Cada vez es más común interactuar con productos que parecen “inteligentes”, aunque quizás la etiqueta “inteligencia artificial” haya sido sustituida por otros eufemismos. Desde noviembre de 2022, con la aparición de la herramienta ChatGPT, ha habido un aumento exponencial en el uso de la inteligencia artificial en todos los ámbitos. Aunque ChatGPT es solo una de las muchas tecnologías generativas de inteligencia artificial, su impacto en los procesos de enseñanza y aprendizaje ha sido notable. Este artículo reflexiona sobre las ventajas, inconvenientes, potencialidades, límites y retos de las tecnologías generativas de inteligencia artificial en educación, con el objetivo de evitar los sesgos propios de las posiciones extremistas. Para ello, se ha llevado a cabo una revisión sistemática tanto de las herramientas como de la producción científica que ha surgido en los seis primeros meses desde la aparición de ChatGPT. La inteligencia artificial generativa es extremadamente potente y mejora a un ritmo acelerado, pero se basa en lenguajes de modelo de gran tamaño con una base probabilística, lo que significa que no tienen capacidad de razonamiento ni de comprensión y, por tanto, son susceptibles de contener fallos que necesitan ser contrastados. Por otro lado, muchos de los problemas asociados con estas tecnologías en contextos educativos ya existían antes de su aparición, pero ahora, debido a su potencia, no podemos ignorarlos solo queda asumir cuál será nuestra velocidad de respuesta para analizar e incorporar estas herramientas a nuestra práctica docente.

La nueva realidad de la educación ante los avances de la inteligencia artificial generativa

This study delves into the dual nature of artificial intelligence (AI), illuminating its transformative potential that has the power to revolutionize various aspects of our lives. We delve into critical issues such as AI hallucinations, misinformation, and unpredictable behavior, particularly in large language models (LLMs) and AI-powered chatbots. These technologies, while capable of manipulating human decisions and exploiting cognitive vulnerabilities, also hold the key to unlocking unprecedented opportunities for innovation and progress. Our research underscores the need for robust, ethical AI development and deployment frameworks, advocating a balance between technological advancement and societal values. We emphasize the importance of collaboration among researchers, developers, policymakers, and end users to steer AI development toward maximizing benefits while minimizing potential harms. This study highlights the critical role of responsible AI practices, including regular training, engagement, and the sharing of experiences among AI users, to mitigate risks and develop the best practices. We call for updated legal and regulatory frameworks to keep pace with AI advancements and ensure their alignment with ethical principles and societal values. By fostering open dialog, sharing knowledge, and prioritizing ethical considerations, we can harness AI’s transformative potential to drive human advancement while managing its inherent risks and challenges.

The Era of Artificial Intelligence Deception: Unraveling the Complexities of False Realities and Emerging Threats of Misinformation

Problem statement: Standardisation of AI fairness rules and benchmarks is challenging because AI fairness and other ethical requirements depend on multiple factors, such as context, usage case, type of the AI system, and so on. In this paper, we elaborate that the AI system is prone to biases at every stage of its lifecycle, from inception to its use, and that all stages require due attention for mitigating AI bias. We need a standardised approach to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot research topic, a holistic strategy for AI fairness is generally missing. The AI fairness assessment is often developer-centric. Standard processes are required for independent audits for the benefit of other stakeholders. Also, most researchers focus only on a few facets of AI model-building. Peer review shows a focus on biases in the datasets, fairness metrics, and algorithmic bias. In the process, other aspects affecting AI fairness get ignored. The solution proposed: We propose a comprehensive approach in the form of a novel seven-layer model, inspired by the Open System Interconnection (OSI) model, to standardise AI fairness handling. Despite the differences in the various aspects, most AI systems have similar model-building stages. The proposed model splits the AI system lifecycle into seven abstraction layers, each corresponding to a well-defined AI model-building or usage stage. We also provide checklists for each layer and deliberate on potential sources of bias in each layer and their mitigation methodologies. This work will facilitate layer-wise standardisation of AI fairness rules and benchmarking parameters. 

