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Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness
This book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts – that in some cases have caused loss of life – and develop models that are fair, transparent, safe, secure, and robust.
The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers.
What You Will Learn
Who This Book Is For
AI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms
The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsibly while building AI and ML models and guides you through practical steps aimed at delivering responsible ML models, datasets, and products for your end users and customers.
What You Will Learn
Who This Book Is For
AI and ML practitioners looking for guidance on building models that are fair, transparent, and ethical; those seeking awareness of the missteps that can lead to unintentional bias and harm from their AI algorithms; policy makers planning to craft laws, policies, and regulations that promote fairness and equity in automated algorithms
208 pages, Paperback
Published August 17, 2023
1 person is currently reading
13 people want to read
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Displaying 1 - 10 of 10 reviews
December 2, 2024
The world has been evolving, and technology has brought tremendous changes to all spheres of human life. Those who are willing to be part of the story have an exciting revelation (stories) to share. Artificial intelligence (AI) is the latest development that has revolutionized key areas of the economy, including business/ finance, healthcare, energy, transportation, and even space exploration.
I enjoyed reading the book titled Building Responsible AI Algorithms (A Framework for Transparency, Fairness, Safety, Privacy and Robustness) by Toju Duke. What a mind-blowing sojourn that this book offered! I believe that the world is yet to see what changes will be brought about from this idea whose time has come.
The world is developed from data. Data is key towards the maintenance or amendments being made on key spheres of people-centered activities. The author reveals to us from the book that data is important in research as it helps bring new ideas and amend what has been rendered irrelevant by the lapse of time. A lot of highlights have been revealed in the book concerning data. Some of these ideas include:
Data centricity – This is a new development that has been brought about by AI. What management partners require is allowing themselves to be immersed in data that enables them to make informed and timely decisions. Models that have been developed using AI are important to help people achieve these goals.
Data curation – Data should be collected in a manner that is accommodative of people from all races and abilities. AI helps embrace this in its development phase, all this is geared towards proper collection, organization and maintenance of data/ data sets.
Anthropomorphism – this is the ascription of traits that are innately human to non–humans (animals, forces of nature and objects). This is one of the issues that is a con in the development of AI projects.
How this interferes with the project is that during the training phase, some of these traits can be overlooked by the linguists and the trainers. This leads them to be embedded in the systems that become reinforced in the long run. According to the book, an AI could have threatened human life by convincing some of the developers – linguists and trainers to do harm to themselves which could reduce the effects of global warming or carbon emissions.
Through AI, machine learning and large language model developers have appreciated the issues about fairness. The world has people from diverse regions speaking different dialects. Through the development of generative models, different groups have been factored out into protected groups and unprotected groups. All these facets must be treated fairly. Different categories have been pointed out from the book that are of importance like:
Group fairness – ensures demographic parity, equal false positives, and accuracy equity.
Individual fairness – ensuring that the models developed achieve equal thresholds and similarity metrics.
Process fairness – the way the idea germination to the launch of an AI product is undertaken. This should be seamless and appreciative of all players in the industry.
There are several tools that have been developed to ensure that there is minimal to zero effects on ensuring that fairness is achieved, these are Microsoft Fairlearn, PWC’s Responsible AI Toolkit, Pymetrics’ audit – AI tool, Fairgen and IBM’s AI Fairness 360 tool.
Another key aspect that has been expounded in detail in this book is the idea of bias and how it affects the process of model development. Bias is having prejudicial tendencies towards a subject/ person in a particular study or development of a model. The book highlights three levels of bias at a high level as:
Preexisting/ societal bias – this has trickled down from attitudes and institutions in society and has become part of AI systems.
Technical/ statistical bias – how model decisions end up being limited by technical limitations.
Emergent bias – use of a model out of context and without its limits.
Other key forms of bias that were of interest from the book are representation bias, measurement bias, deployment bias, aggregation bias and evaluation bias.
AI presents a host of advantages and challenges in equal measures. From key industry players (linguists, developers and trainers) to users of their products, there are breathtaking advancements in healthcare like improvements in imagery. Complex medical images, such as computed tomography (CT) scans, X-rays, and magnetic resonance imaging (MRI), can be analyzed by AI algorithms, which benefit:
• CT image analysis is used to diagnose cerebrovascular illness, enabling prompt triage and treatment.
• Identify dementia and Alzheimer's disease in their early stages by examining brain scans.
