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Risks Associated with Innovation: The Case of Artificial Intelligence
Dr DJEFEDIE Stéphane Contard
Research teacher Higher School of Economic and Commercial Sciences University of Douala
Laboratory of Architectural Finance and Management of Organizations (FARGO) Cameroon
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000496
Received: 02 November 2025; Accepted: 08 November 2025; Published: 17 November 2025
ABSTRACT
The digital transition is fostering the rapid rise of artificial intelligence, which, in turn, is further driving
digitalization, leading to a profound and lasting transformation of society. The growing adoption of artificial
intelligence in organizational activities as well as in various aspects of daily life raises the issue of potential
associated dangers. This text highlights the emergence of artificial intelligence and examines various risks
arising from progressive and disruptive innovations on the social fabric. It also encourages reflection on the
expected benefits as well as the precautions to be considered in the context of this technological change.
Resume
La transition numérique favorise l'essor rapide de l'intelligence artificielle, qui, à son tour, accélère la
digitalisation et entraîne une transformation profonde et durable de la société. L'adoption croissante de
l'intelligence artificielle dans les activités organisationnelles ainsi que dans divers aspects de la vie quotidienne
soulève la question des dangers potentiels qui y sont associés. Ce texte met en lumière l'émergence de
l'intelligence artificielle et examine les différents risques liés aux innovations progressives et disruptives pour
le tissu social. Il invite également à réfléchir aux bénéfices attendus ainsi qu'aux précautions à prendre dans le
contexte de cette mutation technologique.
Keywords-Artificial Intelligence, Risks, Innovation, Political Economy, Management.
MOTS-CLES: Intelligence artificielle, Risques, Innovation, Économie politique, Management
INTRODUCTION
About seventy years ago, the debate surrounding artificial intelligence (AI) focused primarily on a technical
question: "Can a machine think?" [TUR 09]. Today, this question seems to be decided in favor of AI, whose
use is becoming widespread, facilitated by the rise of natural language processing algorithms. Until recently,
its use was mainly attributed to large firms such as Amazon's logistics centers. Now, thousands of small
organizations are implementing solutions such as automated tracking or activity reports, for example to detect
food consumption in prohibited areas of the workplace [OCO 22]. Artificial intelligence also maintains close
connections with other branches of the economy. Its progress makes it possible to integrate intelligent
microprocessors into a wide variety of equipment: from vehicles to industrial equipment, including household
appliances. This development creates an increased need for computing power to manage and analyze the
massive volumes of data from these connected devices [HIL 22].
Technological innovation is a key driver of economic and social transformation in contemporary societies.
Among the most significant technologies of the 21st century, artificial intelligence (AI) stands out for its
ability to automate complex processes, improve business performance, and generate new services and
products. However, this advancement is accompanied by multidimensional risks and uncertainties: job losses,
algorithmic bias, lack of regulation, technological dependence, and even ethical and societal threats. The
central question of this article is therefore the following: how do the risks associated with the adoption of
artificial intelligence manifest themselves, and how can organizations anticipate and manage them in a context
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of rapid innovation? This question is all the more relevant given that AI is often adopted without a systematic
analysis of its collateral effects.
Much research examines how AI can mitigate or anticipate risks [MA21; XU 22; YW 21], notably through
improved forecasting capabilities [KY 21] or through detection mechanisms to limit fraud and financial crimes
[HG 21; QLG 21; SAM 21]. It has also considerably optimized financial risk management, whether market or
credit risks, thanks to the automation of data collection, the construction of predictive models, resilience
testing, or the evaluation of the performance of systems such as credit scoring [JON 21; KAS 21]. Artificial
intelligence tools are also proving valuable in spotting potential risk signals [ARS 21]. Artificial intelligence
can also be used to assess risks and ensure effective monitoring in complex logistics networks, as well as in
the prevention of money laundering [CSK 21; GGB 20]. Collaboration between humans and intelligent
systems—in other words, between human and automated intelligence—tends to produce better outcomes
[BHH 21; ZRM 21]. As with any major innovation, the introduction of emerging technologies generates
societal concerns. Fear of what is new or poorly understood is well documented. Some nations display a
higher cultural tolerance for uncertainty than others [HOF 80; HH 01], and the innovation process remains
inherently unpredictable [UZU 20]. Despite this, the potential for social disruption is a universal issue.
