AI-generated image
Source: Adobe Stock

Issues and Discussion Paper
Best practices for the
responsible use of AI
in the financial sector

Legal Deposit – Bibliothèque et Archives nationales du Québec, 2024
ISBN 978-2-550-96846-7 (PDF)Table of contents
1.

Preamble...........................................................................................................42.

Use of AI in the financial sector.......................................................................53.

Best practices for the responsible use of AI....................................................9
3.1 Practices related to consumer protection.............................................................. 9
3.2 Practices related to transparency for consumers and the public...................... 12
3.3 Practices related to the appropriateness of AISs............................................... 13
3.4 Practices related to responsibility........................................................................ 14
3.5 Practices related to AI design and use................................................................ 14
3.6 Practices related to managing AI-associated risks............................................ 16

4.

Discussion........................................................................................................19

Additional discussion questions................................................................................................20
AMF contact persons................................................................................................................20
Bibliography............................................................................................................................... 21

1. Preamble
The Autorité des marchés financiers (AMF) is a key player within Québec’s financial ecosystem. In its role as regulator,
the AMF acts to maintain a financial sector that is dynamic, operates with integrity and warrants public confidence. The
AMF sets the rules and standards governing market activities and supports the adoption of best practices. In this mode,
it is informed by the realities and lived experiences of its clienteles, key trends, innovations, and insights into domestic
and international markets.
Since 2016, the AMF has undertaken various mission-aligned initiatives relating to the digital transformation of the financial
sector to, in particular, anticipate regulatory and consumer protection issues. In November 2021, it made a clear statementof its interest in the responsible use of artificial intelligence (AI) by signing the Montréal Declaration for responsible AI
development and by publishing the paper Artificial intelligence in finance – Recommendations for its responsible use(the 2021 Report), a report prepared at the AMF’s request by Algora Lab, an interdisciplinary lab of the University ofMontréal and the Institut québécois d’intelligence artificielle (Mila). The 2021 Report sets out 10 recommendations topromote the responsible development of AI in finance: three directed toward the AMF and seven toward the industry.
In 2023, the AMF contributed to the réflexion collective sur l’encadrement de l’intelligence artificielle au Québec (collective
reflection on the regulation of artificial intelligence in Québec), an initiative led by the Conseil de l’innovation du Québec(CIQ) to identify issues and opportunities presented by AI with a view to ensuring its ethical and responsible development
and use while remaining true to Québec values. This activity culminated in a report entitled Prêt pour l’IA – Répondre audéfi du développement et du déploiement responsables de l’IA au Québec (the CIQ Report) (in French only).
In line with the recommendations put forward in the 2021 Report and in the CIQ Report, the AMF is continuing its workby publishing this issues and discussion paper inventorying the best practices that collectively define what is meant today
by “responsible use of AI in the financial sector.” The publication of this paper and the subsequent period of discussion
with stakeholders is in response to the recommendations directed toward the AMF in the 2021 Report.
The purpose of this paper is not to implement a new framework or modify an existing one. None of thepractices presented here constitute a new obligation for financial institutions and other players in the financial
sector (financial players1). Nevertheless, the AMF wishes to engage in dialogue with the industry about the use of
these best practices, which can guide the financial sector toward the responsible use of AI. The financial players are also
reminded that they must continue to comply with all currently applicable laws and requirements.
The AMF will continue to monitor AI developments and the integration of AI in the financial sector. It may consequently
review the herein-described practices in the future and adjust them as new trends or risks emerge.

1	In this paper, “financial players” include financial institutions, investment fund managers, dealers and advisers, firms, independent partnerships andindependent representatives, credit assessment agents, and other companies and professionals subject to the laws and regulations administered by theAMF, except for reporting issuers under the Securities Act, market infrastructures and self-regulatory organizations.

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2. Use of AI in the financial sector
Innovations that are helping to digitally transform the financial sector include ever-growing, “on-demand” computing
power, which is facilitating the processing of large volumes of data generated through, among other things, digitaltechnologies and connected objects. Unsurprisingly, there is an increasing interest in advances in AI, which are now
being leveraged in all sectors of the economy to extract more value from data.
In the financial sector, the use of AI offers a multitude of opportunities with potential benefits for both consumers andbusinesses. For example, the integration of AI could lead to the development of new financial products and services ora reduction in costs for consumers. AI algorithms can also be used by businesses to improve client segmentation andmore accurately tailor services to the specific needs and circumstances of clients, thereby enhancing the client experience.
However, the integration of artificial intelligence systems (AISs) into financial players’ activities should be guided by bestpractices so as to properly manage the associated risks.

