Atlas of AI Paperback Review

Atlas of AI Paperback Review

Table of Contents

Do we ever stop to think about the expansive universe of artificial intelligence and its implications? “Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence” tackles this mesmerizing subject by not just skimming the surface but by examining every crevice and nook.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence     Paperback – Atlas, 11 October 2022

Learn more about the Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence     Paperback – Atlas, 11 October 2022 here.

Overview of the Book

In “Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence,” the author, Kate Crawford, delves into the depths of the artificial intelligence industry. It’s a realm of bewildering possibilities, profound controversies, and unprecedented ethical quandaries. One might assume the book is merely another exegesis waxing technical about AI. Instead, Crawford demystifies and humanizes the subject by combining intricate research with compelling storytelling.

Book Specs

Attribute Details
Title Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence
Author Kate Crawford
Format Paperback
Publication Date 11 October 2022
ISBN Not listed here
Pages Not listed here
Publisher Not listed here

Who Should Read This Book?

This book is for those who appreciate a healthy blend of technology, ethics, and politics. It’s for the curious minds who wonder how AI shapes our world beyond the algorithms and codes. Does AI merely improve our lives, or does it also come with a hidden cost? It’s a mesmerizing journey, one that’s perfect for technophiles and skeptics alike.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence Paperback – Atlas, 11 October 2022

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Power Dynamics in AI

Power imbalances are a recurring theme in Kate Crawford’s profound exploration. One of the standout aspects of “Atlas of AI” is its emphasis on how power is consolidated and exercised throughout the AI industry, thus painting a broader picture of global socio-political landscapes.

Corporate Monoliths

Crawford dives into the colossal influence exerted by tech giants like Google, Facebook, and Amazon. These companies aren’t just technology firms; they’re modern-day power brokers, wielding influence that seeps into nearly every facet of our lives. Their dominance raises questions about the monopolistic control of information and the ethical implications therein.

Government Surveillance

How often do we wonder just how much our governments know about us? “Atlas of AI” makes it clear that AI and machine learning are often tools for surveillance, turning citizens into data points. The future of surveillance outlined in this book is not merely Orwellian; it’s present-day reality juxtaposed with spine-chilling implications.

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The Ethical Quandary

From facial recognition technologies to predictive policing, the ethical considerations are staggering. Crawford takes us on a guided tour of these ethical minefields, encouraging us to question the morality behind AI applications. Are these technologies created for the greater good, or are they thinly veiled instruments of oppression and control?

Bias in Machine Learning

Bias isn’t just a human flaw; it’s one that pervades our machine learning models as well. Crawford delves into the alarming ways in which biases are embedded within AI systems, whether in their data sets or operational protocols. These biases can have real-world repercussions, often perpetuating systemic inequalities

The Dark Side of Data

We might assume data is merely harmless clusters of ones and zeros, but Crawford reveals the dark narrative of data acquisition and utilization. From privacy violations to the commodification of personal information, the book lays bare the often shadowy dealings that fuel the AI industry.

Environmental Toll

The planetary cost of AI is another critical aspect of the book. We often hear about AI’s role in facilitating smart grids, autonomous vehicles, and various sustainable technologies. But at what environmental cost?

Resource Extraction

The mining of rare earth materials needed to manufacture sophisticated AI hardware is extensive and environmentally costly. These materials aren’t just plucked from the Earth with magical ease; they involve labor-intensive and ecologically damaging processes, often in geopolitically unstable regions.

Energy Consumption

The energy demands for running AI systems are gargantuan. Crawford makes it clear that training machine learning models and running AI algorithms are not just computationally but also environmentally expensive. Data centers consume electricity on par with small countries, leaving an enormous carbon footprint.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence     Paperback – Atlas, 11 October 2022

A Call for Regulation

“Atlas of AI” isn’t just a critique; it’s a call to action. Crawford insists on the need for more stringent regulations and ethical guidelines to navigate the rapidly evolving terrain of AI.

Global Policies

The book outlines potential pathways for enacting meaningful regulations. From international treaties to national legislation, there are multiple layers to consider. Crawford advocates for a global consensus on AI ethics—a tall order but a necessary step.

Corporate Responsibility

There’s no sidestepping the fact that corporate responsibility plays a monumental role here. Crawford challenges tech giants to consider the broader implications of their technologies, urging them to move beyond mere profit motives to become stewards of ethical innovation.

Personal Reflections

It’s hard not to reflect on our own relationship with technology after diving into Crawford’s sweeping analysis. Are we mindless consumers of AI-driven convenience, or can we become more conscientious stewards of the digital future? This question looms large as we navigate the ever-shifting waters of technological advancement.

Technology’s Grip on Daily Life

It’s fascinating—and slightly unsettling—how technology has seeped into every pore of our daily existence. From the obvious (smartphones and laptops) to the covert (data-collecting smart appliances), the grip of AI on our lives is tightening. But Crawford doesn’t urge us to shun technology; she encourages us to become more aware and critical of the systems we engage with.

Emotional Resonance

Perhaps what sets this book apart is its emotional resonance. In every chapter, you can almost hear the author’s plea for a more humane approach to technology. It’s not just about scrutinizing the tech and policies; it’s about grasping their human impact, acknowledging the lives touched and sometimes harmed by these systems.

Futuristic Vision

Crawford offers a nuanced view of what the future might hold. It’s neither all doom nor all gloom, but rather a balanced assessment, rich with possibilities and fraught with challenges.

AI for Good

Sure, the book doesn’t paint a rose-tinted picture, but it also acknowledges the positive strides AI can offer. From medical breakthroughs to environmental monitoring, the potential for beneficial applications is vast. However, this future is contingent on strict ethical and regulatory frameworks.

The Path Ahead

Crawford leaves us, readers, with a sense of urgency but also hope. The book acts as both a warning and a guidepost, urging humanity to adopt a more equitable, sustainable, and ethical approach to artificial intelligence. It’s as much about the pitfalls to avoid as it is about the opportunities to seize.

Final Thoughts

Ultimately, “Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence” serves as a clarion call for us to wake up and scrutinize the trajectory of our technological future. It’s an eye-opening read, and while you might not finish it feeling entirely optimistic about the state of AI, you’ll certainly be more informed and hopefully ready to advocate for a smarter, fairer, and more humane approach to technology.

Why It Matters

In a world increasingly driven by data and algorithms, the importance of such a scrutinizing examination cannot be overstated. “Atlas of AI” isn’t just a book; it’s a necessary beacon for anyone interested in the intersection of technology, ethics, and policy. And let’s face it, who among us can afford not to be interested in that?

Feedback, anyone?

Discover more about the Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence     Paperback – Atlas, 11 October 2022.

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About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (= 0.87, < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (= 0.02, = .423). Additionally, CSE (= 0.02, = .423), PC (= 0.05, = .334), ATE (= 0.00, = .983), and PP (= 0.03, = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (= 0.22, < .001) and RES (= 0.21, < .001). The results also indicated that PC was significantly correlated with ATE (= -0.79, < .001), AXY (= 0.18, < .001), and RES (= 0.20, < .001). ATE was significantly correlated with AXY (= -0.19, < .001) and RES (= -0.19, < .001). AXY was significantly correlated with RES (= 0.85, < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, < .001), ATE (F(7,194) = 2.20, = .036), Years computers (F(1,194) = 5.71, = .018), and PC (F(12,194) = 2.00, = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.