Although general opinion tends to consider that artificial intelligence helps reduce a certain number of risks, it
can also generate new forms of fragility, which is the focus of our reflection. Several categories of threats
associated with the use of AI have been highlighted [CUL 21]. Eric Schmidt, former president of Google,
highlights crucial issues such as algorithmic distortions, inequalities, usage drifts, international conflicts, as
well as current technical limitations [MUR 21]. A relevant case is the unintentional implementation of racial
or socio-economic biases in applications based on artificial intelligence. Furthermore, AI systems rely heavily
on the massive exploitation of data, which they process using advanced computing technologies. This data can
serve purposes of general interest, commercial or societal. For example, some companies are using artificial
intelligence programs to examine their databases to identify consumer habits, brand interactions, and customer
profiles. Some of this information is private, which justifies growing concerns about data privacy. To strike a
balance between privacy and business objectives, the European Union's General Data Protection Regulation
(GDPR) [GDPR 18] stipulates that personal data must be "collected for a specific, explicit, and legitimate
purpose and must not be further used in a way that is incompatible with those purposes." It also requires that
this data be "processed lawfully, fairly, and in a transparent manner for the data subject" (Article 5, [GDPR
18]). This same article also sets out strict rules regarding the limitation of the amount of data collected and the
duration of its retention.
The overall objective of this article is to analyze the risks inherent in integrating AI into organizations, while
identifying possible management and regulatory levers. The specific objectives are: - Identify the types of
risks (economic, legal, ethical, social) associated with AI. - Evaluate current governance models for
technological innovations. - Propose strategies to mitigate these risks.
Based on this, we pose the following research questions: 1. What are the main risks associated with the use of
artificial intelligence? 2. How do these risks vary across sectors? 3. What analytical frameworks can be used to
assess and prevent these risks? 4. What recommendations can be made for responsible AI governance? We
draw on a multidisciplinary theoretical framework, notably drawing on: - The technological innovation life
cycle model (Rogers, 2003) - Technological risk theory (Beck, 1992) - Approaches to algorithmic ethics and
AI governance (Floridi, 2018; Jobin et al., 2019) These models allow us to structure our analysis around an
analytical model integrating the interactions between innovation, risk perception, regulation, and use. The
study adopts a mixed-methods exploratory approach, combining data: - Quantitative, from surveys of
technology company managers. - Qualitative, collected through semi-structured interviews with AI experts,
lawyers, and CSR managers. - Secondary, analyzing institutional reports (OECD, UNESCO, UN), legal texts,
and scientific publications. The conceptual framework articulates the following dimensions: innovation – risk
perception – governance – societal impact. The hypotheses tested include: - H1: The more mature an
organization is in its use of AI, the more it develops risk management mechanisms. - H2: Highly regulated
sectors are better at anticipating ethical risks related to AI. The results show that perceived risks vary greatly
depending on the uses of AI. Companies operating in the healthcare, finance, and security sectors express
greater sensitivity to ethical and legal issues. The majority of organizations do not yet have clear internal
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policies governing the use of AI, particularly with regard to algorithm transparency and the processing of
sensitive data. The analysis also highlights a disconnect between rapid innovation and regulatory adaptation,
exposing companies to risks related to their image, compliance, and user trust. In conclusion, AI-related
innovation, while essential, requires rigorous and multifaceted oversight. Risk management must be integrated
from the design phase of AI projects, in line with a culture of transparency, ethics, and social responsibility. 1.
The creation of internal ethics committees within organizations. 2. Ongoing training of stakeholders on AI
issues. 3. The adoption of internationally harmonized regulatory frameworks. 4. The involvement of
stakeholders (employees, users, experts) in the governance of AI projects.
Legally compliant processing requires that the data subject has given consent for one or more specific
purposes, or that such processing is necessary for the performance of a contract to which the data subject is
bound, for compliance with a legal obligation, for the protection of the vital interests of that person or of a
third party, for the public interest or for the legitimate interests of the controller (see Article 6, [GDPR 18]).