https://link.springer.com/content/pdf/10.1007/s43681-023-00266-9.pdf

A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle

Abstract Fairness in machine learning (ML) is an ever-growing field of research due to the manifold potential for harm from algorithmic discrimination. To prevent such harm, a large body of literature develops new approaches to quantify fairness. Here, we investigate how one can divert the quantification of fairness by describing a practice we call “fairness hacking” for the purpose of shrouding unfairness in algorithms. This impacts end-users who rely on learning algorithms, as well as the broader community interested in fair AI practices. We introduce two different categories of fairness hacking in reference to the established concept of p-hacking. The first category, intra-metric fairness hacking, describes the misuse of a particular metric by adding or removing sensitive attributes from the analysis. In this context, countermeasures that have been developed to prevent or reduce p-hacking can be applied to similarly prevent or reduce fairness hacking. The second category of fairness hacking is inter-metric fairness hacking. Inter-metric fairness hacking is the search for a specific fair metric with given attributes. We argue that countermeasures to prevent or reduce inter-metric fairness hacking are still in their infancy. Finally, we demonstrate both types of fairness hacking using real datasets. Our paper intends to serve as a guidance for discussions within the fair ML community to prevent or reduce the misuse of fairness metrics, and thus reduce overall harm from ML applications. 

pdf/fairness-hacking-the-malicious-practice-of-shrouding-3z83dxp29k.pdf

Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms

Artificial intelligence (AI) and its subset machine learning (ML), which focuses on learning tasks from data, are some of the most disruptive emergent technologies. AI standards inform organisations how to develop and manage their AI systems and are emerging to satisfy the increasing demand from industry and society for the safe adoption of AI and ML technologies. However, AI systems must be trustworthy in the sense that they can be relied upon to make responsible decisions. Consequently, trustworthy AI is a collection of principles that encourages responsible development, use and deployment of AI systems, and can be viewed as a framework for managing risk in AI systems. The National Physical Laboratory (NPL) is one of the four institutions responsible for delivering the UK’s national quality infrastructure (NQI), in which standards and certification play key roles. In this context we review research in NPL on trustworthy AI, emphasising the importance of uncertainty quantification (UQ) in enhancing the transparency and trust in results output from AI systems. We review the landscape of AI standards and certification and emphasise their role in the context of trustworthy AI. Third-party certification is a key service in building trust in AI and ML systems and supporting their operationalisation. We argue that certification should assess conformity to AI standards and characteristics of trustworthy AI, and, in addition, should be able to carry out conformity testing and evaluation of the components of an AI system. As a case study we look at ChatGPT, a large AI system which is attracting a lot of attention, and investigate its potential conformity to the principles of trustworthy AI.

http://eprintspublications.npl.co.uk/9683/1/MS45.pdf

Certification of machine learning applications in the context of trustworthy AI with reference to the standardisation of AI systems

Decisions made by various Artificial Intelligence (AI) systems greatly influence our day-to-day lives. With the increasing use of AI systems, it becomes crucial to know that they are fair, identify the underlying biases in their decision-making, and create a standardized framework to ascertain their fairness. In this paper, we propose a novel Fairness Score to measure the fairness of a data-driven AI system and a Standard Operating Procedure (SOP) for issuing Fairness Certification for such systems. Fairness Score and audit process standardization will ensure quality, reduce ambiguity, enable comparison and improve the trustworthiness of the AI systems. It will also provide a framework to operationalise the concept of fairness and facilitate the commercial deployment of such systems. Furthermore, a Fairness Certificate issued by a designated third-party auditing agency following the standardized process would boost the conviction of the organizations in the AI systems that they intend to deploy. The Bias Index proposed in this paper also reveals comparative bias amongst the various protected attributes within the dataset. To substantiate the proposed framework, we iteratively train a model on biased and unbiased data using multiple datasets and check that the Fairness Score and the proposed process correctly identify the biases and judge the fairness. 

https://link.springer.com/content/pdf/10.1007/s43681-022-00147-7.pdf

Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems

Advancing Trustworthy AI: A Comparative Evaluation of AI Robustness Toolboxes

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Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems

A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle

Advancing Trustworthy AI: A Comparative Evaluation of AI Robustness Toolboxes