WHAT IS ARTIFICIAL INTELLIGENCE?
Artificial intelligence refers to a field of science that studies and attempts to reproduce the various mechanisms thatmake up human intelligence. Such efforts are generally derived from a concerted combination of computer scienceand statistical methods that make use of big data and exponentially growing computational power.
Artificial intelligence is generally associated with machine learning and deep learning, which focus on the designof algorithms and methods that efficiently compresses knowledge in a computer system so that it can perform complex
tasks through a process akin to “learning.” It is also associated with natural language processing, which enables
computers to process, generate and manipulate human language. Large language models, for example, areAI models that are typically trained on large amounts of text and have a very large number of parameters. Often
used for the implementation of chatbots, they can capture many aspects of human language syntax and semantics.
Generative AI refers to the use of AI for the purpose of creating new content such as text, images, music, sounds
and videos.
AI model training is the process by which the model “discovers” relationships within the data (training data). The
model can then use those relationships, and sometimes continually refine them, to make predictions on new data
the model has not encountered before.
Finally, an artificial intelligence system (AIS) is “a technological system that, autonomously or partly autonomously,
processes data related to human activity through the use of a genetic algorithm, a neural network, machinelearning or another technique in order to generate content or make decisions, recommendations or predictions”(Source: Bill 188 Artificial Intelligence and Data Act).

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AISs differ from the static systems or models traditionally used in the financial sector in the following respects:• Complexity: AISs use advanced algorithms that can contain millions of parameters. AISs are also capable ofprocessing very large volumes of data, including unstructured data.
• Probabilistic models: AISs process the information fed to themusing probability-based models to, for example, anticipate a future
outcome using standard sequences or historical data, assign anelement to a pre-defined category among all possible categories,
or update an assumption based on new facts. As using thesemodels involves a margin of error, it is not possible to anticipate
future outcomes with certainty.
• Opacity: AISs are sometimes called “black boxes” because itis generally difficult to understand how they reach a particularoutcome or decision. The model structure (e.g., deep learningalgorithms) makes it impossible to clearly establish the logicalrelationship between inputs and outputs. This lack of transparency
can, among other things, impede the processes for identifying andcorrecting errors or discriminatory biases.
• Dynamism: Some AISs (continuous learning models) are designed
to continually adjust as new data inputs are received. Theirperformance may therefore change over time: this is referred toas model drift.

Unstructured data is data that is notarranged according to a pre-definedmodel. It can take the form of images,
videos, audio recordings and othertypes of media. Unstructured data isdistinguished from structured data, which
is organized into pre-defined items, eachof which relates to a specific concept ordata item.
(Source: Statistics Canada)
Al model drift (or concept drift) is where
the statistical properties of the data
the model is trying to predict change
unexpectedly over time. This causes
problems because the model’s predictions
become less and less accurate as timegoes by.
(Source: Wikipedia)

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Complex models and algorithms are already widely used in the financial sector. However, the following AIS characteristics
have the potential to cause material harm:• For investors and financial consumers (collectively, consumers): Consumers may be denied access to a financial
product or service as a result of discriminatory biases introduced into an AIS or a flaw in the AIS. Moreover, conflictsof interest may influence an AIS’s recommendations to an investor.
AIS-powered digital engagement practices (e.g., gamification and nudging) can be used to consumers’ detrimentby exploiting their behavioural biases (e.g., by “nudging” them to take actions that are not in their best interests). AI
can also facilitate consumer surveillance or other privacy incursions owing, for example, to the use of informationcollected from a smart phone or other connected object.
Gamif ica tion involves applying
Another risk for consumers is the absence of clear, accessible
mechanisms used in gaming to marketing
information explaining how AI is integrated into a financial productpractices. Nudging involves using various
or service, the reasons for decisions affecting consumers or what
techniques (such as notifications) toremedies are available to them.
draw consumers’ attention to somethingor encourage them to take a particular
Lastly, the availability of generative AI tools on the web significantly
action. Gamification and nudging are two
increases the risk of fraud for consumers. Deep fakes lend added
digital engagement practices, which arecredibility to misleading posts on social media, and large language
various engagement techniques used withmodels (LLMs) may be used to draft phishing e-mails in multiple
clients in a digital environment.
languages.
• For financial players: Deploying a biased or flawed AIS could
pose a significant reputational risk. What is more, errors or drift
in an AIS used for resource allocation or investing could affectfinancial players’ solvency. This risk may also materialize as aresult of an employee’s uninformed or ill-intentioned use of an AIS(e.g., use of a generative AI tool for which the training data include
one or more copyrighted works without the copyright holder’spermission).