Any failure to comply with these legal obligations may then lead to legal and ethical risks.
LITERATURE REVIEW: Technological progress and uncertainties
A distinction is made between incremental innovation and disruptive innovation [CHR 13]. Innovation is an
obligation for organizations wishing to preserve their market position and maintain a competitive advantage.
Some sectors of activity are more constrained than others to intensify their innovation efforts.
Most advances are incremental in nature [EBO 84], meaning they involve optimizing an existing technology,
process, or service. This allows the company to further differentiate itself from its competitors, which may
justify a price increase and, consequently, an improvement in its profitability.
Incremental innovation is part of a continuous process, the duration of which varies depending on the area of
activity.
These gradual developments facilitate user adaptation because they respect their learning pace. They constitute
a series of adjustments that users must become accustomed to over time. This gradual path to innovation is
essential to the sustainability of businesses.
Disruptive innovation results from the accumulation of multiple incremental innovations. It suddenly
interrupts the continuous process of incremental improvements. Its adoption by influential users forces the
entire sector concerned to follow suit [CRM 13], or risk losing their competitive position.
This type of innovation disrupts the initial implementation schedules planned for incremental innovations,
forcing rapid adoption. This acceleration has a direct impact on society, through the consumers of derived
products or services. It creates a need for rapid adaptation, excluding those who cannot keep up. This causes
economic risks linked to precipitous transformation, as well as social tensions between individuals capable of
adapting and those who are left behind. Although it arises from a chain of incremental innovations, disruptive
innovation abruptly breaks this cycle, like a shock or a crisis. It produces an effect that anticipates several
classic development cycles [WWY 15]. As a result, it offers such a strong strategic advantage that its initiators
can freely set their prices.
Incremental innovations require regular changes, whereas disruptive innovations require radical and often
rapid changes [CC 11]. Humans assimilate gradual learning more easily because it involves little change in
habits.
Conversely, disruptive innovation forces individuals to integrate new knowledge and change their behaviors in
a very short period of time, something that would normally have taken much longer. Every technological
advance carries potential risks for humans. The substitution of tasks with innovative technologies can lead to
the elimination of certain jobs [CRM 13]. Those who fail to keep up with the pace of change may find
themselves falling behind those who adapt more easily, thus leading to a social divide. Educational attainment
influences the ability to learn quickly, which can accentuate inequalities between social classes and generate
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societal tensions or imbalances. However, as the Villani report [VIL 18] points out, automation must not
exacerbate social and economic disparities. On the contrary, artificial intelligence should serve to mitigate
them, provided that thoughtful decisions are made regarding the types of AI to be designed and used.
The introduction of an innovation requires more or less rapid learning and involves a variable degree of new
skills to be acquired [MLL 18]. In this adaptation process, disruptive innovation presents an increased and
more immediate risk of social division, affecting some individuals more than others. This form of innovation
requires policymakers to put in place training and support measures to accompany those who risk being
excluded from the benefits generated. A major danger lies in the widening of the income gap between the
beneficiaries of the innovation and the others. This situation can cause significant social instability that may
lead to popular uprisings.
The growing integration of technological innovations into everyday life increases the risks of social instability
[BIR 11]. Indeed, the use of technologies from giants like GAFAM imposes a quasi-obligation of adoption,
and those who refuse to comply find themselves excluded. The dominance of GAFAM and BAT has
facilitated the work of public administrations in the digitalization of services, to the point that individuals who
have not adopted these tools find themselves deprived of access to public services, with no alternatives
offered.
Innovation generates additional cost risks for companies during the transition phase from an old technology to
a new one [LD 16]. This financial burden is even higher during disruptive innovations. This risk is amplified
due to the requirement for rapid adoption, requiring significant investments to adapt infrastructures. This
transition, sometimes accelerated by exceptional circumstances such as the health crisis linked to COVID-19,
forces organizations to incur expenses to make these changes, thus impacting the economic and social cost of
the process. During this phase, some companies may not survive, generating social tensions, particularly in the
labor market. The various risks associated with innovation can be mitigated through effective governance
within companies and the intervention of public authorities [AC 09].
