportion of non-standard workers, and promoting
actions to organise and collectively represent workers in non-standard employment.
        All these elements, which can only be treated briefly in this paper, would
suggest that atypical forms of employment are eroding the principle of employment
stability on which labour law has been built as well as hampering the access to legal
protections and rights recognised in collective agreements to an increasing number
of workers, altering the coherence of the whole theoretical labour law model.
        The attempts of approximating atypical workers to standard employees with
regards to some of the protections and guarantees of standard employment can be
actually read as that lever which seems to be currently transforming that model. It
could be interpreted as the first step of a process of socialisation of those rights and
conditions that originally were circumscribed only to a part of the national labour
force (Riesco-Sanz, 2016). In that evolving framework, collective agreements could
be one of the tools for the universalization of labour and social rights also to
atypical jobs.
        Involving historical trade unions in the process of universalization of labour
and social rights is crucial for two main reasons. Sectorial and national trade unions
have a strong bargaining power to reach collective agreements, supported by their
respective affiliations, and they have a public role in the guarantee, maintenance and
reproduction of the workforce (Rolle, 2003). In fact, trade union confederations
take active part in public policies, managing working and life conditions of the
whole salaried class in different ways: directly, by means of collective agreements
and as stakeholders of public powers in tripartite social dialogue, and indirectly, as
transmission belt to left parties.


7. Concluding remarks

        The analysis of Uber case shows how algorithms and rating systems are
increasingly becoming central tools to manage labour relations.
        Our standpoint is that managing technological inputs and values is the first
point to negotiate algorithm parameters. In this regard, the negotiation of
algorithms is also a goal recently underscored by trade union confederations, such as
the Italian CGIL, which highlights the need to have knowledge of and negotiate the
basic principles of the software employed in order to bring a contractual regulation
of algorithm forms and guarantee to trade unions the control of work in the value




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chain. 26 It also requires discussing how trade unions are coping with the need to
master such skills and how they should develop such expertise (considering that
different platforms may require different solutions). Indeed, despite the fact that in
this paper we refer to the case-study, this method has not necessarily to be limited
to Uber, as algorithms, ratings and technological tools are used to manage workers
by an increasing number of companies (Rodríguez-Piñero Royo, 2006).
        Thereby, in our opinion, an analysis based on a multidisciplinary approach,
with the interaction of technical and legal studies, can be effective on a double level.
        On a technical level, transport engineering and statistical data analysis can be
used to study the application of technologies on labour relations and to propose
alternative solutions on parameters and values. From that starting point, on legal
level, different solutions can be negotiated between the company and workers’
representatives. The agreement can fix, for example, fairer threshold values
(acceptance and cancellation rates) to measure workers’ performance, remove clearly
discriminatory rates, manage surge pricing and earning rates.
        The proposal to include technological parameters in a collective agreement
pursues two main goals. The first one is to intervene as a party in the
implementation and management of technologies and, by that, to reduce the
employer’s powers of direction and control. The second one is to further strengthen
the role and power of collective actors in the companies of the so-called gig-
economy, in order to organise collective actions and negotiate better wages and
working conditions.
        Nevertheless, negotiating coding in a hypothetical collective bargaining
requires collective actors with the bargaining powers to define it, with the challenges
and difficulties described in the previous paragraph.
However, in this framework the role of trade unions is still decisive at least in three
fields: in representing gig-workers, especially considering the drastic imbalance of
bargaining power in the “gig-economy” (Johnston and Land-Kazlauskas, 2018); in
impelling the internationalisation of trade union movement, conflicts and strategies
against capitals (Baylos Grau, 2008); and lastly, in promoting litigation in the form
of class actions against big companies. From that point of view, trade unions should
re-thin their representative mechanisms (Dirringer, 2017), in order to open union
representation to all workers, independently of the type of contract, under the
common framework of salaried class.

