Hazard Contribution Modes of Machine Learning Components

Amongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool

Artificial intelligence in health care: accountability and safety

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinical tool is something to which current practices of accountability and safety worldwide have not yet adjusted. We focus on two aspects of clinical artificial intelligence used for decision-making: moral accountability for harm to patients; and safety assurance to protect patients against such harm. Artificial intelligence-based tools are challenging the standard clinical practices of assigning blame and assuring safety. Human clinicians and safety engineers have weaker control over the decisions reached by artificial intelligence systems and less knowledge and understanding of precisely how the artificial intelligence systems reach their decisions. We illustrate this analysis by applying it to an example of an artificial intelligence-based system developed for use in the treatment of sepsis. The paper ends with practical suggestions for ways forward to mitigate these concerns. We argue for a need to include artificial intelligence developers and systems safety engineers in our assessments of moral accountability for patient harm. Meanwhile, none of the actors in the model robustly fulfil the traditional conditions of moral accountability for the decisions of an artificial intelligence system. We should therefore update our conceptions of moral accountability in this context. We also need to move from a static to a dynamic model of assurance, accepting that considerations of safety are not fully resolvable during the design of the artificial intelligence system before the system has been deployed

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Development of a Machine Learning Based Algorithm To Accurately Detect Schizophrenia based on One-minute EEG Recordings

While diagnosing schizophrenia by physicians based on patients' history and their overall mental health is inaccurate, we report on promising results using a novel, fast and reliable machine learning approach based on electroencephalography (EEG) recordings. We show that a fine granular division of EEG spectra in combination with the Random Forest classifier allows a distinction to be made between paranoid schizophrenic (ICD-10 F20.0) and non-schizophrenic persons with a very good balanced accuracy of 96.77 percent. We evaluate our approach on EEG data from an open neurological and psychiatric repository containing 499 one-minute recordings of n=28 participants (14 paranoid schizophrenic and 14 healthy controls). Since the fact that neither diagnostic tests nor biomarkers are available yet to diagnose paranoid schizophrenia, our approach paves the way to a quick and reliable diagnosis with a high accuracy. Furthermore, interesting insights about the most predictive subbands were gained by analyzing the electroencephalographic spectrum up to 100 Hz

Health AI for Good Rather Than Evil? The Need for a New Regulatory Framework for AI-Based Medical Devices

Artificial intelligence (AI), especially its subset machine learning, has tremendous potential to improve health care. However, health AI also raises new regulatory challenges. In this Article, I argue that there is a need for a new regulatory framework for AI-based medical devices in the U.S. that ensures that such devices are reasonably safe and effective when placed on the market and will remain so throughout their life cycle. I advocate for U.S. Food and Drug Administration (FDA) and congressional actions. I focus on how the FDA could - with additional statutory authority - regulate AI-based medical devices. I show that the FDA incompletely regulates health AI-based products, which may jeopardize patient safety and undermine public trust. For example, the medical device definition is too narrow, and several risky health AI-based products are not subject to FDA regulation. Moreover, I show that most AI-based medical devices available on the U.S. market are 510(k)-cleared. However, the 510(k) pathway raises significant safety and effectiveness concerns. I thus propose a future regulatory framework for premarket review of medical devices, including AI-based ones. Further, I discuss two problems that are related to specific AI-based medical devices, namely opaque (“black-box”) algorithms and adaptive algorithms that can continuously learn, and I make suggestions on how to address them. Finally, I encourage the FDA to broaden its view and consider AI-based medical devices as systems, not just devices, and focus more on the environment in which they are deployed

Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions

Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions. The consequences of failures and insu_ciencies in such algorithms are severe and a convincing assurance case that the algorithms meet certain safety requirements is therefore required. However, the task of demonstrating the performance of such algorithms is non-trivial, and as yet, no consensus has formed regarding an appropriate set of verification measures. This paper provides a framework for reasoning about the contribution of performance evidence to the assurance case for machine learning in an automated driving context and applies the evaluation criteria to a pedestrian recognition case study

Competency-Based Curriculum Planning Model To Overcome Inconsistencies In Vocational Training

"The objective of the research was to evaluate the consistency of different training proposals, proposing as a reference a competency-based curriculum planning model. Qualitative methodology was used from an interpretive paradigm, making use of inductive and deductive analysis at the same time. Inductively, observations were generated through documentary analysis, interviews with experts, and a focus group with university professors, who then deductively derived interpretations and forecasts regarding curricular planning. As a result, it was obtained that the designed model, based on the triangulation of the information collected, allowed evaluating and determining the strengths and weaknesses of the priority elements of the university curriculum: the graduation profile, the study plan and the evaluation system. The main conclusion was that it is necessary to have clear parameters for specifying the university curriculum, through a referential model that allows the development of a virtuous circle of evaluation and continuous improvement of curricular planning.Keywords: -competencies; curriculum planning; consistency; discharge profile; Curriculum; evaluation systemINTRODUCTIONThe Bologna agreement signed by the European Union led to great transformations in the training processes of future professionals. One of these was that the university curriculum presents competencies to develop for the exercise of a certain career 18 . According to the Tuning Project for Latin America 45 , competencies are classified by their basic, transversal or specific nature. Basic skills allow people to function as individuals who are part of society and support the development of more complex skillsof analysis, synthesis, understanding and action, thanks to the cognitive skills of information processing, argumentation and interpretation 44 accompanied by of central aspects.Transversal, generic or soft skills are common to different professions, and increase performance expertise, employability, management and productivity in different work environments 8,14, 19,26,44 . The specific competencies are those specific to each profession, and establish the performance expected in each of the professional disciplines, which promote specialization, thanks to the development of specific training processes 2, 20, 39,44 .Another transformation was the management of curricular planningso that all the elements of the curriculum (profiles, objectives, competencies, contents, didactic strategies and evaluation strategies) converge harmoniously and, thus, achieve the graduation profile 11,38 . The design of a curricular planning by competencies must start by identifying the challenges and needs of each profession, this with the aim of contributing to the solution of the latent problems that society faces, for which the competencies to be trained for a suitable performance. All this with theaim of guaranteeing the articulation between the training proposal and the set of demands on the profession 6 .Based on the above, curricular planning is defined in a competency-based approach as the design process of each of the central components of the curriculum, taking into account the educational model of the university, which defines the fundamental orientations of training, as well as the environment of the profession, its demands and development trends.From this perspective, a Curriculum Planning Model for Competencies -hereinafter MPCC -becomes the reference for the construction, organization and readjustment of the competency-based training curriculum, which contains the description of the stages and processes that guarantee consistency, coherence, relevance and gradualness of the training process, likewise, it articulates the macro, meso and micro stages of curricular planning where the structure of each of its components is taken into account 3,24,25,41,50 .The fundamental elements of the MPCC are the graduate profile, the study plan and the evaluation system. The graduation profile is made up of the set of generic and specific competencies for performance in a certain profession, identified after