scholarly journals A Survey on Methods and Metrics for the Assessment of Explainability Under the Proposed AI Act

2021 ◽  
Author(s):  
Francesco Sovrano ◽  
Salvatore Sapienza ◽  
Monica Palmirani ◽  
Fabio Vitali
Keyword(s):  
Artificial Intelligence ◽  
Significant Role ◽  
Interdisciplinary Approach ◽  
Philosophical Concept ◽  
Design Systems

This study discusses the interplay between metrics used to measure the explainability of the AI systems and the proposed EU Artificial Intelligence Act. A standardisation process is ongoing: several entities (e.g. ISO) and scholars are discussing how to design systems that are compliant with the forthcoming Act and explainability metrics play a significant role. This study identifies the requirements that such a metric should possess to ease compliance with the AI Act. It does so according to an interdisciplinary approach, i.e. by departing from the philosophical concept of explainability and discussing some metrics proposed by scholars and standardisation entities through the lenses of the explainability obligations set by the proposed AI Act. Our analysis proposes that metrics to measure the kind of explainability endorsed by the proposed AI Act shall be risk-focused, model-agnostic, goal-aware, intelligible & accessible. This is why we discuss the extent to which these requirements are met by the metrics currently under discussion.

Press “A” for Artificial Intelligence in Agriculture: A Review

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Yogesh Awasthi
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Significant Role ◽  
Pattern Analysis ◽  
Cost Effective ◽  
Predictive Analysis ◽  
Accurate Information ◽  
Digital Era ◽  
Learning Techniques ◽  
Crucial Information

Agriculture is the backbone of the developing country. In old era agriculture was based on the experience which was shared by people to people but in this digital era technology play a very important and significant role in agriculture. Now agriculture become a business hub therefore farmers are focusing on precision farming. They introduced the technology in agriculture to define the accurate information about seed, soil, weather, disease and all factors which affecting the farming. Artificial Intelligence uses predictive analysis, image analysis, learning techniques and Pattern analysis to declare the best cost effective and maximum gain for the agriculturist. The aim of this paper is to provide the crucial information with the help of technology which a farmers can use to harvest the variety of crops as per the demand in world so that they can get maximum benefits.

scholarly journals Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

2021 ◽  
Author(s):  
◽  
Hazel Darney
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gender Bias ◽  
Relevant Literature ◽  
Interdisciplinary Approach ◽  
High Stakes ◽  
Daily Lives ◽  
Science Field ◽  
Gender Biases ◽  
Free Machine

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men.    Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human.   The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>

scholarly journals Questions from a Contraceptive Pill Junkie: Applying Human Psychometrics to Investigate Gender Bias in Machine Learning

2021 ◽  
Author(s):  
◽  
Hazel Darney
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gender Bias ◽  
Relevant Literature ◽  
Interdisciplinary Approach ◽  
High Stakes ◽  
Daily Lives ◽  
Science Field ◽  
Gender Biases ◽  
Free Machine

<p>With the rapid uptake of machine learning artificial intelligence in our daily lives, we are beginning to realise the risks involved in implementing this technology in high-stakes decision making. This risk is due to machine learning decisions being based in human-curated datasets, meaning these decisions are not bias-free. Machine learning datasets put women at a disadvantage due to factors including (but not limited to) historical exclusion of women in data collection, research, and design; as well as the low participation of women in artificial intelligence fields. These factors mean that applications of machine learning may fail to treat the needs and experiences of women as equal to those of men.    Research into understanding gender biases in machine learning frequently occurs within the computer science field. This has frequently resulted in research where bias is inconsistently defined, and proposed techniques do not engage with relevant literature outside of the artificial intelligence field. This research proposes a novel, interdisciplinary approach to the measurement and validation of gender biases in machine learning. This approach translates methods of human-based gender bias measurement in psychology, forming a gender bias questionnaire for use on a machine rather than a human.   The final output system of this research as a proof of concept demonstrates the potential for a new approach to gender bias investigation. This system takes advantage of the qualitative nature of language to provide a new way of understanding gender data biases by outputting both quantitative and qualitative results. These results can then be meaningfully translated into their real-world implications.</p>

scholarly journals Artificial Intelligence and Bank Soundness: A Done Deal? - Part 1