(Source: Insights into the risks and benefitsof digital financial services for consumers)
A deepfake uses AI to create highly
realistic images or sound clips of fakeevents.
(Source: Autorité des marchés financiers)

Lastly, AISs pose a cybersecurity risk for financial players because, for example, they may be targets of attacks
designed to deceive or exploit them. Moreover, AIS training requires large quantities of data that, if it contains
personal information and is not properly protected, could be vulnerable to privacy breaches.

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• For market integrity: The opacity and dynamism of AISs used to automate trading processes and investment decisions
creates risks for the financial markets. For example, some AISs used to interact with investors or consumers may present
conflicts of interest that, once uncovered, could adversely affect public confidence in the financial markets. The useof AISs could also facilitate media dissemination of false or misleading information (e.g., deep fakes).
• For financial stability: Errors or drift in such AISs may also result in flash crashes. These sudden market movements,
made more frequent by the increasing speed with which orders are placed and executed, could also result in market
liquidity issues. Large-scale movements could also be generated by the widespread adoption of similar AISs or AISs
trained and fed with similar data, resulting in unexpected concentrations or interconnections that would be difficult
to detect owing to model opacity.
In general, the use of AI increases the scope and speed of impact of existing risks. AIS harm resulting from poor data
quality or from outcomes generated by a biased, flawed or poorly implemented AIS may be more widespread andbe perpetuated more quickly than when traditional systems are used. For example, the use of AISs may, in some cases,
lead to increased systemic discrimination or the exclusion of certain groups of consumers from the financial system andproduce inequality.
Lastly, governments and the private sector are investing heavily in AI research and marketing, causing scientific andtechnological advances to accelerate at breakneck speed. The fast pace of development is widening the mismatch
between the opportunities afforded by AI and our understanding of the issues it may present in the medium to long term.
In view of the rapid, uncertain pace of AI development, but also the potential benefits of AI for the financial sector, theAMF wishes to deepen its understanding of the risks associated with this technology and the practices to be implemented
so that those risks can be managed without inhibiting innovation. At stake is continued confidence in our financial system.

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3. Best practices for the responsible use of AI
The AMF is proposing 30 best practices for the responsible use of AI that draw from ethical principles and legislative andregulatory projects put forward in a number of jurisdictions. These practices apply to all AISs implemented by a financial
player, including those that continue to be internally focused. These practices should be adapted to the characteristicsof the AIS used, the financial player’s business environment and the risk assessment for the use of an AIS, especially as
regards its impact on consumers.

3.1

PRACTICES RELATED TO CONSUMER PROTECTION

The practices presented in this section could help protect consumers from the unfair, abusive or misleading use of AI byfinancial players. These practices promote the use of AI in a manner consistent with consumers’ reasonable expectations
with respect to both privacy and autonomy.
1. Using AI in consumers’ best interests
The use of AI should not cause any harm to consumers, individually or collectively, by, for example, creating barriers tofinancial inclusion, systemizing unjustified discriminatory biases or increasing economic and social inequalities.
AISs can model and influence human behaviour through mechanisms that can be difficult to detect because they can
exploit unconscious processes. AI should not be used to mislead or manipulate the public through, for example, an AIS
that unduly influences and inform consumers’ product or service decisions, the posting of misleading content on social
media (e.g., deep fakes), or the communication of exaggerated information on the value added by a financial player’s
use of AI in its activities.
Lastly, where an AIS presents itself as human, consumers should be clearly told that it is an automated agent.

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2. Respecting consumers’ privacy
Any intrusion by an AIS into consumers’ private lives should be consistent with personal information protection and privacy
laws and regulations2 and warranted by the potential benefits accruing to consumers.
Requests for consumers’ consent to the collection and use of their data should be in plain language and based on a clear
description of the AIS’s purposes and the need for the AIS to collect data to ensure its proper functioning. The consent
should cover any collection of consumers’ personal information, including data relating to them (e.g., data contained insocial media posts). Outcomes or decisions provided by an AIS regarding a consumer may constitute personal informationand should therefore be treated as such.
An AIS intended to monitor a consumer’s behaviour should only be deployed with the consumer’s consent. Such monitoringshould be relevant and necessary for the use of the product or service offered. However, no AIS should make a consumer
feel under constant surveillance.
Lastly, some AISs enable inferences about a person’s characteristics, habits or life events. For example, a person’s chancesof having children or belonging to a particular ethnic group can be evaluated. In some contexts, the inference may beperceived by the consumer as an intrusion into their private life. AISs should produce inferences only about characteristics,
events or habits that may reasonably be expected by the consumer and that are related to the nature of the financial
product or service offered.
3. Increasing consumer autonomy
The deployment of an AIS should not have the effect of limiting freedom of choice or influencing a consumer’s behaviouror lifestyle. On the contrary, AISs should be designed and made available to consumers to support them in making
decisions better aligned with their circumstances and financial goals.
Financial players making automated decisions through an AIS should inform consumers of this fact and obtain theirprior consent. Clear, reasonably priced options should be offered to consumers who do not wish to interact with or bemonitored by an AIS or have an automated decision made about them.

