Good Intentions Bad Inventions How Employees Judge Pervasive Technologies

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Good Intentions, Bad

Inventions: How Employees

Judge Pervasive Technologies

in the Workplace

Marios Constantinides

Nokia Bell Labs, Cambridge (UK)

Daniele Quercia

Nokia Bell Labs, Cambridge (UK)

Abstract—Pervasive technologies combined with powerful AI have been recently introduced to

enhance work productivity. Yet, some of these technologies are judged to be invasive. To identify

which ones, we should understand how employees tend to judge these technologies. We

considered 16 technologies that track productivity, and conducted a study in which 131

crowd-workers judged these scenarios. We found that a technology was judged to be right

depending on the following three aspects of increasing importance. That is, whether the

technology: 1) was currently supported by existing tools; 2) did not interfere with work or was ﬁt

for purpose; and 3) did not cause any harm or did not infringe on any individual rights. Ubicomp

research currently focuses on how to design better technologies by making them more accurate,

or by increasingly blending them into the background. It might be time to design better

ubiquitous technologies by unpacking AI ethics as well.

Index Terms: H.1.2.b Human-centered

computing; C.3.h Ubiquitous computing

THE INTRODUCTION New pervasive technolo-

gies in the workplace have been introduced to

enhance productivity (e.g., a tool that provides

an aggregated productivity score based on, for

example, email use on the move, network con-

nectivity, and exchanged content). Yet, some of

them are judged to be invasive so much so that

they make it hard to build a culture of trust at

work, and often impacting workers’ productivity

and well-being in negative ways [2]. While these

technologies hold the promise of enabling em-

ployees to be productive, report after report has

highlighted the outcries of AI-based tools being

biased and unfair, and lacking transparency and

accountability [6]. Systems are now being used to

analyze footage from security cameras in work-

place to detect, for example, when employees are

not complying with social distancing rules1; while

there is a handful of good intentions behind such a

technology (e.g., ensuring safe return to the ofﬁce

after the COVID-19 pandemic), the very same

technology could be used to surveil employees’

movements, or time away from desk. Companies

1https://www.ft.com/content/58bdc9cd-18cc-44f6-bc9b-8ca4a

c598fc8

under submission 1

arXiv:2210.06381v2 [cs.HC] 13 Oct 2022

now hold protected intellectual properties on tech-

nologies that use ultrasonic sound pulses to detect

worker’s location and monitor their interactions

with inventory bins in factories.2

As we move towards a future likely ruled

by big data and powerful AI algorithms, im-

portant questions arise relating to the psycho-

logical impacts of surveillance, data governance,

and compliance with ethical and moral concerns

(https://social-dynamics.net/responsibleai). To

make the ﬁrst steps in answering such questions,

we set out to understand how employees judge

pervasive technologies in the workplace and, ac-

cordingly, determine how desirable technologies

are supposed to behave both onsite and remotely.

In so doing, we made two sets of contribu-

tions: First, we considered 16 pervasive technolo-

gies that track workplace productivity based on

a variety of inputs, and conducted a study in

which 131 US-based crowd-workers judged these

technologies along the 5 well-established moral

dimensions of harm, fairness, loyalty, authority,

and purity [9]. We found that the judgments of a

scenario were based on speciﬁc heuristics reﬂect-

ing whether the scenario: was currently supported

by existing technologies; interfered with current

ways of working or was not ﬁt for purpose; and

was considered irresponsible by causing harm

or infringing on individual rights. Second, we

measured the moral dimensions associated with

each scenario by asking crowd-workers to asso-

ciate words reﬂecting the ﬁve moral dimensions

with it. We found that morally right technologies

were those that track productivity based on task

completion, work emails, and audio and textual

conversations during meetings, whereas morally

wrong technologies were those that involved

some kind of body-tracking such as tracking

physical movements and facial expressions.

RELATED WORK

On a pragmatic level, organizations adopted

“surveillance” tools mainly to ensure security and

boost productivity [4]. In a fully remote work

setting, organizations had to adopt new security

protocols [5] due to the increased volume of

online attacks,3and they ensured productivity by

2Wrist band haptic feedback system: https://patents.google.c

om/patent/WO2017172347A1/

3https://www.dbxuk.com/statistics/cyber-security-risks-wfh

tracking the efﬁcient use of resources [4].

However, well-meaning technologies could

inadvertently be turned into surveillance tools.

For example, a technology that produces an ag-

gregated productivity score4based on diverse

inputs (e.g., email, network connectivity, and ex-

changed content) can be a double-edged sword.

On the one hand, it may provide managers and

senior leadership visibility into how well an or-

ganization is doing. On the other hand, it may

well be turned into an evil tool that puts employ-

ees under constant surveillance and unnecessary

psychological pressure.5More worryingly, one

in two employees in the UK thinks that it is

likely that they are being monitored at work [18],

while more than two-thirds are concerned that

workplace surveillance could be used in a dis-

criminatory way, if left unregulated. Previous

studies also found that employees are willing to

be ‘monitored’ but only when a company’s moti-

vations for doing so are transparently communi-

cated [14]. Technologies focused on workplace

safety typically receive the highest acceptance

rates [11], while technologies for unobtrusive and

continuous stress detection receive the lowest,

with employees mainly raising concerns about

tracking privacy-sensitive information [12].

To take a more responsible approach in de-

signing new technologies, researchers have re-

cently explored which factors affect people’s

judgments of these technologies. In his book

“How humans judge machines” [10], Cesar Hi-

dalgo showed that people do not judge humans

and machines equally, and that differences were

the result of two principles. First, people judge

humans by their intentions and machines by

their outcomes (e.g., “in natural disasters like

the tsunami, ﬁre, or hurricane scenarios, there

is evidence that humans are judged more posi-

tively when they try to save everyone and fail—

a privilege that machines do not enjoy” [10]-p.

157). Second, people assign extreme intentions

to humans and narrow intentions to machines,

and, surprisingly, they may excuse human actions

more than machine actions in accidental scenarios

(e.g., “when a car accident is caused by either a

falling tree or a person jumping in front of a car,

4https://www.theguardian.com/technology/2020/nov/26/micro

soft-productivity-score-feature-criticised-workplace-surveillance

5https://twitter.com/dhh/status/1331266225675137024

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GoodIntentions,BadInventions:HowEmployeesJudgePervasiveTechnologiesintheWorkplaceMariosConstantinidesNokiaBellLabs,Cambridge(UK)DanieleQuerciaNokiaBellLabs,Cambridge(UK)AbstractPervasivetechnologiescombinedwithpowerfulAIhavebeenrecentlyintroducedtoenhanceworkproductivity.Yet,someofthesetechnologies...

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