Perceived Trustworthiness of Natural Language Generators

Natural Language Generation tools, such as chatbots that can generate human-like conversational text, are becoming more common both for personal and professional use. However, there are concerns about their trustworthiness and ethical implications. The paper addresses the problem of understanding how different users (e.g., linguists, engineers) perceive and adopt these tools and their perception of machine-generated text quality. It also discusses the perceived advantages and limitations of Natural Language Generation tools, as well as users' beliefs on governance strategies. The main findings of this study include the impact of users' field and level of expertise on the perceived trust and adoption of Natural Language Generation tools, the users' assessment of the accuracy, fluency, and potential biases of machine-generated text in comparison to human-written text, and an analysis of the advantages and ethical risks associated with these tools as identified by the participants. Moreover, this paper discusses the potential implications of these findings for enhancing the AI development process. The paper sheds light on how different user characteristics shape their beliefs on the quality and overall trustworthiness of machine-generated text. Furthermore, it examines the benefits and risks of these tools from the perspectives of different users.Comment: 16 pages, 5 figures, First International Symposium on Trustworthy Autonomous Systems (TAS '23

RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems

Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. However, practitioners lack actionable instructions to operationalise ethics during AI systems development. A literature review of different ethical guidelines revealed inconsistencies in the principles addressed and the terminology used to describe them. Furthermore, requirements engineering (RE), which is identified to foster trustworthiness in the AI development process from the early stages was observed to be absent in a lot of frameworks that support the development of ethical and trustworthy AI. This incongruous phrasing combined with a lack of concrete development practices makes trustworthy AI development harder. To address this concern, we formulated a comparison table for the terminology used and the coverage of the ethical AI principles in major ethical AI guidelines. We then examined the applicability of ethical AI development frameworks for performing effective RE during the development of trustworthy AI systems. A tertiary review and meta-analysis of literature discussing ethical AI frameworks revealed their limitations when developing trustworthy AI. Based on our findings, we propose recommendations to address such limitations during the development of trustworthy AI.Comment: Accepted at [TAS '23]{First International Symposium on Trustworthy Autonomous Systems

A Shift In Artistic Practices through Artificial Intelligence

The explosion of content generated by Artificial Intelligence models has initiated a cultural shift in arts, music, and media, where roles are changing, values are shifting, and conventions are challenged. The readily available, vast dataset of the internet has created an environment for AI models to be trained on any content on the web. With AI models shared openly, and used by many, globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring into music, arts, and new media?Comment: Submitted to Leonardo Journa

Generative Artificial Intelligence for Software Engineering -- A Research Agenda

Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios. We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering. We identified 78 open Research Questions (RQs) in 11 areas of Software Engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology. GenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research

AI for Agile development: a Meta-Analysis

This study explores the benefits and challenges of integrating Artificial Intelligence with Agile software development methodologies, focusing on improving continuous integration and delivery. A systematic literature review and longitudinal meta-analysis of the retrieved studies was conducted to analyse the role of Artificial Intelligence and it's future applications within Agile software development. The review helped identify critical challenges, such as the need for specialised socio-technical expertise. While Artificial Intelligence holds promise for improved software development practices, further research is needed to better understand its impact on processes and practitioners, and to address the indirect challenges associated with its implementation.Comment: 8 pages, 2 figures, 24th International Conference on Agile Software Development. AI-Assisted Agile Software Development Research Worksho

Automating crowd simulation: from parameter tuning to dynamic context-to-policy adaptation

