Leseprobe
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The AI Dilemma
Art Kleiner, Juliette Powell
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Sozialwissenschaften, Recht, Wirtschaft / Wirtschaft
Beschreibung
The misuse of AI has led to wrongful arrests, denial of medical care, even genocide-this book offers 7 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.
Kundenbewertungen
machine learning, data, surveillance, data rights, artificial intelligence, A.I., social justice, activism, Algorithm, responsible a.i, community, a.i. systems, technology, data ethics, algorithmic bias, automation, risk management, accountability