Login

**Farid** · 08-30-2023, 12:56 PM

The AI Dilemma | 225 | Juliette Powell | 2023 | Berrett-Koehler Publishers | 1523004193

Generative content creators. Self-driving vehicles. Predictive analytics. In the right hands, they're beneficial to all. In the wrong hands, they amplify human bias, enable dangerous frauds, and harm vulnerable people.

Artificial intelligence is a mirror to humanity: it is forcing us to confront our worst and become our best. As decision makers and technology users, we need to use it to gain real control - instead of the illusion of control that machine learning often delivers.

Juliette Powell and Art Kleiner offer seven principles for ensuring that AI systems support human flourishing. They draw on Powell's research at Columbia University and use a wealth of real-world examples. Four principles relate to AI systems themselves. Human risk must be rigorously determined and consciously included in any design process. AI systems must be understandable and transparent to any observer, not just the engineers working on them. People must be allowed to protect and manage their personal data. The biases embedded in AI must be confronted and reduced.

The final three principles pertain to the organizations that create AI systems. There must be procedures in place to hold them accountable for negative consequences. Organizations need to be loosely structured so that problems in one area can be isolated and resolved before they spread and sabotage the whole system. Finally, there must be psychological safety and creative friction, so that anyone involved in software development can bring problems to light without fear of reprisal.

Powell and Kleiner explore how to implement each principle, citing current best practices, promising new developments, and sobering cautionary tales. Incorporating the perspectives of engineers, businesspeople, government officials, and social activists, this book will help us realize the unprecedented benefits and opportunities AI systems can provide.

Juliette Powell is an entrepreneur, technologist, and strategist, who works at the intersection of culture, data science, and ethics. Art Kleiner is a writer, editor, and entrepreneur, with a focus on management, scenario thinking, and strategy.
The misuse of AI has led to wrongful arrests, denial of medical care, even genocide-this book offers seven powerful principles that business can use now to end the harm. AI holds incredible promise to improve virtually every aspect of our lives, but we can't ignore its risks, mishaps, and misuses. Juliette Powell and Art Kleiner offer seven principles for ensuring that machine learning supports human flourishing. They draw on Powell's research at Columbia University and use a wealth of real-world examples. Four principles relate to AI systems themselves. Human risk must be rigorously determined and consciously included in any design process. AI systems must be understandable and transparent to any observer, not just the engineers working on them. People must be allowed to protect and manage their personal data.
The biases embedded in AI must be confronted and reduced. The final three principles pertain to the organizations that create AI systems. There must be procedures in place to hold them accountable for negative consequences. Organizations need to be loosely structured so that problems in one area can be isolated and resolved before they spread and sabotage the whole system. Finally, there must be psychological safety and creative friction, so that anyone involved in software development can bring problems to light without fear of reprisal. Powell and Kleiner explore how to implement each principle, citing current best practices, promising new developments, and sobering cautionary tales. Incorporating the perspectives of engineers, businesspeople, government officials, and social activists, this book will help us realize the unprecedented benefits and opportunities AI systems can provide.

Contents of Download:
The AI Dilemma 7 Principles for Responsible Technology.pdf (2.63 MB)

KatFile Link(s)