2	Particularly the rules governing the financial player’s privacy policy, the consent of the person concerned, the security and destruction of personal data,
the rights of access and correction, and the disclosure of confidentiality incidents to the bodies responsible for administering such legislation. In Québec,the Act respecting the protection of personal information in the private sector and the Act respecting Access to documents held by public bodies and theProtection of personal information are administered by the Commission d’accès à l’information.

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4. Treating consumers fairly
Consumer segmentation based on relevant and socially accepted criteria enables financial players to offer financial
products and services appropriate to the circumstances of each group of consumers, at an appropriate price and with
an appropriate risk profile. Segmentation is used, for example, in evaluating credit for mortgage loans or assessing therisk covered by an insurance policy. However, the only way to achieve these benefits is to use data and models that are
free of quality issues and discriminatory biases.
AIS performance depends intrinsically on the quality of the data used. An AIS should be trained with up-to-date data thatis representative of the system’s target population and does not reflect, for example, biases from discriminatory practices.
To obtain such data, vulnerable groups of consumers or groups of consumers that may be unduly disadvantaged (e.g.,
new immigrants, people who are illiterate, or people who are unfamiliar with digital technologies) should be identified,and an AIS’s impact on these groups should be assessed at the design phase. Measures should also be implemented
to validate the equity of outcomes provided by the AIS, and where two consumers or groups of consumers are treateddifferently, the difference in treatment should be justifiable on the basis of appropriate criteria. Financial players might
consider involving an ethicist or sociologist in such analyses in order to clearly identify sources of discrimination andappropriate mitigation measures.
5. Managing conflicts of interest in consumers’ best interests
Conflicts of interest (e.g., the possibility that an AIS will favour a financial player’s interests over the interests of consumers)that may appear in AIS outcomes or decisions should be eliminated or mitigated throughout the life cycle of the AIS.
Particular attention should be paid to identifying conflicts of interest in complex or opaque AISs or in AISs with hard-toexplain outcomes.
6. Consulting consumers required to use AIS
Consumers should be regarded as stakeholders in an AIS’s design. Before deploying an AIS that could have a high impact
on consumers, consumers’ input should be sought and considered by the financial players in identifying ethical risks,particularly where an AIS could potentially lead to feelings of privacy incursion or loss of autonomy. Consultations would
include, but not be limited to, the participation of consumers whose data is used by the AIS or who could be impacted by
AIS outcomes or decision making. Minority or vulnerable groups of consumers should be involved in the consultations.
Participation mechanisms enabling consumers’ comments to be gathered regularly throughout the life cycle of an AIS
should also be implemented for AISs that could have a high impact on consumers generally or on a particular group of
consumers.

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3.2	PRACTICES RELATED TO TRANSPARENCY FOR CONSUMERS AND THE PUBLIC
This section presents practices intended to establish an adequate level of transparency regarding financial players’ useof AI. Such a level of transparency may be achieved without the financial player having to reveal trade secrets.
7.

Disclosing information about the AI design and use framework

Consumers must be able to form an opinion about respect for ethical principles and the social acceptability of the use
made of AI in the financial sector. A lack of transparency on the part of financial players regarding their approach toand ethical and governance frameworks for the use of AI could limit debate.
8. Disclosing information on the use of AI in products and services
Consumers should have access to the information they need to assess
the benefits and risks associated with the use of AI in the context ofprocuring a financial product or service, especially when making
a product or service decision. Such information should cover, inparticular, the objectives, limitations and functioning of the AIS and themeasures in place to mitigate the associated risks. Consumers should
also have access to all relevant information on the rights and remedies
available to them should they incur harm from interacting with the AIS.