Case Analysis: The Dangers of Artificial Intelligence
The risks associated with artificial intelligence can be grouped into traditional economic categories, such as
market dynamics, competitiveness, or employment, while others are more related to concerns about social
disruption.
Market and Political Economy Threats
The market, as is well known, tends to reinforce inequalities by favoring the growth of the most successful
companies, leading to the formation of monopolies. The main concern is that tech giants (such as GAFAM in
the United States or BAT in China) are establishing themselves as leaders, marginalizing traditional players
such as banks and small fintechs [AB 18]. This concentration can be illustrated by examples such as Paypal 's
acquisition of Swedish AI company iZettle for $2.2 billion, eliminating a potential competitor. Such
strengthening of monopoly power translates into fewer options for consumers and a likely increase in prices.
However, given the continued acceleration of technological advances, today's monopolies may no longer exist
tomorrow.
These companies, born of technological advances, have seen some of their services transform into essential
elements of the economy. For example, email and certain social networks have become indispensable for both
individuals and public administrations. The financial value of GAFAM or BAT companies often exceeds the
gross domestic product of many countries around the world. This gives them considerable implicit economic
power, allowing them to negotiate directly with the governments of major nations regarding their impact on
society. The intensive use of these digital platforms by populations also strengthens the influence of these
companies on public opinion.
The constant increase in social media platforms' need for digital data storage has become a strategic lever in
geopolitical negotiations, raising concerns about the abusive capture of users' personal information in each
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nation. This phenomenon therefore carries with it the risk of concentration of market power by these
companies, as well as possible economic and even political influence.
Beyond these classic economic concerns, sociological worries are also fueled by science fiction writers [FES
19]. One of these concerns the danger posed by authoritarian regimes in an Orwellian state, capable of
monitoring not only the behaviors, but also the thoughts and emotions of its inhabitants. Indeed, China has
already demonstrated that facial recognition technology can spot a wanted criminal in a crowd of 20,000
people [ZEN 20]. Another possible concern is that companies could exploit all the data to force you to
consume according to their choices. A third apprehension is that humans will become subservient to robots or
find themselves inferior to them. Today, it is considered that decision-making can be delegated, shared, or
integrated into a hybrid system between humans and machines [SBV 19]. Furthermore, the use of surveillance
technologies by governments can generate a fear of loss of freedom, diminishing trust in political authorities.
This situation could once again lead to risks of social instability, as observed in several countries (Russia,
France, etc.).
Management risks: who is responsible?
A more technical concern among market participants concerns the question of ultimate liability in the event of
an error. For example, if an artificial intelligence system grants credit inappropriately, causing the bank to fail,
who should be held accountable: the human or the machine? Does the manager consider it unfair to be held
liable [FRI 19]? Similarly, in the case of an autonomous vehicle involved in a fatal accident, who is at fault:
the passenger or the manufacturer?
Recently, several hundred employees overseeing post offices in the UK were wrongly accused of falsifying
accounts and embezzling funds due to faulty software. These employees took legal action against their
employer, who acknowledged that the computer program contained errors. However, the court did not accept
this justification, and management was found liable for these unfounded accusations [OCO 22].
With the development of artificial intelligence, an increasing number of complex activities could be
automated. This includes financial management, reviewing assessments, and all operations related to massive
data processing. Only final decision-making would remain under human responsibility. This creates a major
risk that humans will become dependent on the automation of these functions [RIC 19]. The danger of
erroneous strategic decisions based on inaccurate data can be considerable.
The rise in dependence on data and digital technologies is such that human reasoning and discernment now
rely on the information provided by these tools [TCY 19]. Thus, individuals put aside their critical thinking
and acquired knowledge to rely entirely on technological devices. On the one hand, this creates a significant
risk in terms of management, and on the other, the threat that artificial intelligence will become even more
efficient. The ability of AI to converge with collected data could allow machines to achieve a level of
decision-making comparable to that of humans.
