Amel Bennaceur

Dhaminda B. Abeywickrama, Amel Bennaceur, Greg Chance et al.

As autonomous systems (AS) increasingly become part of our daily lives, ensuring their trustworthiness is crucial. In order to demonstrate the trustworthiness of an AS, we first need to specify what is required for an AS to be considered trustworthy. This roadmap paper identifies key challenges for specifying for trustworthiness in AS, as identified during the "Specifying for Trustworthiness" workshop held as part of the UK Research and Innovation (UKRI) Trustworthy Autonomous Systems (TAS) programme. We look across a range of AS domains with consideration of the resilience, trust, functionality, verifiability, security, and governance and regulation of AS and identify some of the key specification challenges in these domains. We then highlight the intellectual challenges that are involved with specifying for trustworthiness in AS that cut across domains and are exacerbated by the inherent uncertainty involved with the environments in which AS need to operate.

Amel Bennaceur, Gopi Krishnan Rajbahadur, Prince Mercy et al.

Organisations designing, developing, and deploying machine learning systems (MLS) need to be able to check that these systems are trustworthy, and communicate this clearly to their stakeholders, be they different categories of users, engineers, or wider society. By focusing on stakeholders, Requirements Engineering is well positioned to drive the design and engineering of MLS that align with the needs of their stakeholders. Yet, we still need a systematic process for modelling and reasoning about requirements for MLS that is driven both by stakeholders' needs and constraints for MLS development. This paper proposes a framework entitled REAL (Requirements Engineering for mAchines that Learn - and Fail) to help develop MLS that align with stakeholders' needs by adopting a requirements engineering approach. This model-based framework is based on three principles. First, weaving together requirements for data, models, and the system as a whole. Second, using failure to drive the exploration of alternative requirements. Third, iterative and traceable refinement of MLS requirements. We demonstrate the proposed framework using an example from autonomous driving and show that REAL supports the development of MLS that better align with stakeholders' requirements. A replication package is available online.

Livia Lestingi, Amel Bennaceur, Marcello M. Bersani et al.

Autonomous agents operating in socio-critical settings must coordinate with humans under uncertainty while respecting explicit safety constraints. Existing approaches either account for social dynamics without formal guarantees or provide formal assurance while abstracting away human behaviour. We introduce FormIDEAble, a formally grounded approach for synthesising socially-aware cooperation strategies with safety guarantees. The cooperation between humans and the autonomous agent is modelled as a Priced Timed Markov Decision Process, and decision-making is formulated as a cost-bounded reachability problem. We illustrate the approach using an emergency evacuation scenario. Initial experimental evidence demonstrates the effectiveness of the approach and highlights the trade-offs between optimisation and safety guarantees. FormIDEAble provides a principled foundation for formally assured, socially-aware decision-making in socio-critical systems.

Ali Farahani, Liliana Pasquale, Amel Bennaceur et al.

Software systems are increasingly making decisions on behalf of humans, raising concerns about the fairness of such decisions. Such concerns are usually attributed to flaws in algorithmic design or biased data, but we argue that they are often the result of a lack of explicit specification of fairness requirements. However, such requirements are challenging to elicit, a problem exacerbated by increasingly dynamic environments in which software systems operate, as well as stakeholders' changing needs. Therefore, capturing all fairness requirements during the production of software is challenging, and is insufficient for addressing software changes post deployment. In this paper, we propose adaptive fairness as a means for maintaining the satisfaction of changing fairness requirements. We demonstrate how to combine requirements-driven and resource-driven adaptation in order to address variabilities in both fairness requirements and their associated resources. Using models for fairness requirements, resources, and their relations, we show how the approach can be used to provide systems owners and end-users with capabilities that reflect adaptive fairness behaviours at runtime. We demonstrate our approach using an example drawn from shopping experiences of citizens. We conclude with a discussion of open research challenges in the engineering of adaptive fairness in human-facing software systems.