Made by Humans: The AI Condition

by Ellen Broad

An Australian data expert challenges the idea of AI being better than humans

Who is designing AI? A select, narrow group. How is their world view shaping our future?

Artificial intelligence can be all too human: quick to judge, capable of error, vulnerable to bias. It's made by humans, after all. Humans make decisions about the laws and standards, the tools, the ethics in this new world. Who benefits. Who gets hurt.

Made by Humans explores our role and responsibilities in automation. Roaming from Australia to the UK and the US, elite data expert Ellen Broad talks to world leaders in AI about what we need to do next. It is a personal, thought-provoking examination of humans as data and humans as the designers of systems that are meant to help us.

Genres: Nonfiction, Artificial Intelligence, Technology, Australia

196 pages, Paperback

Published July 30, 2018

Reviews

[Ali · Rating 4 out of 5]

This is a solid primer on the issues swirling around ethics and AI. In a style more understated than most tech writers, Broad gets at some of the central issues here : that we trust technology more than human judgement; that human intervention allows for nuance lost in unmerited automated systems; that a lot of tech is bad, and terrible software dev cultures are a large part of why. She focuses less on racism and structural power, but there are many authors including Safiya Umoja Noble, Alondra Nelson and Cathy O'Neil which should be read. For Australians, Broad's book also has much relevant local content, including a great analysis of the RoboDebt issue. In short: a really good starting place on the topic.

[kari · Rating 3 out of 5]

I hoped for some more complexity, but as an introduction, it's good and well-written.

[Bear Reads Books · Rating 3 out of 5]

Good but not outstanding. Seems to reach it's point early and does not say anything more after that.

[Nicolas · Rating 5 out of 5]

Made by Humans presents a really engaging tour through some of the most important challenges facing human societies in the immediate future. Massive increases in data collection, storage, and processing infrastructure mean that AI will augment decisionmaking, automate routine work, and radically transform many sectors of society. Ellen Broad's book masterfully provides an introduction to the opportunities and challenges of AI and the best thinking about how we might respond. I thoroughly recommend this book to anyone looking for an accessible, interesting, and thought-provoking overview of AI and the changes it brings that we will all need to think about.

[Kathy Reid · Rating 5 out of 5]

Ellen Broad’s multi-faceted exploration of the many inter-twined aspects of artificial intelligence embarks and concludes at the same salient juncture; emerging technologies are conceived, shaped, used, governed and iterated by humans. Just as humans are inherently neither good nor bad, the systems we construct echo our moral plurality, our unconscious bias, and, frequently, our unwillingness to be critically interrogated.

That this is Broad’s first book – given its well-researched examples, coherent structure and intellectual incisiveness – is surprising. Its clarion call – for greater care, more rigourous thinking and a more holistic approach to the almost-infantile adoption of artificial intelligence, machine learning and autonomous decision-making – is not.

Structured in three distinct parts – Humans as Data, Humans as Designers and Making Humans Accountable, the book covers much territory. From systemic and cultural biases in how data used by machine learning is selected and captured, to the errors that are introduced to data sets by humans, to decisions made about system tradeoffs, what privacy means in different contexts, how open a system is to inspection and intelligibility, how diverse that system is, to who is accountable for the impacts of a system, real life examples are interwoven with provocative and often confronting questions.

Broad does not set out – at least in this tome – to answer these questions – rather, she lays a foundation for examining each of these questions in more depth. Personally I’d like to see a follow-up to this that covers attempts to standardise practices in machine learning and artificial intelligence – the frameworks and benchmarks – often competing – that have been proposed – alongside efforts at industry adoption and (likely) the barriers that are faced.

[Leticia Mooney · Rating 4 out of 5]

I am not going to write a long and detailed consideration of this book, but it isn't because I don't want to. It's because I have just finished it and feel like it concluded far too rapidly, as though it was written to a deadline and not to completion. However, I will suggest that this is a necessary read for anyone in the technology space. I often see people talk about machine learning as though it is a neutral tool. People talk about AI as though it is "ready to roll". This book proves that it is anything but that.
Ellen Broad has lifted the shades off of a difficult, scary, and optimistic world. I agree that humans may come to value slowness. But in the meantime I feel like we need to tread carefully. Very, very carefully.

[Natasha Hurley-Walker · Rating 3 out of 5]

A clear and concise examination of the issues around ethics in AI development today, but somewhat limited in scope to existing examples. I personally would have a liked a little more thought about future possibilities, as per Superintelligence: Paths, Dangers, Strategies. But the near future is the hardest thing to predict, so perhaps it's best for readers to draw their own conclusions! The resulting suggestions around ethics in AI / software development were laudible; perhaps a forward-thinking government like the EU will use them as a top down mandate for future development.