In organizations, artificial intelligence technologies such as genetic algorithms, neural networks, and fuzzy
logic are already used for day-to-day management, but their use is expected to extend further to strategy
definition [GAV 18]. This raises new concerns: poor strategy development can lead to the failure of the entire
structure, while failure to adopt these advanced methods risks placing the company in a weak position against
the competition.
National legislation defines and assigns the responsibility of business leaders for validated management
decisions and the results obtained, whether financial, economic, environmental, etc. With the growing rise of
digital technology linked to the explosion of data management, and consequently the use of AI to process this
information, managerial decision-making is becoming more complex [IJS 13]. If the manager relies on
innovation to control the volume of data, his assessment of the information to be extracted and the choices to
be made are now more exposed to risks.
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Operational threats: security and malfeasance
Current literature shows that artificial intelligence can be used to identify and prevent fraud related to bank
cards and other financial transactions [SOV 18; BNW 20].
Operational risks arise from biased or unrepresentative data, the choice of algorithmic models, and human
decisions influenced by AI interpretations [AH 21]. These vulnerabilities can affect both private companies
and public finances [PCE 21]. Furthermore, the increased automation of digital processes makes IT systems
more exposed to cyberattacks. It also increases the risk of fraud that is more difficult to detect than that
resulting from traditional procedures. For example, NFTs and cryptocurrencies suffer from a lack of
regulation, which leads to low trust and an increased risk of embezzlement [MEN 19]. This lack of trust is also
evident among patients, who often prefer a diagnosis made by a human professional rather than by an
automated system [LDA 22].
On a more technical level, major security issues are emerging, including the significant risks of hacking
related to Big Data, which is concentrated in the hands of a few key players. With the proliferation of
transactions on interconnected networks, the threat of cyberattacks is intensifying [VAR 19]. A related risk is
the bankruptcy of a major player, which could render many dependent applications unusable.
Data security has become a critical concern, whether it concerns stored customer information or the IT
infrastructure of businesses and governments. Personal data breaches regularly make headlines. Furthermore,
cyberattacks against government systems have become a veritable "weapon of war" for some countries, as
evidenced by the conflict between Russia and Ukraine in 2022. These attacks also frequently target businesses,
sometimes with ransom demands to lift the blockage caused by the intrusion.
Another issue concerns “ deepfakes ,” or synthetic content, which is almost undetectable to the naked eye.
Beyond reputational risks and cyber threats, they open up a new avenue for fraud. This issue is particularly
critical in the insurance sector. “ Deepfakes ” can be used to formulate false claims, produce fake appraisal
reports, or even simulate the existence and condition of fictitious assets [VEK 21]. Furthermore, consumers
express concerns about the protection of their privacy, fearing that their personal data will be accessed or
disclosed without consent. This could explain the reluctance to use online chats in banking services [AI 21].
Moreover, customers sometimes perceive these exchanges as generating information asymmetry, since the
bank can record the conversation while the customer does not receive a copy.
Table 2.1. Operational risks - Security and fraud
Type of Risk
Risk Level
Non-representative data
AVERAGE
Biases inherent in representative data
Weak
Choice of algorithms
AVERAGE
Human decisions
AVERAGE
Cyberattacks
Pupil
NFT/Cryptocurrency Fraud
AVERAGE
Large amount of data stored with few actors
Pupil
Source: Author
Low Risk => No more risk than in the "old world ";
Medium Risk => Need to address latent implicit risks. Otherwise high risk .
High Risk => Mandatory risk management by stakeholders. Failure to do so may result in negative
consequences for businesses and society.
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Dangers associated with the collection and processing of data and individual freedoms
The assessment of individual rights remains complex. The Ontario Human Rights Committee [PRO 09]
illustrates several challenges in this area. For example, in a high-crime area, how can information be collected
while respecting individual privacy rights to determine whether community policing is necessary? Another
example is how can data on sexual harassment be gathered when human rights rules and policies are unclear?