         26 CGIL, “Progetto lavoro 4.0. Primo report di attività. Italia 2030. Per un’innovazione

socialmente sostenibile”, Conferenza di Programma, Milan 30-31 January 2018, especially
interesting at point 8 (p. 26) on the negotiation of algorithm. The document is available at
http://www.cgil.it/cose-progetto-lavoro-4-0/



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References
Aloisi A., Case study research on labour law issues arising from a set of “on-demand/gig economy platforms, in
        CLLPJ, 2016, 37, pp. 620-653.
Alvino I., I nuovi limiti al controllo a distanza dell’attività dei lavoratori nell’intersezione fra le regole dello Statuto
        dei lavoratori e quelle del Codice della privacy, in LLI, 2016, 2, pp. 3-45.
Baylos Grau A., El espacio supranacional de ejercicio del derecho de huelga y la restricción legal de sus capacidades
        de acción, in RDS, 2008, 41, pp. 123-143.
Baylos Grau A., Códigos de conducta y Acuerdos Marco de empresas globales: apuntes sobre su exigibilidad
        jurídica, in Lan Harremanak, 2005, I, pp. 103-138.
Cardona Rubert M.B., Las relaciones laborales y el uso de las tecnologías informáticas, in Lan Harremanak,
        2003, pp. 157-173.
Cina A., GPS: Princìpi, modalità e tecniche di posizionamento, Torino, Celid, 2004.
De Stefano V., The rise of the «just-in-time workforce»: on-demand work, crowdwork and labour protection in the
        “gig-economy”. Conditions of Work and Employment Series OIT, 2015, n. 71.
Degryse C., Digitalisation of the economy and its impact on labour markets, WP ETUI, 2016, n. 2.
Dijkstra E.W., A note on two problems in connexion with graphs, in Numerische Mathematik, 1959, 1, pp.
        269-271.
Dirringer J., Progetti e sviluppi normative per la tutela del lavoro nelle piattaforme digitali in alcuni Paesi europei e
        nell’Unione Europea, in RGL, 2017, 2, 147-164.
Donini A., Il lavoro digitale su piattaforma, in LLI, 2015, 1, pp. 52-71.
Dzieza J., The rating game. How Uber and its peers turned us into horrible bosses, The Verge (online) available
        at https://www.theverge.com/2015/10/28/9625968/rating-system-on-demand-economy-
        uber-olive-garden.
Faioli M., Jobs «App», gig economy e sindacato, in RGL, 2017, 2, pp. 291-305.
Goerlich Peset J.M., La noción de trabajo autónomo económicamente dependiente: puntos críticos, in Justicia
        Laboral, 2008, 33, pp. 14-47.
Górriz López C., Uber. Transportes de pasajeros y competencia desleal”, in Revista de Derecho terrestre,
        marítimo, aéreo y multimodal, 2015, 16, pp. 77-98.
Hart P.E. - Nilsson N.J. - Raphael B., A Formal Basis for the Heuristic Determination of Minimum Cost
        Paths, in IEE Transactions on Systems Science and Cybernetics SSC4, 1968, pp. 100-107.
Horan H., Will the growth of Uber increase economic welfare?, in Transportation Law Journal, 2017, 44: 33,
        pp. 33-105.
Howe J., The rise of Crowdsourcing, in Wired Magazine, 14 June 2006.
Huws U. - Spencer N. - Syrdal D. - Holts K., Work in the European Gig Economy. Reseacrh results from
        the UK, Sweden, Germany, Austria, The Netherlands, Switzerland and Italy, FEPS, UNI Europa,
        University of Hertfordshire, 2018.
Hwang S. - Yu D., GPS localization improvement of smartphones using built-in sensors, in International Journal
        of Smart Home, 2012, Vol. 6, No. 3, pp. 1-8.
Johnston H. - Land-Kazlauskas C., Organizing on-demand: representation, voice, and collective bargaining in
        the gig economy, Conditions of Work and Employment Series OIT, 2018, n. 94.
Kullmann M., Platform Work, Algorithmic Decision-Making and EU Gender Equality Law, in IJCLLIR,
        2018, 1/34, pp. 1-21.
Lassandari A., Problemi di rappresentanza e tutela collettiva dei lavoratori che utilizzano le tecnologie digitali, in
        RGL, 2017, 2, pp. 59-70.