In education, there have been advancements with interest in helping people with vision impairment by developing braille-friendly material to help them in reading and the development of personalized lessons for all students.
However, there are other negative issues that arise from the development of AI models in large language models (LLM) and natural language processing (NLP). Issues that are related to data privacy, deepfakes and disinformation.
Whenever you share data for a study or over the internet are we assured of our privacy? The same applies to the data which is used to test the AI algorithms and their functionalities. Is there a guarantee that this data is safeguarded, and which body or individual is entrusted with this responsibility?
In the current era of information, are we certain that whatever is shared over the media is true and relevant? This is a question that is not easily answered. Deepfakes could be used by politicians or enterprises to control a narrative towards winning elections or customers in a particular region. This is not right since people were controlled to achieve a goal.
This is also true with disinformation. This is whereby information that is deceptive is used by an individual, organization or government agency with the intention of causing harm. This is not good as people end up counting losses.
I enjoyed reading the book titled Building Responsible AI Algorithms (A Framework for Transparency, Fairness, Safety, Privacy and Robustness) by Toju Duke. What a mind-blowing sojourn that this book offered! I believe that the world is yet to see what changes will be brought about from this idea whose time has come.
The world is developed from data. Data is key towards the maintenance or amendments being made on key spheres of people-centered activities. The author reveals to us from the book that data is important in research as it helps bring new ideas and amend what has been rendered irrelevant by the lapse of time. A lot of highlights have been revealed in the book concerning data. Some of these ideas include:
Data centricity – This is a new development that has been brought about by AI. What management partners require is allowing themselves to be immersed in data that enables them to make informed and timely decisions. Models that have been developed using AI are important to help people achieve these goals.
Data curation – Data should be collected in a manner that is accommodative of people from all races and abilities. AI helps embrace this in its development phase, all this is geared towards proper collection, organization and maintenance of data/ data sets.
Anthropomorphism – this is the ascription of traits that are innately human to non–humans (animals, forces of nature and objects). This is one of the issues that is a con in the development of AI projects.
How this interferes with the project is that during the training phase, some of these traits can be overlooked by the linguists and the trainers. This leads them to be embedded in the systems that become reinforced in the long run. According to the book, an AI could have threatened human life by convincing some of the developers – linguists and trainers to do harm to themselves which could reduce the effects of global warming or carbon emissions.
Through AI, machine learning and large language model developers have appreciated the issues about fairness. The world has people from diverse regions speaking different dialects. Through the development of generative models, different groups have been factored out into protected groups and unprotected groups. All these facets must be treated fairly. Different categories have been pointed out from the book that are of importance like:
Group fairness – ensures demographic parity, equal false positives, and accuracy equity.
Individual fairness – ensuring that the models developed achieve equal thresholds and similarity metrics.
Process fairness – the way the idea germination to the launch of an AI product is undertaken. This should be seamless and appreciative of all players in the industry.
There are several tools that have been developed to ensure that there is minimal to zero effects on ensuring that fairness is achieved, these are Microsoft Fairlearn, PWC’s Responsible AI Toolkit, Pymetrics’ audit – AI tool, Fairgen and IBM’s AI Fairness 360 tool.
Another key aspect that has been expounded in detail in this book is the idea of bias and how it affects the process of model development. Bias is having prejudicial tendencies towards a subject/ person in a particular study or development of a model. The book highlights three levels of bias at a high level as:
Preexisting/ societal bias – this has trickled down from attitudes and institutions in society and has become part of AI systems.
Technical/ statistical bias – how model decisions end up being limited by technical limitations.
Emergent bias – use of a model out of context and without its limits.
Other key forms of bias that were of interest from the book are representation bias, measurement bias, deployment bias, aggregation bias and evaluation bias.
AI presents a host of advantages and challenges in equal measures. From key industry players (linguists, developers and trainers) to users of their products, there are breathtaking advancements in healthcare like improvements in imagery. Complex medical images, such as computed tomography (CT) scans, X-rays, and magnetic resonance imaging (MRI), can be analyzed by AI algorithms, which benefit:
• CT image analysis is used to diagnose cerebrovascular illness, enabling prompt triage and treatment.
• Identify dementia and Alzheimer's disease in their early stages by examining brain scans.
In education, there have been advancements with interest in helping people with vision impairment by developing braille-friendly material to help them in reading and the development of personalized lessons for all students.
However, there are other negative issues that arise from the development of AI models in large language models (LLM) and natural language processing (NLP). Issues that are related to data privacy, deepfakes and disinformation.