A digital watermark is a permanentmark embedded in a document orother medium to, in particular, protect
intellectual property rights and fightpiracy.
(Source: Grand dictionnaireterminologique)

Plain, non-technical and concise language should be used whencommunicating with consumers. The disclosure interface should bedesigned to encourage consumers to read the information closely rather than respond quickly. Consumers who find thedisclosed information insufficient should be able to request and receive assistance from a technical expert.
Consumers should also be informed, by appropriate means (e.g., digital watermarking), that content published by afinancial player has been wholly or partly created by a generative AI tool.
9. Explaining outcomes relating to a consumer
Whenever an AIS could have a high impact on a consumer, the consumer should have the opportunity to request a clear,reliable explanation of the process and main factors that led to the outcomes or decision provided by the AIS. The AIS
should be designed so that such outcomes are traceable and explainable.
When explanations are clear, there is no need to share intellectual property or source code. Non-technical language
should be used, at a level of detail proportional to the seriousness of the consequences for the consumer of an erroneousdecision or outcome.
Also, the consumer should be able to obtain a list of any personal information about them that is used by the AIS and tocorrect or update such information if it is inaccurate.

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10.	Providing consumers with communication channels and assistance and compensating mechanisms
When consumers interact with an AIS, they should be able to get
help, at any stage of the process, through an interaction with acompetent person. They should also have the option of requesting tohave the outcomes or decision of the AIS reviewed by a person. For
this, consumers should have access to someone able to explain thefunctioning of the AIS to them and give them the opportunity to present
their arguments challenging the outcome or decision.

Consumers are generally considered
to be in a vulnerable situation when
they cannot properly assess theconsequences of some decisions orsituations. The vulnerability may be
temporary or permanent.

Information on these and other assistance and compensating
mechanisms should be easily accessible and comprehensible toconsumers. When implementing such mechanisms, particular attention
should be paid to vulnerable consumers.

(Source: A practical guide for thefinancial services industry – Protecting
vulnerable clients, Autorité des marchés
financiers)

3.3

PRACTICES RELATED TO THE APPROPRIATENESS OF AISSThe characteristics specific to AISs entail significant risks for all financial sector stakeholders. The practices proposed inthis section address the appropriateness of using AI in a given situation, particularly where there are options that perform
equally well or yield equivalent outcomes but carry fewer risks or are more easily explainable.
11. Justifying each case of AI use
The objectives sought in connection with the design or use of an AIS should be appropriate to the financial player’s
mission. Also, consumers should be able to derive a benefit from the AIS, which should be simple and easy to summarize(e.g., consumers will be able to benefit from faster or better-tailored service or save money).
It should also be possible to demonstrate that a proper balance has been achieved between the financial player’s interests
and the interests of other parties impacted by the AIS (e.g., certain vulnerable groups of consumers).
12. Prioritizing the simplest, most easily explainable treatment
The outcomes obtained using an AIS should generally be better than those obtained without using it or by using simpler,
more easily explainable technologies. This practice should be more strictly applied for AISs with a potential high impact
on consumers.
The benefits of using AI should also outweigh the foreseeable risks and harm, generally and for each group of individuals
or consumers that could be affected.

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3.4

PRACTICES RELATED TO RESPONSIBILITY

The task-performance and decision-making autonomy that an AIS is capable of exhibiting has profound implications foran organization’s AI governance. The practices presented in this section help ensure that responsibility for the outcomes
and decisions of an AIS is clearly assigned to an individual.
13. Being accountable for the actions and decisions of an AIS
The use of AI must not contribute to a lack of accountability among financial players. On the contrary, responsibility for
all outcomes of and harms caused by an AIS deployed by a financial player (including AISs acquired from third parties)
should remain with the financial player, regardless of the intended objectives.
14. Making employees and officers accountable with respect to the use of AI
AISs can enhance the work performance of a financial player’s
employees. Given the potential for automation bias, financial players’
employees and officers should be made aware that they are at all times
accountable for their actions and decisions and that their actions anddecisions cannot be attributed to an AIS.
15.	Implementing human control proportional to AIS risks

Automation bias is the propensity
for humans to favour suggestions fromautomated decision-making systems
and to ignore contradictory information
made without automation, even if it iscorrect.

(Source: Wikipedia)
An AIS’s level of task-performance and decision-making autonomy
should be established and justified at the design stage. The design
should also enable an employee with the necessary skills to exercise
adequate control over each deployed AIS based on its impact on consumers and the risks associated with its use.
An employee should, among other things, individually review and validate all AIS decisions that adversely affect a
consumer’s ability to obtain a financial product or service or any other decision that has a high impact on the consumer’s
financial well-being.

