AI is a way for us to understand and grasp knowledge, information and put data into context. It is being used globally to make all kinds of decisions that directly impact our lives and yet most understand very little about it. We start with the definition that data is an artifact of human experience. We need the widest variance of humans, irrespective of your role and skillset, developing AI so that everyone's story is a part of the models we are building. Ergo, this book is for you!
The reader will explore a conceptual model for data and understand what humans are good at and what machines are good at. The book dives into why accountability, fairness, transparency, explainability, kindness, robustness, and data privacy are essential concepts in AI that are not being taught nor insisted upon. Earning trust in AI is not a technical challenge, but socio-technical!
By the end of this book, the reader will be able to detail power structures with respect to AI and understand the changing roles and responsibilities in the AI field. They will also be able to advocate for the culture required to curate AI responsibly and what it means to be a good "parent" to AI.
This book is an essential read for anyone interested in understanding AI and the responsibility that comes with developing and using it.
Below is an excerpt from the book, the last chapter.
There is much we need to unlearn to evolve as better stewards of AI. So we dedicate this book to our sons and daughters and gentle readers so that you can champion the cause.
Chapter 1 — This book is for you: We need a billion more humans literate in AI systems. We need more people involved in the creation of AI. We need you; your story is essential for creating effective AI.
Chapter 2 — A conceptual model for Data: Data is an artifact of human experience, and information, knowledge, and wisdom require context, relationships, and stories.
Chapter 3 — What is AI: AI is a system that simulates human intelligence, and responsible AI aims to augment humans.
Chapter 4 — Stories that keep us up at night: The stories that keep us up at night are nothing compared to our resilience as a species. What divides us pales in comparison to what unites us.
Chapter 5 — The end of opacity: The end of opacity or ambiguity means that we can hold corporations responsible for their impact on individuals and communities for their deployment of AI.
Hold them accountable for ensuring what they deploy aligns with principles for trust and transparency.
Demand that AI systems have frequent auditing by ethically qualified personnel.
Demand that companies take on the burden of the total lifecycle cost of ownership if they reap the benefit. Align the incentives for corporations to take on the entire lifecycle of a product from the planning, development, and production phase through the use and then onto the end of the product.
Chapter 6 — Positive Parenting: Positive reinforcement works far better than negative; incentivize the behaviors you wish to see more of, replicate what works, be curious, and know that we borrow our world from our children.
Chapter 7 — Rules to live by: Models of how to live are all around us, and we can be the model by unlearning and rethinking our accountability.
Chapter 8 — Myth versus Reality: Understanding the myths and truths about AI and who benefits when we perpetuate those myths.
Chapter 9 — Language and Archaeology: Studying language and archaeology can give us the understanding we need to survive current Generative AI models.
Chapter 10 — Cognitive Science and Ontologies: Cognitive Science and Ontologies are different ways to examine AI and information systems, the cure that grows near the cause.
Chapter 11 — Roles and Responsibilities: Roles and responsibilities for responsible AI include many more than are currently being sought after for AI jobs.
Chapter 12 — The culture to curate AI responsibly: Systems of inequality are perpetuated through taking power away or building AI systems that control versus augment humans. The culture required to curate AI responsibly includes a growth mindset, multi-disciplinary teams, and diverse and inclusive leadership.
Chapter 13 — AI Education and Certification NOW: AI Education must start now at all levels, especially K-12. We are 100s of millions of people short of creating AI representative of the human race.
We need more people with diverse thoughts and stories to develop AI. We need people with the imagination to design and build AI to reflect better the communities that AI serves today.
In this book, we have attempted to teach that we need YOU. So please reach out to us. We want to hear from you!
A fellow with the London-based Royal Society of Arts, Boinodiris has focused on inclusion in technology since 1999. She currently leads IBM Consulting’s Trustworthy AI Practice and serves on the leadership team of IBM’s Academy of Technology. Boinodiris is a co-founder of the Future World Alliance, a non-profit dedicated to curating K-12 education in AI ethics. She is pursuing her Ph.D. in AI and Ethics at University College Dublin’s Smart Lab. In 2019, she won the United Nations Woman of Influence in STEM and Inclusivity Award and was recognized by Women in Games International as one of the Top 100 Women in the Games Industry as she began one of the first scholarship programs in the United States for women to pursue degrees in game design and development.
Global executive leader with 20+ years of IT and data science experience. Previously, in her roles as Chief Data Officer, Chief Data Scientist, and Global Talent Transformation Offering Leader, Beth drove transformation for IBM’s clients through the design and delivery of scaled AI systems. She made innovation with analytics and AI into a 2B$ business and intends to show the world that every human can grow their own AI. She is now the CEO and Chairwoman of Bast.ai.