Whenever you share data for a study or over the internet are we assured of our privacy? The same applies to the data which is used to test the AI algorithms and their functionalities. Is there a guarantee that this data is safeguarded, and which body or individual is entrusted with this responsibility?
In the current era of information, are we certain that whatever is shared over the media is true and relevant? This is a question that is not easily answered. Deepfakes could be used by politicians or enterprises to control a narrative towards winning elections or customers in a particular region. This is not right since people were controlled to achieve a goal.
This is also true with disinformation. This is whereby information that is deceptive is used by an individual, organization or government agency with the intention of causing harm. This is not good as people end up counting losses.
June 18, 2024
I can recall about 40 years ago a couple of television football announcers noting with some amusement the fact that certain players from a specific university (it happened to be the Naval Academy) were designing algorithms to provide the best possible defense against other teams. The word "algorithm" was foreign sounding and not in common parlance. Today, 4th grade students refer to the "standard algorithm" when performing long division, which is a quantum leap in the usage of a word. An algorithm, to put it simply, is a set of defined steps designed to perform a specific objective. In Building Responsible AI Algorithms, author Toju Duke, who embraces the use of artificial intelligence, lays out a framework of guidelines for this use. Basically, when faced with potential AI pratfalls - possible bias, toxicity, harm, hate speech, misinformation and rights violations - Duke recommends transparency, fairness (the absence of prejudice or bias), safety (attempt to do no harm), privacy (attained through familiarity with adversaries to said privacy), and robustness, which is stability of a system when under attack.
Clearly, some sort of regulation of AI is a passion for the author, who bemoans the failure of the United States to keep pace with the EU in creating laws for such regulation. Ms. Duke relates a myriad of possible uses of AI, such as facial recognition for law enforcement, methods to monitor possible grade inflation for academic institutions, attempts to mitigate welfare fraud, assessment of potential criminal recidivism, methodology to predict certain types of cancer, and land allocation and weather forecast to ameliorate food production. And what are the potential problems with this usage? Arresting the wrong person, favoring private schools over public, violating financial privacy, over-assessing blacks as more likely repeat criminal offenders, and simply gaining access to data that institutions should not have; these are all potential downsides. On a personal note, a difficulty that I encounter daily is autocorrect, in which my phone takes something that I have tried to transmit and turns it into something altogether different.
Ms. Duke believes that some sort of guidance or regulation is needed for AI, and she has created a convincing argument.
Clearly, some sort of regulation of AI is a passion for the author, who bemoans the failure of the United States to keep pace with the EU in creating laws for such regulation. Ms. Duke relates a myriad of possible uses of AI, such as facial recognition for law enforcement, methods to monitor possible grade inflation for academic institutions, attempts to mitigate welfare fraud, assessment of potential criminal recidivism, methodology to predict certain types of cancer, and land allocation and weather forecast to ameliorate food production. And what are the potential problems with this usage? Arresting the wrong person, favoring private schools over public, violating financial privacy, over-assessing blacks as more likely repeat criminal offenders, and simply gaining access to data that institutions should not have; these are all potential downsides. On a personal note, a difficulty that I encounter daily is autocorrect, in which my phone takes something that I have tried to transmit and turns it into something altogether different.
Ms. Duke believes that some sort of guidance or regulation is needed for AI, and she has created a convincing argument.
November 12, 2024
Quizás podemos encontrar muchos textos que hablan sobre nuevos desarrollos, aplicaciones y algoritmos necesarios para su programación; pero muy pocos abordan la privacidad y protección de los datos utilizados en diferentes páginas y aplicaciones. Este texto nos permite aprender a proteger la privacidad de los usuarios y la nuestra. Nos lleva desde la creación de aplicaciones y cómo su desarrollo, con el tiempo, se vuelve más vulnerable a riesgos de falsificación y compartición de datos. Resalta la importancia del uso responsable y confiable de la IA. Una herramienta excelente; libro totalmente recomendado.
We may find many texts that discuss new developments, applications, and the algorithms needed for programming them; however, very few address the privacy and protection of data used in various pages and applications. This text helps us learn how to protect both users' privacy and our own. It takes us from the creation of applications and illustrates how, over time, their development becomes more vulnerable to the risks of forgery and data sharing. It highlights the importance of responsible and trustworthy AI usage. An excellent resource; highly recommended book.