                LLI, Vol. 4, No. 2, 2018, ISSN 2421-2695                                                             C. 49
          G. BIRGILLITO - M. BIRGILLITO, Algorithms and ratings: tools to manage labour relations



Lee M.K. - Kusbit D. - Metsky E. - Dabbish L., Working with Machines: The Impact of Algorithmic and
         Data-Driven Management on Human Worker, in Proceedings of the 33rd Annual ACM Conference on
         Human Factors in Computing Systems, New York: ACM New York, 2015, pp. 1603-1612.
Page X. - Marabelli M. - Tarafdar M., Perceived Role Relationships in Human-Algorithm Interactions: The
         Context of Uber Drivers (paper presented at the Thirty Eighth International Conference on
         Information Systems ICIS), 2017.
Parker G. - Van Alstyne M. - Choudary S., Platform revolution, 2016, New York, Norton&Company.
Perulli A., Il lavoro autonomo, le collaborazioni coordinate e le prestazioni organizzate dal committente, in WP
         CSDLE Massimo D’Antona.IT - 272/2015.
Prassl J. - Risak M., Uber, Taskrabbit and Co: Platforms as Employers? Rethinking the Legal Analysis of
         Crowdwork, in CLLPJ, 2016, No.37, pp. 619-651.
Riesco-Sanz A., Trabajo, independencia y subordinación. La regulación del trabajo autónomo en España, in
         Revista Internacional de Sociología, 2016, 74, 1, p. 26.
Riesco-Sanz A., Empresas sin asalariados y asalariados sin empresas: apuntes sobre la crisis y transformación del
         empleo, in Lan Harremanak, 2016, II, pp. 134-148.
Rifkin J., La era del acceso: la revolución de la nueva economía, Barcelona, Paidós, 2013.
Rao G.S., Global Navigation Satellite Systems, New Delhi, Tata McGraw-Hill, 2010.
Rodríguez Fernández M.L., Plataformas, microworkers y otros desafíos del trabajo en la era digital, in Mora
         Cabello de Alba L., Rodríguez Fernández M.L. (eds.) El futuro del trabajo que queremos,
         Albacete: Bomarzo, 2017, pp. 95-113.
Rodriguez-Piñero Royo M., Las nuevas tecnologías en la empresa. Guías de negociación, Consejo Andaluz
         de Relaciones laborales, 2006.
Rogers B., The social costs of Uber, in Temple University Legal Studies - Research Paper 2015, n. 28.
Rogers J., Streeck W. ed., Works Councils. Consultation, representation, and cooperation in Industrial
         Relations, Chicago, University of Chicago Press, 2015.
Rolle P., Por un análisis ampliado de la relación salarial, in Cuaderno de Relaciones Laborales, 2003, 21, pp.
         145-175.
Santoro Passarelli G., El trabajo autónomo económicamente dependiente en Italia, in Documentación Laboral,
         2013, 98, pp. 9-18.
Silberman M., Irani L., Operating an employer reputation system: lessons from Turkopticon, 2008 - 2015, in
         CLLPJ, 2016, vol. 37, 3, pp. 472 - 505.
Trillo Párraga, Uber, ¿sociedad de la información o prestadora de servicios de transporte? Comentario a la sentencia
         del Tribunal de Justicia (Gran Sala), de 20 de diciembre de 2017, in RDS, 2017, 80, pp. 127-138.
Trillo Párraga F., Derechos colectivos del trabajador autónomo económicamente dependiente, in Documentación
         Laboral, 2009, 85, pp. 89-116.
Valenduc G.- Vendramin P., Work on the digital economy: sorting the old from the new, in ETUC WP,
         2016.




               LLI, Vol. 4, No. 2, 2018, ISSN 2421-2695                                                      C. 50