3.5

PRACTICES RELATED TO AI DESIGN AND USE

A flawed governance framework, confusion over roles and responsibilities, or the absence of a clear ethical framework
creates situations conducive to the materialization of risks associated with a financial player’s use of AI. The practices
presented in this section are intended to mitigate such risks.
16. Overseeing AI design and use
A governance structure should be put in place to ensure oversight of a financial player’s use of AI. This structure should
include standards governing AIS design and use, such as model calibration and data processing standards. It should
be possible to adjust these standards based on the AIS’s impact on consumers and the risks associated with its use, andto cover all stages of its life cycle.
The roles and responsibilities of each AIS stakeholder, including the officers of the financial player, should be clearly
defined. These parties should have the skills and resources required to discharge their responsibilities.

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17. Establishing a code of ethics for the design and use of AI
A code of ethics should be established or amended to include the values and ethical principles that should be observed
when using AI and to set out the sanctions applicable in the event of non-compliance with the obligations specified in
the code. Individuals subject to this code of ethics should also be provided with an interpretation of those principles inplain and concise language.
In the context of the design of an AIS, one might consider involving a committee in the assessment of the ethical risks
associated with an AIS and the implementation of mitigation measures.
18. Creating an environment favourable to transparency and disclosure
An organizational environment should be promoted that encourages transparency and disclosure so that doubts orconcerns related to AISs (e.g., errors, negative outcomes, data misuse and data leaks) may be raised anonymously andwithout fear of reprisal.
19. Establishing a consistent approach to AIS design, deployment and monitoring
The approach to AIS design, deployment and monitoring should be consistent across the organization, as opposed tofragmented or decentralized, in order to, for example, be able to identify interconnection risks when a dataset is used
as inputs for more than one AIS. An ethics-by-design approach should be favoured.
In addition, financial players should implement regular, comprehensive reporting to senior management and the board
directors on the performance of all deployed AISs and the risks associated with them.
20. Facilitating the creation of diversified work teams
Diversity and inclusion are values critical to the development of AISs for use in the financial sector. Consequently, the teams
formed to develop, deploy and monitor a financial player’s AISs should reflect the diversity of consumers and society as
a whole in order to, for example, prevent the risk of unjustifiably discriminating among consumers. These teams would
not only be diverse in terms of gender, culture and age but also professionally and in terms of skills.
21. Conducting due diligence on third-party AISs
Due diligence should be conducted when procuring an AIS designed or provided by a third party. Third-party AISs andthe associated training data should be subject to the same standards as in-house designed AISs. Consequently, staff
involved in procuring a third-party AIS should have the skills required to apply those standards properly.
Also, the financial player and the third party should enter into an agreement setting out clear service levels and the rights
and remedies available to the financial player in case of poor AIS performance.

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22. Using AI in a manner enabling the achievement of sustainable development objectives
The carbon footprint from the design and use of AISs, including data hosting, should be measured and limited by avoiding
such things as the proliferation of unnecessary or redundant data infrastructures, employing simpler or pretrained models
or limiting the use of energy-intensive AISs.
Generally, AI should be used by a financial player in a manner consistent with the financial player’s environmental
commitments.

3.6

PRACTICES RELATED TO MANAGING AI-ASSOCIATED RISKS

The practices presented in this section are intended to reduce the occurrence of potential adverse consequences resulting
from unexpected behaviours exhibited by an AIS, including, without limitation, unacceptable harms for consumers.
23. Assessing the risks associated with the use of an AIS
Risks that may result from the use of an AIS must be identified considering the AIS’s impact on consumers and the risks
associated with its use to the financial player’s activities, market integrity, and financial stability. Important factors toconsider in assessing these risks include the IAS’s degree of complexity, opacity, dynamism and autonomy.
More resources should be dedicated to the design and monitoring of an AIS with a potential high impact on consumers
or with a high risk assessment.
24. Ensuring AIS security
A financial player’s AISs should be protected, particularly againsthacking (including data poisoning or adversarial attacks) and cyberattacks. When implementing security measures, AIS interconnectivity
should be assessed to limit the extent of attacks or disruptions. These
measures should be monitored and reviewed to ensure, among otherthings, that they remain aligned with technological developments.
In addition, measures should be implemented to ensure the integrityof the data used as inputs or to train AISs, even if the data is not
personal information. Only authorized personnel should have accessto such data.

