In April, 2018, the European Commission established its vision of Articial Intelligence (AI), leading to the production of guidelines to achieve trustworthy AI one year later. These guidelines, although not mentioning it explicitly, over ow with issues well known in Requirements Engineering (RE). By relating recent RE works to these guidelines, this position paper attempts to show that RE is one of the core components for achieving trustworthy AI, and thus can have a critical impact on the evolution of AI systems and the AI eld as a whole for the next few years in Europe.
The AI HLEG guidelines [AI 19] highlight three components which should be met throughout the entire life cycle of an AI system, from its conception to its disposal. It must be lawful, thus complying with applicable laws and regulations, ethical, thus ensuring adherence to ethical principles and values, and robust (both from a technical and social perspective), thus avoiding unintentional harm. While the lawful aspect, despite being complex to achieve, is a rather straightforward objective (i.e. comply to national, European, and international laws), the ethical and robust aspects bring more interpretations, and thus are further investigated in these guidelines. This investigation has lead the AI HLEG to establish seven key requirements to be met, through technical and non-technical means, for the development, deployment, and use of AI systems.
Human agency and oversight, thus allowing humans to assess or challenge the system, as well as governance mechanisms of humans over the system.
Technical robustness and safety, including resilience to attack and security, fall back plan and general safety, accuracy, reliability and reproducibility.
Privacy and data governance, especially privacy of acquired and generated user data, the quality and integrity of the data used in learning processes, and a controlled access to user data.
Transparency by supporting the traceability and explainability of the processes and decisions of the AI system, as well as communicating about its abilities and limitations.
Diversity, non-discrimination and fairness, especially the avoidance of unfair bias, allowing all people to access and use the AI system through accessibility and universal design, and the participation of stakeholders who might be directly or indirectly a ected.
Environmental and societal well-being by monitoring the impact on society and democracy and considering the broader society, other sentient beings and the environment, as stakeholders for sustainability and environmental friendliness.
Accountability through auditability, without impairing business models and intellectual property, minimisation and reporting of negative impacts, including whistle-blowers and NGOs, ensuring trade-o s when con icts arise between these requirements, and supporting redress when adverse impacts occur despite the care provided to meet these requirements.
To implement the above requirements, both technical and non-technical methods must be considered. By technical methods, we could mention speci c AI system architectures, \by design" conceptions (e.g. privacyby-design, security-by-design), explanation methods (e.g. eld of Explainable AI (XAI)), testing and validating, and quality of service (QoS) indicators. Non-technical methods could be regulations (e.g. product safety laws), codes of conduct and key performance indicators (KPIs), standardisation (e.g. ISO, IEEE P7000), certi cation, accountability via governance frameworks (e.g. person in charge of AI ethics), education, and social dialogue. These methods, and more globally the requirements to satisfy, should encompass all stages of an AI systems life cycle, as shown in Figure 1, reproduced from [AI 19].
If the reader is familiar with RE literature, we might dare to claim that reading the previous section was enough to remind him or her of several RE works. Indeed, since the guidelines should be operationalized throughout the whole life time of AI systems, it also covers all the phases of RE, from eliciting the requirements of the AI system to verifying their satisfaction. Actually, each requirement can be related to recent works in the eld: Human agency and oversight can be supported at development time, by supporting the decision making about quality requirements at strategic and operational levels [OW18], as well as run time, by monitoring the satifaction of the requirements and diagnosing their violations [VRCH17].
Technical robustness and safety is also of interest, whether we speak about methods to follow o cial standards or the impacts of these methods on the people implementing them [PH17]. The AI HLEG also includes in this requirement the security aspect, which can be covered by various sources of security requirements, thus helping to reach a comprehensive set of security goals [GBO18].
Privacy and data governance can be subject to interpretation misalignments, which can be mitigated through shared ontologies [BHBN18], as well as incomplete privacy policies, which can increase the perceived privacy risks of the users [BEB19].
Transparency regarding how the AI system works can be achieved by showing how the components of the produced AI system relate to its requirements, in other words having a performant traceability [HP18], but transparency can also bring issues like information overload, information starvation, and transparency leading to biased decisions [HSPA18].
Diversity, non-discrimination and fairness can be achieved by ensuring that the relevant stakeholder proles are explicitly considered, for example through a comprehensive list of personas and persona-based modelling [NRJTW+18] or by analysing the massive feedback of actual users of an AI system [LZW18]. Environmental and societal well-being is also an aspect that some RE researchers investigate, like integrating green strategies in quality requirements for optimizing energy and other resources consumption [CFL18]. Accountability is also of interest in RE, whether by supporting auditors of AI systems by ensuring that traceability links reach a satisfying level of quality [HP18] or by supporting the audited companies in evolving their acceptance tests based on the evolution of the requirements they should meet [HBCG18]. We can also go further by considering RE frameworks, like OpenReq [PFF18], which help requirements analysts through automated recommendations of various kinds, like new requirements, quality tips, stakeholders to consider, or requirements prioritization. Such kind of framework could be strengthened and extended to help requirements analysts to cope with the various dimensions covered by the AI HLEG requirements list. Although not always related to AI, and without assuming they are the most relevant, these works deal with these requirements in some ways, and thus provide illustrative examples of how RE is deeply linked to these guidelines.
