In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions

Abstract Real engines of the artificial intelligence (AI) revolution, machine learning (ML) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.

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Criticalities and Advantages of the Use of Artificial Intelligence in Research

10.4018/978-1-7998-8473-6.ch011 ◽

2022 ◽

pp. 161-175

Author(s):

Jessica Camargo Molano ◽

Jacopo Cavalaglio Camargo Molano

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Public Schools ◽

School System ◽

Detection System ◽

Rapid Development ◽

Academic Research ◽

The Face ◽

The Impact ◽

Machine Learning Models

In recent years, artficial intelligence, through the rapid development of machine learning and deep learning, has started to be used in different sectors, even in academic research. The objective of this study is a reflection on the possible errors that can occur when the analysis of human behavior and the development of academic research rely on artificial intelligence. To understand what errors artificial intelligence can make more easily, three cases have been analyzed: the use of the IMPACT system for the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington, the face detection system, and the “writing” of the first scientific text by artificial intelligence. In particular, this work takes into consideration the systematic errors due to the polarization of data with which the machine learning models are trained, the absence of feedback and the problem of minorities who cannot be represented through the use of big data.

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Machine Learning and its role in Diverse Business Systems

Research Journal of Science and Technology ◽

10.52711/2349-2988.2021.00033 ◽

2021 ◽

pp. 213-217

Author(s):

Neha Bhateja ◽

Nishu Sethi ◽

Shivangi Kaushal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Statistical Methods ◽

Business Value ◽

Massive Data ◽

Human Intervention ◽

Business Organizations ◽

Business Systems ◽

Rapid Pace

Machine learning as a branch of Artificial Intelligence is growing at a very rapid pace. It has shown significant benefits across a number of different industry verticals in helping them improve their productivity and making them less reliant on humans. The success and the growth of any industry depends on the manageability of massive data, using the data for predictions and deriving business value, automating the processes without the need of human intervention, provide satisfactory services to their clients and the security of client's information. Machine learning is a method that provides a way to transform the processes that leads to growth by using the statistical methods. The focus of this paper is to provide an overview of machine learning and highlight the various areas where machine learning is implemented by the business organizations and industries.

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A quantum machine learning algorithm based on generative models

Science Advances ◽

10.1126/sciadv.aat9004 ◽

2018 ◽

Vol 4 (12) ◽

pp. eaat9004 ◽

Cited By ~ 14

Author(s):

X. Gao ◽

Z.-Y. Zhang ◽

L.-M. Duan

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Quantum Computer ◽

Learning Algorithm ◽

Probability Distributions ◽

Quantum Algorithm ◽

Generative Models ◽

Proposed Model ◽

Quantum Machine Learning ◽

Future Technologies

Quantum computing and artificial intelligence, combined together, may revolutionize future technologies. A significant school of thought regarding artificial intelligence is based on generative models. Here, we propose a general quantum algorithm for machine learning based on a quantum generative model. We prove that our proposed model is more capable of representing probability distributions compared with classical generative models and has exponential speedup in learning and inference at least for some instances if a quantum computer cannot be efficiently simulated classically. Our result opens a new direction for quantum machine learning and offers a remarkable example where a quantum algorithm shows exponential improvement over classical algorithms in an important application field.

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Online Detection and Prediction of Fused Deposition Modelled Parts Using Artificial Intelligence

10.4018/978-1-7998-8516-0.ch009 ◽

2022 ◽

pp. 194-209

Author(s):

Sachin Salunkhe ◽

G. Kanagachidambaresan ◽

C. Rajkumar ◽

K. Jayanthi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Critical Issue ◽

Extrusion Process ◽

Human Interaction ◽

Fused Deposition Modelling ◽

Fused Deposition ◽

Machine Learning Model ◽

Proposed Model ◽

Complex Parts

Fused deposition modelling (FDM) is a technology used for filament deposition of heated plastic filaments by a given pattern by the melted extrusion process. Delamination is a critical issue of FDM's incredibly complex parts. In this chapter, the artificial intelligence (machine learning) model is used for online detections and prediction of FDM parts. The proposed machine learning and convolutional neural network model is capable of online detect delamination of FDM parts. The proposed model can also be applied for different types of additive manufacturing materials with less human interaction.

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How Futures Studies and Foresight Could Address Ethical Dilemmas of Machine Learning and Artificial Intelligence

World Futures Review ◽

10.1177/1946756719894602 ◽

2019 ◽

Vol 12 (2) ◽

pp. 169-180

Author(s):

Alejandro Díaz-Domínguez

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Ethical Issues ◽

Predictive Analytics ◽

Ethical Dilemmas ◽

Futures Studies ◽

Short Term ◽

Ethical Concerns ◽

Time Horizons

Drawing from ethical concerns raised by communities of machine learning developers and considering predictive analytics’ very short-term predictions, several futures studies techniques are examined to offer some insights about possible bridges between machine learning and foresight. This review develops three main sections: (1) a brief explanation of central concepts, such as big data, machine learning, and artificial intelligence, hopefully not too simplistic but readable for larger audiences; (2) a discussion about ethical issues, such as bias, discrimination, and dilemmas in research; and (3) a brief description of how futures studies could address ethical dilemmas derived from different time horizons among machine learning immediate results, forecasting short-term predictions, and foresight long-term scenarios.

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Machine Learning in Agriculture

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i6.713 ◽

2018 ◽

Vol 8 (6) ◽

pp. 26 ◽

Cited By ~ 1

Author(s):

Matthew N. O. Sadiku ◽

Chandra M. M Kotteti ◽

Sarhan M. Musa

Keyword(s):

Climate Change ◽

Artificial Intelligence ◽

Machine Learning ◽

Disease Diagnosis ◽

Automated Detection ◽

Disease Prediction ◽

Paper Briefly ◽

Agriculture Sector ◽

Modern Agriculture ◽

Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data. Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change. Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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Development of innovative hygienic technologies for preservation of the workers’ reproductive health

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-59-9-792-793 ◽

2020 ◽

pp. 792-792

Author(s):

M. A. Fesenko ◽

G. V. Golovaneva ◽

A. V. Miskevich

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Reproductive Health ◽

Reproductive System ◽

Preventive Measures ◽

Learning Algorithms ◽

Reproductive Function ◽

Machine Learning Algorithms ◽

New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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