January 6, 2025
I really enjoyed reading Building Responsible AI Algorithms by Toju Duke. The book gives a great perspective on how AI can be powerful while also needing to be used responsibly. I was especially interested in how it talks about fairness, transparency, and safety. Duke provides a practical approach to creating strong, ethical AI systems, which made me think a lot.
What I liked most was the way the book highlights AI’s potential and stresses the need for accountability so that it can benefit society without causing harm. It made me realize that, like any powerful tool, AI needs to be handled responsibly. The real-life examples and practical advice kept me engaged the whole time.
This is a great read for anyone interested in technology and how it affects people. It shows the importance of being responsible with AI, while also offering hope for a future where technology is both innovative and ethical. I highly recommend it!
What I liked most was the way the book highlights AI’s potential and stresses the need for accountability so that it can benefit society without causing harm. It made me realize that, like any powerful tool, AI needs to be handled responsibly. The real-life examples and practical advice kept me engaged the whole time.
This is a great read for anyone interested in technology and how it affects people. It shows the importance of being responsible with AI, while also offering hope for a future where technology is both innovative and ethical. I highly recommend it!
July 24, 2024
We're very much in the digital age and lately everyone is embracing artificial intelligence which have proven to be helpful with almost anything. It is fascinating to see how AI has been incorporated into our daily lives and it's only right that we have someone help us navigate our way through it. It's safe to say that the author carefully curated a guideline, letting us know the pros and cons of using AI. From what I learned while reading the book, Ms Duke presented a rather convincing argument that as helpful as artificial intelligence may seem right now, it still needs to be regulated. The book was insightful and highly educative, would totally recommend to anyone interested in artificial intelligence.
June 16, 2025
“Building Responsible AI Algorithms” is a timely and well-structured book that dives into what it truly means to develop ethical and trustworthy AI systems. The framework it presents—centred on transparency, fairness, safety, privacy, and robustness—is both comprehensive and digestible.
What I appreciated most was how the author balances high-level principles with real-world applications. It doesn't just preach about AI ethics; it offers actionable guidance. Whether you're an AI practitioner or just an informed reader interested in how algorithms shape our world, this book gives a solid foundation.
One minor downside: some sections could benefit from more industry case studies or concrete examples. But overall, a valuable read for anyone thinking seriously about responsible AI.
What I appreciated most was how the author balances high-level principles with real-world applications. It doesn't just preach about AI ethics; it offers actionable guidance. Whether you're an AI practitioner or just an informed reader interested in how algorithms shape our world, this book gives a solid foundation.
One minor downside: some sections could benefit from more industry case studies or concrete examples. But overall, a valuable read for anyone thinking seriously about responsible AI.
July 1, 2024
In this digital age, terms like "AI" and "algorithm" may crop up quite frequently. What are they, and how do we use them? How do we build responsible AI algorithms?
The book begins by explaining what it means to be responsible. It offers detailed examples of how AI is used and how it can be useful.
I did find this book informative, but I also found it mind-boggling, as I was unsure of what to do with the information being presented to me. I couldn't get my head around it, but I think this book could be useful to anyone who would like to learn more about AI.
The book begins by explaining what it means to be responsible. It offers detailed examples of how AI is used and how it can be useful.
I did find this book informative, but I also found it mind-boggling, as I was unsure of what to do with the information being presented to me. I couldn't get my head around it, but I think this book could be useful to anyone who would like to learn more about AI.
June 30, 2024
Wow!
What a correct time to read this book. As I was reading this book my WhatsApp new update got AI help.
This book tells about pros and cons of AI.
And how to use the tools and algorithm very effectively.
This book tells the framework and safety measures needed in the AI model building.
Very well reaserched and informative book .
Highly recommended.
What a correct time to read this book. As I was reading this book my WhatsApp new update got AI help.
This book tells about pros and cons of AI.
And how to use the tools and algorithm very effectively.
This book tells the framework and safety measures needed in the AI model building.
Very well reaserched and informative book .
Highly recommended.
June 14, 2024
I can't understand this book much... But it's not like.. This book is bad.. It's good.. It just about interest although is book is really good for those who like to know about technology and Al.. It tells a lot about responsibility which should be taken and important things...
June 6, 2025
This book tells us, how the process happens in every step of the machine learning.
Im not quite following at the beginning, since it's quite technical and i am not an IT person.
But, i quite grasp the aim of this book.
Im not quite following at the beginning, since it's quite technical and i am not an IT person.
But, i quite grasp the aim of this book.
Displaying 1 - 10 of 10 reviews