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ALGORITHMS IN THE WORKPLACE – THE RISE OF ALGORITHMIC MANAGEMENT


July 26, 2021
by Lewis Silkin LLP

With algorithms playing an increasingly fundamental role in our lives, Lee Nair,
Managing Associate and Jasmin Stevens, Trainee Secondee from Lewis Silkin LLP
identify potential workplace issues around bias, data protection, trust and good
work in the context of the rise of algorithmic management and highlight the
suggested recommendations from recent studies by the Institute for the Future of
Work and ACAS.


ALGORITHMS IN THE WORKPLACE

Algorithms play an increasingly fundamental role in our lives, and the workplace
is no exception. Accelerated by the Covid-19 crisis, the adoption of data-driven
algorithmic systems that control how, when and where we work has rapidly
increased.  While two studies, by the Institute for the Future of Work and ACAS,
recognise the benefits algorithms and AI can offer in the context of the
workplace, they also identify a number of unintended consequences. In this
article we look at the sorts of workplace issues that arise and the suggested
recommendations set out in the studies.


ALGORITHMS, AI AND MACHINE LEARNING

The term “algorithm” encompasses a variety of different types of systems and
technologies with a range of capabilities. Some algorithms are relatively simple
in terms of data collection, but other types of AI and machine learning enable
systems to learn for themselves how to achieve a set goal, rather than merely
following pre-programmed steps.


THE RISE OF “ALGORITHMIC MANAGEMENT”

“Algorithmic management” is a relatively recently coined phrase to describe the
way in which AI can be used in the workplace. The ACAS study My boss the
algorithm: an ethical look at algorithms in the workplace notes that the term
“algorithmic management” reflects a diverse set of technological tools and
techniques to remotely manage workforces, relying on data collection and
surveillance of workers to enable fully-automated (without human assistance) or
semi-automated (which informs and shapes human decision making) decision making.
The extent to which decision making is automated or semi-automated varies by
system and business approach to deployment.

The IFOW study The Amazonian Era – How algorithmic systems are eroding good work
notes that the impacts of automation through algorithmic management are varied
and identifies categories:

View fullsize



BIAS AND ALGORITHMIC MANAGEMENT

Algorithms have the potential to reduce subjective and sub-conscious bias
involved in decisions made by humans but there is a growing debate around
whether algorithms increase or diminish bias and unlawful discrimination in
employment decisions. Using algorithms (both “off the shelf” and bespoke
programmes incorporating machine learning) in the employment context can pose
significant risks for employers, and there have been some high-profile examples
of the risks of getting it wrong.  For example, a few years ago Amazon abandoned
its AI-developed recruitment tool (that was four years in the making) as it
reportedly favoured male candidates. The ICO has published guidance on AI and
data protection setting out ways of mitigating risks of discrimination when
using AI systems.


DATA PROTECTION AND ALGORITHMIC MANAGEMENT

Algorithmic systems at work can be deployed to perform a number of traditional
HR and managerial tasks, such as recruitment, task-allocation, performance
management and monitoring. These systems can offer transformative benefits,
providing valuable data driven insights to the workforce. However, handled the
wrong way they can risk direct conflict with fundamental principles of data
minimisation and transparency embedded in the General Data Protection Regulation
(GDPR). Employers must also implement suitable safeguarding measures under the
GDPR if relying on purely automated decision-making which affects employees. In
order to comply with GDPR principles while taking full advantage of new
technologies, employers need to consider each case on its facts and balance (i)
the interests of the data subjects protected by the GDPR; and (ii) the
employer’s interests in investing in new data driven, people analytics
technologies within the workplace.


JOB SATISFACTION, TRUST, AND ALGORITHMIC MANAGEMENT

Algorithms are particularly prominent in the gig economy, where individuals may
receive the entirety of their instructions and feedback via a platform. This
technology can promote flexibility to accept jobs which are convenient because,
for example, they are close by and won’t take long to complete, and can be
juggled with jobs on other platforms. This flexibility is a significant draw to
the gig economy for many individuals.

The IFOW’s study recognises that as the practices and business models of the gig
economy are being extended beyond gig work to many other sectors, resulting in a
more widespread restructuring of workplace behaviours, relationships and jobs
which could have negative consequences for the workforce.

AI systems can have a significant impact on human interaction and job
satisfaction. Where employers adopt algorithmic systems to take on increasing
aspects of a manager’s role or where work is redefined in narrow, measurable
terms, this can increase work pressure, undermine the value of human skill and
judgement and significantly impact on the interpersonal relationships between
managers and their direct reports. Algorithms, particularly those measuring
workforce productivity, also risk eroding employees’ trust if not used in an
upfront, considered and proportionate manner.