Data poisoning attacks andadversarial attacks (sometimes also
referred to as adversarial example
attacks) seek to alter an AIS’s behaviour
by adding malicious or corrupted data
during the training or production phase.
(Source: Commission Nationale del’Informatique et des Libertés)(in French only)

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25. Governing the data used by AISs
Governance of the data used to train an AI model or as inputs for an
AIS should be implemented through, in particular, processes forassessing data representativeness and quality and for determining
whether discriminatory biases are present in the data. An AIS with ahigh impact on consumers should not be deployed if the risks associatedwith the data used are not adequately mitigated.
The financial player’s governance rules should be applied to both datait has collected or generated itself and data procured from third parties.
Particular attention should be paid to potential issues arising from theuse of non-traditional, unstructured synthetic, anonymized and deidentified data, as well as issues that may emerge during the processof aggregating data from various sources (e.g., the risk of consumer
re-identification). All AISs should be tested on real (non-synthetic) data
before being released to production.
Data should also be used in accordance with the applicable terms ofuse (e.g., where data is copyrighted).
26. Managing the risks associated with AI models
The robustness of an AIS (i.e., its ability to function effectively under avariety of conditions) is critical to its reliability. The robustness of AISs
designed by a financial player should be enhanced by the competenceof the staff entrusted with this task and through the use of rigorous
design guides based on recognized technical standards.

Synthetic data is data that is artificiallygenerated through simulations rather
than produced by real-world events.
Synthetic data production aims to create
a dataset with statistical properties
as close as possible to a real dataset
but that does not contain personal
information, for example.
(Source: Wikipedia)
Data that is personal information is
de-identified when it no longer allows
the person concerned to be directly
identified. In addition, data that is
personal information is anonymized if
it is, at all times, reasonably foreseeable
in the circumstances that it irreversibly nolonger allows a person to be identified
directly or indirectly. Re-identification
risk is the risk that a person may be
identified from data that has been
anonymized or de-identified.
(Source: Commission d’accès àl’information) (in French only)

The risks associated with the models underlying AISs should be
identified, assessed and mitigated at each stage of their life cycle.
This practice extends to the use of open-source code models.
Risks that should be identified and managed include model drift or unexpected AIS behaviours in new conditions.
In addition, particular attention should be paid to risks arising from interconnections between a financial player’s AIS
when, for example, the outcomes of one AIS are used as inputs for another AIS. Finally, succession risk should be mitigatedby ensuring the continued availability of competent staff qualified to ensure AIS monitoring.

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27. Performing an impact analysis and testing an AIS
An impact analysis (also known as an algorithmic impact assessment) should be initiated at an AIS’s design stage.
The depth of the impact analysis should be adjusted based on the impact that the AIS could have on consumers (or aparticular group of consumers).
As part of the analysis, the appropriateness of using AI and of incursions into consumers’ private lives should be justified.
The analysis should also address discriminatory biases (often discovered during the parameter estimation and model
assessment phases), conflicts of interest, or other factors that could cause inequitable outcomes.
Testing in an environment separate from the production environment should be conducted to validate the absence oferrors and malfunctions and verify that the AIS behaves as expected in a variety of conditions.
An impact analysis and pre-deployment testing should also be conducted when material changes are made to an AIS,
significant drift occurs, or an AIS is used to achieve new objectives.
Impact analysis and testing should be entrusted to a diversified multidisciplinary team that is competent (particularly inthe area in which the AIS will be used) and trained in AI-related ethical issues.
28. Monitoring AIS performance on an ongoing basis
AIS monitoring should be implemented to enable the detection of deviations from normal operation (such as material
model drift or a degradation in input data quality), discriminatory or inequitable outcomes, inappropriate use, or use
for harmful purposes. Thresholds should be established for critical parameters, and an in-depth AIS review should betriggered if the thresholds are exceeded.
A circuit-breaking feature to interrupt the use of an AIS if its performance deteriorates beyond a given threshold shouldbe implemented and periodically tested. In addition, a financial player should include its AISs in its business continuity
plans to ensure the financial player’s operational resilience.
29. Regularly auditing AISs
Deployed AISs should be audited periodically. Audit depth and frequency and the determination of whether to use
external, independent auditors should be based on an AIS’s impact on consumers and the risks associated with its use.
Documentation relevant to the AIS, together with the codes and training datasets, should be made available to theauditors and regulators.
30. Training employees and users on AI
Employees and any other third parties involved in designing, deploying and monitoring an AIS, senior management, andother managers with responsibilities in connection with an AIS should maintain an appropriate level of technical skill andsufficient knowledge of the ethical risks to perform their tasks properly. In addition to technical and ethical aspects, training
and awareness initiatives deployed by a financial player should cover personal information and privacy protection andintellectual property compliance. Employee training and awareness initiatives should also address the proper use ofgenerative AI tools available online.
In addition, all users of an AIS deployed by a financial player, including consumers or other external parties, should be
able to understand the AIS’s objectives and limitations. They should have access to the training they need to use it properly.