[Jeanette · Rating 5 out of 5]

This book should be on everyone's reading list. It is an easy to read book that puts Artificial Intelligence and the data sets, decisions and algorithms used into lay-person terms. It also reminds those who are in technology about the responsibility of over-automation and how it can quickly reestablish inherent bias.

If you are curious about AI or not really sure what it means to you, this is a great book to get started. Written in a non-intimidating and straight-forward way, this book covers a lot of ground.

[Vicky · Rating 5 out of 5]

There are so many views, expectations and warnings on AI and how it will change our life. This book is different, it shows that we are shaping the future themselves, creating prejudice, bias and one-sided input into the new universe of technology. Machine learning is changing everything we understand and comprehend about life and humanity. But we are still responsible for the direction it will go. Algorithms already employ us, diagnose our health and create our profiles for different purposes. The future is unpredictable.

[Penny Jones · Rating 4 out of 5]

As a reader with no expertise in this area at all I found this very accessible. It is well written with many memorable stories and illustrations mostly from the USA, UK and Australia . The dangers as well as the opportunities of automation are explored and some of the ethical challenges presented by the need for regulation. I did not understand everything but I understood far more than I thought I might and it inspired me to find out more.

[Keith Dodds · Rating 4 out of 5]

A very good first book by Australian AI writer Ellen Broad. Ellen's paints a broad picture of how human bias, frailties and weaknesses are reflecting in the algorithms and use of data in Artificial Intelligence. A former leader of the Open Data Institute and a prominent spokesperson on the emerging AI landscape and the ethical considerations required.

Highly recommended.

[Germaine Irwin · Rating 2 out of 5]

Maybe 2.5 since it does provide some great examples of the issues with machine learning. However, I don’t think it goes far enough to discuss how we need to address the growth of AI and not forget how humans are so much more complicated than an algorithm.

[Lucy Hawthorne · Rating 4 out of 5]

Topical and easy to read. Broad uses a wide range of really interesting examples to illustrate how AI isn't quite as 'neutral' as we've been lead to believe. With the pace of technological change in this area, I expect this book will date relatively quickly (so read it now!).

[Kristen · Rating 5 out of 5]

A great introduction to the pitfalls and perils of AI and how can be just as biased as any human

[Aranka · Rating 2 out of 5]

The discussions in this book are very shallow and introductory, lacking the intellectual challenges that I seek.

I think current AI systems can be viewed as an extension of human way of thinking, duly reflecting our own prejudices and fallibility. Instead of being cautious about AI, we might be better off trying to let humans make better judgements and be more disciplined.

[Kathy Reid · Rating 5 out of 5]

Ellen Broad's multi-faceted exploration of the many inter-twined aspects of artificial intelligence embarks and concludes at the same salient juncture; emerging technologies are conceived, shaped, used, governed and iterated by humans. Just as humans are inherently neither good nor bad, the systems we construct echo our moral plurality, our unconscious bias, and, frequently, our unwillingness to be critically interrogated.

That this is Broad's first book - given its well-researched examples, coherent structure and intellectual incisiveness - is surprising. Its clarion call - for greater care, more rigourous thinking and a more holistic approach to the almost-infantile adoption of artifical intelligence, machine learning and autonomous decision-making - is not.

Structured in three distinct parts - Humans as Data, Humans as Designers and Making Humans Accountable, the book covers much territory. From systemic and cultural biases in how data used by machine learning is selected and captured, to the errors that are introduced to data sets by humans, to decisions made about system tradeoffs, what privacy means in different contexts, how open a system is to inspection and intelligibility, how diverse that system is, to who is accountable for the impacts of a system, real life examples are interwoven with provocative and often confronting questions.

Broad does not set out - at least in this tome - to answer these questions - rather, she lays a foundation for examining each of these questions in more depth. Personally I'd like to see a follow-up to this that covers attempts to standardise practices in machine learning and artificial intelligence - the frameworks and benchmarks - often competing - that have been proposed - alongside efforts at industry adoption and (likely) the barriers that are faced.

[Daniel Boos · Rating 4 out of 5]

Ellen Broad fasst sehr gut die aktuelle Diskussion rund um "schwache künstliche Intelligenz" aus einer gesellschaftspolitischen Perspektive zusammen und schlägt Ansätze zum verantwortungsvollen Umgang damit vor. Themen sind u.a Privacy, Bias/Diskriminierung. Ihre Ansätze fokussieren auf Verbesserung der Nachvollziehbarkeit, erhöhter Einbezug diverser Akteure und Perspektiven, sowie die Festlegung von Verantwortlichkeiten für beteiligte Akteure (z.B. Politik, Unternehmen, Entwickler, Nutzende). Spannend sind gerade auch ihre persönlichen Erfahrungen als Aktivistin für netzpolitische Anliegen. Das Buch empfehle ich Personen die sich für einen Einstieg in die aktuelle netzpolitische Debatte interessieren.