GOOD WORK AND ALGORITHMIC MANAGEMENT

The IFOW’s study highlights the potential impact of algorithmic management on
the following headline areas of what, in aggregate, make up ‘Good Work (work
that promotes dignity, autonomy, equality, has fair pay and conditions and where
people are supported to develop their talents and have a sense of community):

 * Access, fair pay and conditions: where management platforms are used not just
   to manage performance, but also to determine employment terms (including
   tasks, shifts, pay and working time) by way of algorithmically predicted
   performance, it can diminish fair, open and consistent standards of work and
   damage morale.

 * Dignity, autonomy, and equality: a shift from managerial trust and
   transparent dialogue towards micro-management through intense monitoring and
   surveillance, by collecting data about every aspect of working life, can
   undermine the dignity, autonomy and trust of workers. It can create a culture
   of proof, as opposed to a culture of trust. In addition, workers may fear
   engaging in conversation with colleagues due to a lack of privacy, which
   dehumanises the job further.

 * Learning and development: algorithmic systems can be used to capture
   knowledge, in a bid to create a “GPS” type manual of work, a template for
   future digital instruction, so that anyone can do the job.  This can save
   time, lead to faster decision making and increase productivity.  However,
   channelling staff to just one way of doing things can also limit
   opportunities for innovation, human skill and judgement.


RECOMMENDATIONS - ALGORITHMS IN THE WORKPLACE

Both the IFOW and ACAS studies make clear that the adoption of algorithms, AI
and machine learning in the workplace must be done with care. The studies make a
number of recommendations which include:

 * Agreed standards on the ethical use of algorithms around bias, fairness,
   surveillance, and accuracy.

 * Using algorithms to advise and work alongside human line managers, but not to
   replace them - a human manager should always have final responsibility for
   any workplace decisions.

 * Line manager training on how to understand algorithms and how to handle an
   ever-increasing amount of data responsibly.

 * Greater transparency for employees (and prospective employees) about when
   algorithms are being used and how they can be challenged, particularly in
   recruitment, assignment of work and performance management.

 * A new Employment Bill with a dedicated schedule of ‘Day 1’ digital rights
   providing new protections for all staff, irrespective of employment status,
   including rights to security, knowledge, involvement, and human contact.  The
   Bill could also offer a right to disconnect – an idea gathering momentum
   following Ireland’s introduction of a statutory Code of Practice and the UK
   Government’s calls to ban bosses from contacting workers out of hours.

 * A new Accountability for Algorithms Act in the public interest requiring
   early algorithmic impact assessment and adjustment when adverse impacts are
   identified. This assessment could extend to equality impacts and the
   physical, mental, and financial risks of labour intensification.

 * Investigation, research and guidance by the Health and Safety Executive on
   the health risks from the intensification of work under management by
   algorithmic systems.

 * New mandatory disclosure obligations requiring regular reporting on the fact,
   purpose and outcomes of algorithmic systems shaping access, terms, and
   quality of work.

 * Collective bargaining covering the use of algorithmic systems and new
   collective rights for involvement and review when algorithmic systems are
   introduced.

 * Employee contracts, collective agreements, technology agreements and employee
   privacy notices to include explicit commitments about the employer’s
   collection and use of employee data through algorithmic systems.

 * Company reporting on equality and diversity, such as around the gender pay
   gap, to include information on any use of relevant algorithms in recruitment
   or pay decisions and how they are programmed to minimise biases.


CONCLUSION - ALGORITHMS IN THE WORKPLACE

AI continues to permeate many aspects of our lives, transforming work and
working lives across a myriad of different sectors, working models and
workforces. Adopting data driven AI technologies has profound implications for
the experience, value and role of work and calls for a responsible, transparent
– and human – approach.


FURTHER READING

 * Our recent virtual discussion series “Technology, trust and the evolving
   employment “deal”, focussed on the changing relationships between employers
   and their workforce and the impact of technology and trust on the employment
   “deal”. You can find out more here.

 * At our event “HR in the age of big data, AI and algorithms” we captured
   insights from our panellists - leading practitioners and thinkers in this
   area. You can watch this here.

 * James Davies, partner at Lewis Silkin LLP, has written an in depth analysis
   on  algorithms and employment law, looking at the potential increase in
   claims about algorithms and discrimination in the years ahead and why the
   current employment law framework is ill-equipped to deal with this.

 * Terrel Douglas and Shalina Crossley at Lewis Silkin LLP have written about
   the implications of a right to disconnect in the UK in this article here.

 * Adrian Wakeling, Senior Policy Adviser at ACAS, wrote for the Future of Work
   Hub, taking a detailed look at the ACAS report My boss the algorithm: an
   ethical use of algorithms at work.

 * Jeremias Adams-Prassl, a Professor of Law with a particular interest in the
   future of work, explores the rise of the “algorithmic boss” in this article
   AI driven decision making: the rise of the "algorithmic boss" written for the
   Future of Work Hub.





This article was written for the Future of Work Hub by Lee Nair, Managing
Associate and Jasmin Stevens, Trainee Secondee at Lewis Silkin LLP.

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