In these situations, the entity collecting the data should not have a direct interest that could lead to
discrimination. A third example concerns the treatment of LGBT people by hotel reception staff. In the event
of a complaint, can the establishment require the use of video surveillance for all guests? Can it also collect
information on guests' sexual orientation? Would it be necessary to ask each guest about their sexual
preferences? The data collected must correspond to the purpose of the collection, which in this case could be
to improve the treatment of LGBT people. Regarding transgender people, what happens if the individual
identifies as a woman while someone else identifies them as a man?
An example of data analysis might involve identifying the intersections of different characteristics and
comparing them with a relevant reference group. For example, if a person is male, young, of Asian descent,
and illiterate, should the control group used for comparison be similar in all respects except illiteracy? Even
publicly available data can present categorization problems. For example, Statistics Canada uses 12 racial
classifications. What happens to a person from two different racial categories? Qualitative data has its own
limitations, particularly due to the subjectivity and interpretations of the respondent and the researcher.
Similarly, quantitative data may not be appropriate in certain contexts, such as for small samples or non-
normal distributions. Therefore, any processing using artificial intelligence tools can also be misleading.
Threat in the employment sector
A labor market vision focuses on the future evolution of work. It analyzes the anticipated proportion of the
workforce, as well as the beneficiaries and disadvantaged within it. The substitution of certain functions, or
even entire job categories, with technology leads to a possible increase in unemployment. This can also create
downward pressure on wages, thus generating a risk of social and political instability.
A recent book found that the most modest jobs would be replaced by automation [BRY 14]. Other research
showed that high-skilled positions (in management, professional, and technical occupations) as well as low-
skilled service jobs have benefited, while the middle class has suffered losses [DAV 15]. Specialists are
therefore the big beneficiaries. For example, Abu Dhabi's sovereign wealth fund, one of the world's largest, is
actively recruiting experts, influencers, machine learning specialists, strategists, portfolio managers, risk
analysts, as well as professionals from several other specialized fields [ENG 22]. David [DAV 15] pointed out
that, even if automation replaces some employees, it also increases their marginal productivity, which tends to
boost their income. For example, with the advent of ATMs, teller employees have shifted to more personalized
banking services. However, these benefits were expected to be limited as AI progresses toward more creative
forms of learning. Recent data from a major consulting firm reinforces these concerns. According to a
McKinsey report [MAN 17], nearly half of the tasks performed by employees could be automated, implying
that 15% of the global workforce is at risk of losing their jobs, while new positions will require different skills.
This automation will put downward pressure on wages in developed countries. Beyond the robotization of
repetitive tasks, it is now anticipated that the cognitive functions of managers will also be delegated to
machines, leaving humans only those requiring emotional intelligence [HRM 19]. Many banks are reducing
their staff in industrialized countries, but it remains unclear whether fintech startups have compensated for this
job loss. Physically demanding and often low-skilled occupations are the most likely to be replaced by
technology, increasing the risk of a social divide between low- and high-skilled workers. However, rather than
talking about job losses, some emphasize the transformation of jobs linked to AI [VIL 18]. Indeed, a job is
made up of several tasks: some can be automated, while others are less so. In other words, specific tasks
within a job are at risk of being automated. The people most affected are often low-skilled workers, manual
workers, as well as some white-collar employees. This also represents an opportunity to develop more human
skills, such as creativity, manual dexterity, abstract thinking, and problem-solving skills.
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However, all these measures are causing concern and a form of "technophobia" among employees.
They fear that the integration of AI into their company will quickly make them redundant [SIN 20].
As a result, they are beginning to consider different career paths [PRE 19]. The risk associated with
job automation is also negatively correlated with labor productivity, and more open economies are
more exposed to this threat [FNV 21].
Risks linked to other social divisions
Artificial intelligence can both mitigate and increase certain risks, and the majority of banks and FinTechs
agree that the impact on organizational risk will be limited, while an increase in societal risks is to be expected
[ZAB 21]. Tech giants such as Google, Amazon, Facebook, and ByteDance are often criticized for their lack
of ethical consideration in the design of their AI systems, including their use for surveillance purposes and the
spread of algorithmic bias, where computer systems consciously or unconsciously reproduce biases derived
from biased or corrupted data [MUR 21]. For example, AI can reinforce existing gender stereotypes [ADA
19]. One study showed that Samsung's male and female voice assistant Bixby react differently depending on
the gender in the responses provided. Another demonstrated that an algorithm developed by Amazon to
analyze job applications tended to penalize those containing the term "woman." Indeed, the algorithm, trained
on historical data reflecting predominantly male recruitment, reproduced this bias in the selection of future
candidates.