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4. Discussion
The AMF wishes to initiate a discussion with all Québec financial sector stakeholders on best practices that promote
the responsible use of AI. It will be a general consultation on the opportunities and risks associated with the use of AI infinance and on the actions that should be focused on to mitigate the risks to consumers, financial players, market integrity
and financial stability.
The issues raised and practices proposed in this document affect all financial sector stakeholders, investors and financial
consumers.
Interested parties are invited to request a meeting with the AMF contact persons so that they can submit their commentson the proposed practices and their answers to the questions below before June 14, 2024. The AMF will also beorganizing round tables and other discussion forums during this period in order to obtain additional input and deepen its
understanding of the use of AI in the financial sector. Information about the various ways to participate in this discussion
is available on the AMF website at https://lautorite.qc.ca/en/general-public/publications/for-professionals/ai-in-thefinancial-sector.
Also, anyone interested in submitting comments in writing may do so on or before June 14, 2024, by sending them to:
Me Philippe Lebel
Corporate Secretary and Executive Director, Legal Affairs
Autorité des marchés financiers
Place de la cité, tour Cominar
2640, boulevard Laurier, 3e étage, Québec (Québec) G1V 5C1
Fax: 418-525-9512
E-mail: consultation-en-cours@lautorite.qc.ca.

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ADDITIONAL DISCUSSION QUESTIONSIn addition to comments on the appropriateness of the best practices presented in this paper, the AMF wishes to receive
comments responding to the following questions:
1. In addition to the values and ethical principles set out in this paper, what other important principles do you thinkshould be put forward?
2. Which cases of AI use in finance present the most risks? Are the proposed practices sufficient to mitigate the risks
associated with the use of AI in the financial sector? Consider the risks to consumers, financial players, market integrity
and financial stability.
3. Is there a risk that the implementation of the practices described in this paper could inhibit innovation in the financial
sector? If so, why?
4. Do the financial players have the resources, particularly the required qualified human resources, to implement the
proposed practices? Does the implementation of the practices described in this paper seem achievable to you? Over
what time horizon (short, medium, long term)?
5. What role should technical standards play in practices related to the responsible use of AI in the financial sector?
6. How are the financial players integrating generative AI into their activities? How are they regulating employees’
use of on-line generative AI tools?
7. What role do you see the AMF playing in supporting financial players in integrating AI into their activities, and what
would be the best way for the AMF to fulfill that role?

AMF CONTACT PERSONSClarification or additional information can be obtained by contacting:
Lise Estelle Brault, CFA

Marie-Ève Lainez

Senior Director, Data Value Creation,
Digital Transformation and Innovation

Director, Digital Transformation and Innovation

Telephone: 514-395-0337, ext. 4481
Toll-free: 1-877-525-0337, ext. 4481
LiseEstelle.Brault@lautorite.qc.ca

Telephone: 514-395-0337, ext. 4891
Toll-free: 1-877-525-0337, ext. 4891
Marie-Eve.Lainez@lautorite.qc.ca

February 12, 2024

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THE ALAN TURING INSTITUTE (2023). “The AI Revolution: Opportunities and Challenges for the Finance Sector.”
September 2023. Online: https://www.turing.ac.uk/sites/default/files/2023-09/full_publication_pdf_0.pdf

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THE WHITE HOUSE (2023). “Fact Sheet: U.S.-EU Trade and Technology Council Deepens Transatlantic Ties,” May 31.
Online: https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/31/fact-sheet-u-s-eu-trade-andtechnology-council-deepens-transatlantic-ties/
THE WHITE HOUSE (2023). “Voluntary AI Commitments.” September 2023. Online: https://www.whitehouse.gov/
wp-content/uploads/2023/09/Voluntary-AI-Commitments-September-2023.pdf
WEISER, BENJAMIN (2023). “Here’s What Happens When Your Lawyer Uses ChatGPT.” The New York Times, May
27, 2023. Online: https://www.nytimes.com/2023/05/27/nyregion/avianca-airline-lawsuit-chatgpt.html

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Autorité des marchés financiers / Issues and Discussion Paper /Best practices for the responsible use of AI in the financial sector

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