Computer-generated crowds are becoming more and more frequent in films, video games and safety assessment applications. Many crowd simulation algorithms exist to address the needs of this diverse range of industries. Even though the underlying principles are similar, there are large differences between the resulting synthetic trajectories. Each algorithm has strengths and weaknesses that need to be weighted, and appropriate parameter values for them must be selected as well. These are not easy tasks and Machine Learning algorithms are often used to guide these decisions. In this work we study three of these tasks: parameter tuning, trajectory evaluation, and character policy selection and adaptation. Our results show the usefulness of the proposed methods to evaluate previously unseen synthetic trajectories to find appropriate parameter values for the algorithms without directly relying on real data. Moreover, by classifying the context of characters, we propose a policy adaptation strategy to improve crowd simulations.Les multituds simulades per ordinador són cada cop més habituals en cinema, vídeo jocs i en aplicacions relacionades amb la seguretat. Existeixen molts algoritmes per simular multituds per adreçar tal varietat d’indústries. Tot i que els principis subjacents són similars, hi ha diferències entre les simulacions resultants. Cada algoritme té avantatges i inconvenients que s’han de valorar, i, a més a més, cal trobar valors pels seus paràmetres. Aquestes no són tasques senzilles i, sovint, es fan servir algoritmes d’aprenentatge automàtic per guiar aquestes decisions. Estudiem tres d’aquestes tasques: donar valor als paràmetres, avaluar trajectòries, i adaptar les polítiques. Els resultats demostren la utilitat dels mètodes proposats per avaluar trajectòries noves per tal de trobar valors apropiats pels paràmetres dels algorismes sense fer servir dades reals directament. A més a més, proposem una estratègia per adaptar la política de cada agent a través del reconeixement del context, millorant les simulacions

Cross-Entropy method for Kullback-Leibler control in multi-agent systems

Supervisor: Dr. Vicenç Gómez Cerdà; Co-Supervisor: Dr. Mario CeresaTreball fi de màster de: Master in Intelligent Interactive SystemsWe consider the problem of computing optimal control policies in large-scale multiagent systems, for which the standard approach via the Bellman equation is intractable. Our formulation is based on the Kullback-Leibler control framework, also known as Linearly-Solvable Markov Decision Problems. In this setting, adaptive importance sampling methods have been derived that, when combined with function approximation, can be effective for high-dimensional systems. Our approach iteratively learns an importance sampler from which the optimal control can be extracted and requires to simulate and reweight agents’ trajectories in the world multiple times. We illustrate our approach through a modified version of the popular stag-hunt game; in this scenario, there is a multiplicity of optimal policies depending on the “temperature” parameter of the environment. The system is built inside Pandora, a multi-agent-based modeling framework and toolbox for parallelization, freeing us from dealing with memory management when running multiple simulations. By using function approximation and assuming some particular factorization of the system dynamics, we are able to scale-up our method to problems with M = 12 agents moving in two-dimensional grids of size N = 21×21, improving on existing methods that perform approximate inference on a temporal probabilistic graphical model

A perceptually-validated metric for crowd trajectory quality evaluation

Simulating crowds requires controlling a very large number of trajectories and is usually performed using crowd motion algorithms for which appropriate parameter values need to be found. The study of the relation between parametric values for simulation techniques and the quality of the resulting trajectories has been studied either through perceptual experiments or by comparison with real crowd trajectories. In this paper, we integrate both strategies. A quality metric, QF, is proposed to abstract from reference data while capturing the most salient features that affect the perception of trajectory realism. QF weights and combines cost functions that are based on several individual, local and global properties of trajectories. These trajectory features are selected from the literature and from interviews with experts. To validate the capacity of QF to capture perceived trajectory quality, we conduct an online experiment that demonstrates the high agreement between the automatic quality score and non-expert users. To further demonstrate the usefulness of QF, we use it in a data-free parameter tuning application able to tune any parametric microscopic crowd simulation model that outputs independent trajectories for characters. The learnt parameters for the tuned crowd motion model maintain the influence of the reference data which was used to weight the terms of QF.With partial support of the EU funded project PRESENT, H2020-ICT-2018-3-856879. As Serra Húnter Fellow, Ricardo Marques acknowledges the support of the Serra Húnter Programme to this work

Dynamic Combination of Crowd Steering Policies Based on Context

Simulating crowds requires controlling a very large number of trajectories of characters and is usually performed using crowd steering algorithms. The question of choosing the right algorithm with the right parameter values is of crucial importance given the large impact on the quality of results. In this paper, we study the performance of a number of steering policies (i.e., simulation algorithm and its parameters) in a variety of contexts, resorting to an existing quality function able to automatically evaluate simulation results. This analysis allows us to map contexts to the performance of steering policies. Based on this mapping, we demonstrate that distributing the best performing policies among characters improves the resulting simulations. Furthermore, we also propose a solution to dynamically adjust the policies, for each agent independently and while the simulation is running, based on the local context each agent is currently in. We demonstrate significant improvements of simulation results compared to previous work that would optimize parameters once for the whole simulation, or pick an optimized, but unique and static, policy for a given global simulation context