Scams targeting older adults have increased, often committed by their caregivers or family members. To
counter this phenomenon, banking institutions are now using artificial intelligence to identify these frauds
[CRO 19]. However, integrating new technologies can be more complicated for seniors, increasing their
vulnerability to social isolation.
Threat linked to global rivalry: geopolitical issues
The growing dependence of national economies on technology is generating new geopolitical rivalries over
control of these innovations. After a period marked by the relocation of production chains, recent years have
seen the emergence of government restrictions on the implementation of technologies, perceived as sources of
economic advantage and levers of political power. Are we witnessing a return to the era of comparative
specialization of states [POR 11]?
Economic rivalry and geopolitical tensions are forcing states to foster technological innovation at home and
reduce their external dependence. Several examples from European countries during the COVID-19 pandemic
in 2020-2021 concern medicines and electronic components. These sectors have become strategic issues for
Western countries vis-à-vis Asia. For example, the United States has encouraged Taiwanese semiconductor
companies, such as TSMC, to establish their new factories in the United States. Furthermore, INTEL is
considering building a new factory in Europe rather than Asia. In the competition for the development of
artificial intelligence, it seems that the country with the largest volume of data will surpass those with the most
financial resources. China, with its vast connected population, would thus benefit from an advantage [ZEN 20].
Furthermore, if its population is unaware of its civil rights or willing to sacrifice them to achieve a position of
global leadership, China could then design surveillance systems that Western countries would be reluctant to
deploy [ZEN 20]. Nevertheless, during the COVID crisis, several other nations have managed to establish
tracking systems previously unacceptable to their societies.
Table 2.2. Implicit risk level by type of innovation
Type of innovation
Type of Risk
Of market
and
political
economy
Managerial
Operational:
Security and
Fraud
Within the
market of
work
Social
divisions
International
competition:
geopolitics
Incremental
Weak
AVERAGE
AVERAGE
Weak
Weak
Weak
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Innovation
Innovation
Disruptive
Pupil
Pupil
Pupil
Pupil
Pupil
Pupil
Source: Author
Low Risk => No more risk than in the “old world”;
Medium Risk => Need to address latent implicit risks. Otherwise high risk .
High Risk => Mandatory risk management by stakeholders. Failure to do so may result in negative
consequences for businesses and society.
Table 1 summarizes the levels of risk that incremental and disruptive innovation could present.
DISCUSSION
Tolerance for ambiguity is a characteristic of national cultures as described by [HOF 80; HH 01]. Thus, the
uncertainty inherent in any transformation is more tolerated in societies where this sensitivity is less marked
(such as in Anglo-Saxon countries or in Southeast Asia), unlike in other regions (such as France, Germany,
Italy or Spain). The adoption of new developments resulting from innovation is more easily carried out in
cultures where intolerance to uncertainty is low. Conversely, in countries where this aversion is stronger,
populations tend to show more reluctance towards technological advances, and their integration is more
gradual.
It is noted that the majority of companies that have introduced disruptive innovations into society come from
Anglo-Saxon or Asian countries. In contrast, companies from other regions focus more on incremental
improvements. These disparities can be explained by the economic structures specific to each country [WR
09]. For example, the adoption of financial technologies (Fintech) is much more advanced in Anglo-Saxon and
Asian nations than in most European countries. The automation of physical tasks in logistics centers or large
retail stores is already normalized in some countries. Conversely, in Europe, this mechanization raises
concerns about job losses. This illustrates the differences in the reception of innovation according to national
contexts. Innovation is continuous, and its pace is accelerating. Its integration will be more or less easy
depending on the country, and the acquisition of new skills will be essential for future generations to be able to
adapt to it.
