I've had a long-time interest in two things: mathematics and social issues. This is why I got degrees in social work (Masters) and sociology (PhD) and eventually focused on the quantitative aspects of these two areas. Social Workers Count gave me the chance to marry these two interests by showing the role mathematics can play in illuminating a number of pressing social issues.
As I write these lines, artificial intelligence (AI) is getting a lot of attention.
This is largely due to ChatGpt recently bursting onto the scene. But even before ChatGpt began making its mark, AI was often in the news. Some have expressed worry that it will take our jobs, others that it will reinforce systemic oppression by making racially or otherwise discriminatory decisions, and some have even voiced concerns that one day a superintelligent AI might pose an existential threat to humanity.
In the midst of all this, what might get lost is what AI is, what it's capable of doing, and what its limitations are. Wenger's book is intended to address all of these questions. It manages to do so in a way which goes into some of the mathematics of AI systems and yet remain accessible to a lay audience.
After laying out the technical aspects of AI, Wenger discusses some of the social, economic, and political issues I opened this section with. For anyone interested in understanding what the most prevalent breed of AI systems are doing, the book is a superb read.
Artificial intelligence is everywhere―it’s in our houses and phones and cars. AI makes decisions about what we should buy, watch, and read, and it won’t be long before AI’s in our hospitals, combing through our records. Maybe soon it will even be deciding who’s innocent, and who goes to jail . . . But most of us don’t understand how AI works. We hardly know what it is. In "Is the Algorithm Plotting Against Us?", AI expert Kenneth Wenger deftly explains the complexity at AI’s heart, demonstrating its potential and exposing its shortfalls. Wenger empowers readers to answer the question―What…
The previous book is about the most prevalent forms of AI, many of which focus on prediction or classification.
For example, a bank may use an AI system that utilizes data about those applying for a loan to predict whether they're likely to default. The judicial system might use an AI model which takes into account a convicted person's attributes in order to predict whether that person is likely to re-offend. A hospital might observe attributes of cells in order to classify them as cancerous or not.
Judea Pearl, a computer scientist at UCLA, has been in a long-running effort to get those working in AI to focus more on designing systems which could engage in causal reasoning. And in doing so, he's had a major influence on a number of disciplines, including computer science, philosophy, statistics, epidemiology, and the social sciences.
In The Book of Why, Pearl teams up with science writer Dana Mackenzie in order to bring his revolutionary ideas to a lay audience. One of the mantras of quantitative disciplines is that "correlation doesn't imply causation." For those interested in what's involved in distinguishing correlation from causation, The Book of Why is a must read.
'Wonderful ... illuminating and fun to read' - Daniel Kahneman, winner of the Nobel Prize and author of Thinking, Fast and Slow
'"Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term "thinking machine"' - Vint Cerf, Chief Internet Evangelist, Google, Inc.
The influential book in how causality revolutionized science and the world, by the pioneer of artificial intelligence
'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking…
Marianne Bohr and her husband, about to turn sixty, are restless for adventure. They decide on an extended, desolate trek across the French island of Corsica — the GR20, Europe’s toughest long-distance footpath — to challenge what it means to grow old. Part travelogue, part buddy story, part memoir, The…
Many quant geeks are familiar with statistics. The dominant school of statistical thought is called "Frequentist" or "Classical."
It focuses on either 1) testing a given hypothesis by determining how likely observed data are on the assumption that the hypothesis is true or 2) constructing intervals for which a certain percentage of them contain the actual value of whatever is being estimated.
A lesser known, although this seems to be changing, school of thought is Bayesian statistics. It focuses on using prior information about some phenomenon in order to revise or update one's beliefs about it.
If you're into stats but don't know much about Bayesian statistics, Donovan and Mickey's book is a great place to start. It's somewhat mathematical but covers the technical aspects much more accessibly that any other book I've seen on the topic.
Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and…
Jun Otsuka, a philosopher who also has training in statistics, zooms in on their philosophical foundations.
His book discusses the metaphysical, epistemological, and semantic assumptions on which Classical statistics, Bayesian statistics, predictive/classification AI models, and causal inference are based.
For those interested in these disciplines but who're also sensitive to the philosophical issues they raise, Otsuka's book is simply amazing. Run out and get a copy as soon as possible.
Simply stated, this book bridges the gap between statistics and philosophy. It does this by delineating the conceptual cores of various statistical methodologies (Bayesian/frequentist statistics, model selection, machine learning, causal inference, etc.) and drawing out their philosophical implications. Portraying statistical inference as an epistemic endeavor to justify hypotheses about a probabilistic model of a given empirical problem, the book explains the role of ontological, semantic, and epistemological assumptions that make such inductive inference possible. From this perspective, various statistical methodologies are characterized by their epistemological nature: Bayesian statistics by internalist epistemology, classical statistics by externalist epistemology, model selection by pragmatist…
My book is a collection of monthly Editor-in-Chief letters to the readership of World Neurosurgery, a journal that I edit. Each essay is short and sweet. The letters were written for neurosurgeons but have been re-edited so that they apply to all human beings. They cover topics such as leadership,…
Many people associate mathematics with calculating things or plugging numbers into formulas to get answers to a multitude of problems.
But this isn't how mathematicians view their discipline. They see mathematics as more about starting with definitions of key mathematical concepts, stating axioms about these concepts, and proving things about them. For those interested in going from calculating and plug and chug mathematics to "real" mathematics, Richard Hammack's book is a terrific place to start.
The book covers a number of topics that cut across all of pure and applied mathematics, topics such as sets, relations, and functions. But the heart of the book is focused on how mathematicians go about proving things. If one wants a glimpse of how mathematicians really work, go out and get this book immediately.
This book is an introduction to the language and standard proof methods of mathematics. It is a bridge from the computational courses (such as calculus or differential equations) that students typically encounter in their first year of college to a more abstract outlook. It lays a foundation for more theoretical courses such as topology, analysis and abstract algebra. Although it may be more meaningful to the student who has had some calculus, there is really no prerequisite other than a measure of mathematical maturity.
Topics include sets, logic, counting, methods of conditional and non-conditional proof, disproof, induction, relations, functions, calculus…
Social work students are often required to take courses in the domain of quantitative literacy, but struggle with the relative inattention to policy and social issues. These courses, as well as the books written for them, may also present mathematical demands many social workers are unprepared to meet. However, issues such as poverty measurement, adjustment of the purchasing power of social welfare benefits, demographic strains on the Social Security program, and probability theory as a means of estimating the likelihood of child abuse or neglect represent only a few of the many quantitative problems related to the concerns of professional social workers.
Social Workers Count provides social workers with the background necessary to engage the quantitative aspects of policy and social issues relevant to social work.
A fast-paced literary thriller with a strong sci-fi element and loaded with existential questions. Beyond the entertainment value, this book takes a hard look at the perilous world of publishing, which is on a crash course to meet the nascent, no-holds-barred world of AI. Could these worlds co-exist, or will…
Five days before the end of humanity, five unlikely heroes find themselves on an impossible quest to outlive the apocalypse.
5 Stars is the survival story of a mother and her baby facing impossible odds amidst a global apocalypse. Set in a dying world overseen by “The Neon God,” the…