At this point, we only related existing RE works to the high level requirements of the guidelines. But we can further look in the details, especially by taking the dichotomy operated by the AI HLEG with technical and non-technical methods to satisfy these requirements.
The technical methods mainly relate to what the AI scienti c community may produce, but works in RE can also help on some regards. For instance, we work on translating human-written mind map diagrams into machine-readable graphs to produce knowledge bases to be queried [BGOK18]. The structured graphs and the models used for translating the mind maps into the graphs o er a support to explain the translation. An AI system based on such a knowledge base would be then at least partially explainable, as opposed to a knowledge base directly built from unstructured data. We can also mention the writing of acceptance tests: automatically translating requirements into acceptance tests is not a recent idea [GH99] and we are also working on how to evolve tests with their requirements [HBCG18]. Quality of service (QoS) indicators can also build on the satisfaction of quality requirements if we structure and formalize them well enough to build concrete quality measures [OW18].
At the opposite, non-technical methods can have a rather comprehensive coverage of RE works. Regulations are far to be ignored in RE, with for example the Nomos framework to deal with them [Sie10, IJS+14]. Of course, they are only one source of norms, and other approaches consider more sources of requirements to be more comprehensive [GBO18], including standards and certi cations. [BOJRG18] also highlights the lack of standards to ensure the compatibility of the Internet of Things (IoT) ecosystems, an aspect also applicable to ethical concerns since each component of an IoT ecosystem might ful l them to di erent degrees and in di erent ways, thus making hard to grasp the ful lment of the whole. RE can also help in the elicitation of user preferences from massive amounts of users [LZW18] and thus help in establishing lists of concrete requirements, based on social dialogue, that governance frameworks could build on.
In brief, it is easy to nd recent RE works to illustrate the multiple focuses of the AI HLEG guidelines. In fact, to do so, we were prepared to look at papers published in several occurrences of RE-related venues like REFSQ, EmpiRE, CAiSE, or iStar. In the end, the attentive reader may have noticed that, among the 18 RE references we cite in this section, 11 are from REFSQ 2018 (almost half of the 23 papers of the venue), with 9 of them used to illustrate all the key requirements presented. In other words, a single occurrence of a RE-related conference can su ce to broadly illustrate the key requirements of the AI HLEG guidelines, an evidence that further strenghten the correlation between these guidelines and the eld of RE. 4
Of course, relating existing works to the AI HLEG requirements does not mean that we have all we need to satisfy them. Rather, there is still a lot of work, starting from communicating better what we do. Indeed, despite the 59 occurrences of \requirements" (without counting pictures) in the 41 pages of the AI HLEG document, the total absence of \requirements engineering" should ring a bell.
Beside making people aware of the RE methods and tools, there is also space for improvements to support the AI HLEG key requirements. For instance, although we have a growing interest in the challenges of AIrelated topics, like the Internet of Things (IoT) [BOJRG18], it is still young on the speci cities of Big Data issues [AM18], a core aspect of AI. There is also non AI-speci c aspects which are still open issues in RE, like how to deal with ambiguous and incomplete requirements [DvdSL18]. [CHD18] also shows that human analysts can have various degrees of reliability in recovering traces, and thus further support is required. Requirements analysts may exploit tools to help them in various RE tasks, but some are still far from a reliable use in production [HP18]. Dealing with requirements in complex organizations is by itself challenging, with the di culty to detect infeasible requirements, the variance in assumptions and de nitions between teams, or the overlook of sources of requirements [ADW18, WPKG18].
We can also take a step further: after considering how to use RE for AI, we can also consider how to use AI for RE. Like other elds, RE is gradually relying on more AI techniques to help the requirements analysts doing their job [WV18, DvdSL18, PFF18, LZW18]. But if we claim that RE can help in designing ethical AI systems, can we still say so if the AI systems RE relies on are not ethical? Can we ensure that the requirements we help to discover allow to reach ethical AI if the AI used to identify those requirements is not ethical? In other words, RE4AI is not the end of the loop: it is but a mere step towards RE4RE. 5
In this position paper, we attempted to show that RE has a tremendous opportunity in the current European trends in the eld of AI. We rst explained how AI is considered as a key component for the future by the European Comission, with the production of guidelines to produce trustworthy AI systems (i.e. lawful, ethical, and robust). We listed the seven key requirements of these guidelines and tried to show how recent works in RE relate to them, from both technical and non-technical perspectives. We hope that these relations show how the eld of RE, despite not being explicitly mentioned, is at the core of these guidelines, and thus the great amount of opportunities that RE researchers may found in working on this topic. We also relied on recent works in RE to remind some current challenges that may increase the di culty of this task. Nevertheless, we have no doubt that the RE community is strong and dynamic enough to tackle them. We are aware that the guidelines are already two years old, and that our presentation is far from the high standards of the scienti c rigour, but we hope that this position paper brings a relevant and interesting point to the workshop discussions, maybe helping at drawing a future research agenda. 6
This publication re ects the view only of the author, and Meritis PACA cannot be held responsible for any use which may be made of the information contained therein. [ADW18]
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