Knowledge acquisition can take several forms: early introduction to technology at school; learning by doing,
which allows students to progress more quickly through concrete experiments conducted in the classroom or at
high school; and the development of creative thinking, encouraging students to think outside of traditional
frameworks to explore original solutions to a problem. This way of thinking is the foundation of "design
thinking " and represents the first step in the innovative process. In short, it is becoming essential for societies
to transform educational approaches aimed at new generations.
At the same time, public authorities must anticipate future developments and implement training programs in
advance to support the retraining of current workers [CU 18]. For example, by training operators specialized in
robotic technologies. Some companies are taking the initiative to do this themselves, due to a lack of available
qualified personnel. Indeed, carrying out mass layoffs would represent a significant cost and risk aggravating
social tensions linked to unemployment.
For example, Renault has decided to convert its Flins site into a factory dedicated to the circular economy in
the automotive sector, while ensuring the professional retraining of all its employees. Upskilling workers to
adapt to innovations, whether progressive or radical, is becoming a strategic issue for all countries.
Furthermore, it is essential to consider how to structure and regulate interactions between humans and
technologies. Should regulations be imposed on technology companies when their advances cause job losses?
How far should automation be allowed? What legal responsibility should users be assigned in a world where
the alternative to technology could disappear? These questions are numerous, as the pace of innovation is
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accelerating sharply, particularly thanks to the rise in computing capacity and the energy availability that
powers them. We are on the cusp of a major technological transformation that requires rigorous management
of social implications.
CONCLUSION
What actions should be considered? Many entities may find themselves unable to decide whether data
collection is ethical or not, which can lead to the risk of prosecution or legal sanctions. In this context, they
may choose to rely on the expertise of specialized organizations: “Sensitive to the issues related to data ethics
and their use by artificial intelligence, the Delta organization has established a collaboration with the
International Observatory on the Social Impacts of AI (OBVIA), with the aim of building a reference
framework intended to provide more precise guidance for its future projects.” [JS 22].
To some extent, artificial intelligence will create new forms of employment, particularly because human
intervention is required for data annotation. Platforms such as Mechanical Turk or Hive have developed as
digital spaces offering a variety of fragmented tasks, sometimes related to sectors such as catering, but not
exclusively. However, once algorithms master labeling autonomously, human intervention could be limited to
a supervisory role to detect possible errors. One possible response to this development could be the
introduction of reduced working hours (such as a four-day week), combined with the implementation of a
guaranteed universal income [ASH 19; AD 21].
The ethical issues surrounding artificial intelligence are reminiscent of those in financial services before the
2008 crisis. In both cases, the complexity of the mechanisms, the lack of transparency of certain algorithms,
and insufficient regulations can cause unforeseen and negative impacts. To ensure responsible AI, the process
should include design, coding, evaluation, and, above all, auditing [BLA 22]. Philanthropic funding is being
mobilized to support research into ethics and explore the positive uses of AI [MUR 21]. However, as the case
of Google illustrates, these resources are sometimes allocated only to traditional research profiles. For
example, Google fired a Black researcher after she highlighted biases in their AI systems [SHW 21]. Faced
with the rapid advances in AI, education and training systems must rethink their approaches to better prepare
individuals for new professions. The major problem is the current inability of these structures to adapt their
programs to the pace of technological change, creating a growing gap between the skills needed and those
available. Thus, beyond the impacts, it is becoming crucial to integrate educational and training dimensions
into the development of AI [VIL 18].
According to Gilda Darlas , the major challenge lies in the fact that artificial intelligence tends to amplify the
negative aspects of human nature, and that the solution involves a transformation of education from childhood
onwards, in order to strengthen shared experiences and empathy between individuals [AD 21]. While playing
a protective role for the most vulnerable, educational institutions must also train the most enterprising students
to quickly identify and seize opportunities in a constantly changing environment. This involves teaching skills
such as resilience, adaptability, and preparation for the professional world. These skills will complement the
often-highlighted skills such as creativity, teamwork, analytical thinking, and communication. Of course, these
human qualities must be accompanied by a solid foundation in classic financial tools, such as economic data
analysis or company valuation, in order to help future professionals understand the recommendations of
automated systems and detect their potential errors.
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