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The alignment problem: machine learning and human values/ by Brian Christian.

By: Publication details: Atlantic Books, 2020. LondonDescription: xii, 476p. ; 25 cmISBN:
  • 9781786494313
Subject(s): DDC classification:
  • 174.90063 CHR
Contents:
Prophecy. Representation -- Fairness -- Transparency -- Agency. Reinforcement -- Shaping -- Curiosity -- Normativity. Imitation -- Inference -- Uncertainty.
Summary: "A jaw-dropping exploration of everything that goes wrong when we build AI systems-and the movement to fix them. Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill. When systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. In best-selling author Brian Christian's riveting account, we meet the alignment problem's "first-responders," and learn their ambitious plan to solve it before our hands are completely off the wheel"--Provided by publisher.
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Holdings
Item type Current library Call number Status Date due Barcode
Book Book Indian Institute of Management Visakhapatnam 174.90063 CHR (Browse shelf(Opens below)) Checked out 06/30/2024 001646
Book Book Indian Institute of Management Visakhapatnam New Materials Shelf 174.90063 CHR (Browse shelf(Opens below)) Checked out 10/07/2023 001350

Prophecy. Representation --
Fairness --
Transparency --
Agency. Reinforcement --
Shaping --
Curiosity --
Normativity. Imitation --
Inference --
Uncertainty.

"A jaw-dropping exploration of everything that goes wrong when we build AI systems-and the movement to fix them. Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us-and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole-and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill. When systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. In best-selling author Brian Christian's riveting account, we meet the alignment problem's "first-responders," and learn their ambitious plan to solve it before our hands are completely off the wheel"--Provided by publisher.

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