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Ethical Machines

Your Concise Guide to Totally Unbiased, Transparent, and Respectful AI

Reid Blackman

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ca. 32,99
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Harvard Business Review Press img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / Informatik, EDV

Beschreibung

What will you do when your AI misbehaves?

The promise of artificial intelligence is automated decision-making at scale, but that means AI also automates risk at scale. Are you prepared for that risk?

Already, many companies have suffered real damage when their algorithms led to discriminatory, privacy-invading, and even deadly outcomes. Self-driving cars have hit pedestrians; HR algorithms have precluded women from job searches; mortgage systems have denied loans to qualified minorities. And often the companies who deployed the AI couldn't explain why the black box made the decision it did.

In this environment, AI ethics isn't merely an academic curiosity, it's a business necessity. In Ethical Machines, Reid Blackman gives you all you need to understand AI ethics as a risk management challenge. He'll help you build, procure, and deploy AI in a way that's not only ethical but also safe in terms of your organization's reputation, regulatory compliance, and legal standing—and do it at scale.

And don't worry—the book's purpose is to get work done, not to ponder deep and existential questions about ethics and technology. Blackman's clear and accessible writing helps make a complex and often misunderstood concept like ethics easy to grasp. Most importantly, Blackman makes ethics actionable by tackling the big three ethical risks with AI—bias, explainability, and privacy—and tells you what to do (and what not to do) to mitigate them.

With practical approaches to everything from writing a strong statement of AI ethics principles to creating teams that effectively evaluate ethical risks, Ethical Machines is the one guide you need to ensure your AI advances your company's objectives instead of undermining them.

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Schlagwörter

artificial intelligence, AI, machine learning, technology, operations, data, data science, ethics, bias, inequality, transparency, privacy, security, automation, risk, risk management, risk mitigation, management, leadership, reputation management, automated decision making