2021 ◽  
Author(s):  
Charmele Ayadurai ◽  
Sina Joneidy
Keyword(s):  
Artificial Intelligence ◽  
Significant Role ◽  
Crucial Role ◽  
Significant Contribution ◽  
Economic Prosperity ◽  
Bank Stability ◽  
The Face ◽  
Main Strand ◽  
Bank Soundness ◽  
The Way

Banks soundness plays a crucial role in determining economic prosperity. As such, banks are under intense scrutiny to make wise decisions that enhances bank stability. Artificial Intelligence (AI) plays a significant role in changing the way banks operate and service their customers. Banks are becoming more modern and relevant in people’s life as a result. The most significant contribution of AI is it provides a lifeline for bank’s survival. The chapter provides a taxonomy of bank soundness in the face of AI through the lens of CAMELS where C (Capital), A(Asset), M(Management), E(Earnings), L(Liquidity), S(Sensitivity). The taxonomy partitions opportunities from the main strand of CAMELS into distinct categories of 1 (C), 6(A), 17(M), 16 (E), 3(L), 6(S). It is highly evident that banks will soon extinct if they do not embed AI into their operations. As such, AI is a done deal for banks. Yet will AI contribute to bank soundness remains to be seen.

9. Delivering legal services: practitioners, adjudicators, and legal scholars

2018 ◽  
Author(s):  
Martin Partington
Keyword(s):  
Artificial Intelligence ◽  
Significant Role ◽  
Legal System ◽  
Private Colleges ◽  
Legal Education ◽  
Legal Profession ◽  
Legal Services ◽  
Legal Practice ◽  
Legal Advice ◽  
The Law

This chapter discusses the role both of those professionally qualified to practise law—solicitors and barristers—and of other groups who provide legal/advice services but who do not have professional legal qualifications. It examines how regulation of legal services providers is changing. It notes new forms of legal practice. It also considers how use of artificial intelligence may change the ways in which legal services are delivered. It reflects on the adjudicators and other dispute resolvers who play a significant role in the working of the legal system. It reflects on the contribution to legal education made by law teachers, in universities and in private colleges, to the formation of the legal profession and to the practice of the law.

scholarly journals Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System

2019 ◽  
Vol 11 (13) ◽  
pp. 1528 ◽  
Author(s):  
Sébastien Valade ◽  
Andreas Ley ◽  
Francesco Massimetti ◽  
Olivier D’Hondt ◽  
Marco Laiolo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Surface Deformation ◽  
Morphological Changes ◽  
Interdisciplinary Approach ◽  
Volcanic Hazards ◽  
Short Wave ◽  
Volcano Monitoring ◽  
Magma Intrusion ◽  
Earthquake Catalogues ◽  
Multiple Sensors

Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

Ian Glynn, An anatomy of thought: The origin and machinery of the mind. New York: Oxford University Press, 1999. Pp. vi, 456. Hb $35.

2002 ◽  
Vol 31 (4) ◽  
pp. 613-616
Author(s):  
Ronald Gray
Keyword(s):  
Artificial Intelligence ◽  
New York ◽  
Interdisciplinary Approach ◽  
Oxford University ◽  
The Mind ◽  
The Brain ◽  
Oxford University Press

In this highly ambitious book, Glynn attempts to provide a description of both how the brain works and how it has developed. Taking an interdisciplinary approach (he is a physiologist by training), he relies on insights from a wide number of disciplines, including psychology, neurology, anthropology, linguistics, artificial intelligence, psychiatry, physiology, and even philosophy. He is interested in providing answers to some perennial and interconnected questions that relate to the mind: “What kind of thing is mind? What is the relation between our minds and our bodies and, more specifically, what is the relation between what goes on in our minds, and what goes on in our brains? How did brains and minds originate? Can our brains be regarded as nothing more than exceedingly complicated machines? Can minds exist without brains” (p. 4). Although his arguments are rather technical, the book is intended for a nonscientist audience.

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