How Algorithms Create and Prevent Fake News (Record no. 367731)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04005 a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250927134940.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250924b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781484275696 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Giansiracusa N. |
| 9 (RLIN) | 213019 |
| 245 ## - TITLE STATEMENT | |
| Title | How Algorithms Create and Prevent Fake News |
| Remainder of title | Exploring Impacts social Media , Deepfakes, GPT 3 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Apress |
| Date of publication, distribution, etc. | 2021 |
| Place of publication, distribution, etc. | USA |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 235pp. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | "It's a joy to read a book by a mathematician who knows how to write. [...] There is no better guide to the strategies and stakes of this battle for the future."<br/><br/>---Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank. <br/><br/>“By explaining the flaws and foibles of everything from Google search to QAnon—and by providing level-headed evaluations of efforts to fix them—Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media.”<br/><br/>—Jonathan Rauch, senior fellow at the Brookings Institute and contributing editor of The Atlantic<br/><br/>From deepfakes to GPT-3, deep learning is now powering a new assault on our ability to tell what’s real and what’s not, bringing a whole new algorithmic side to fake news. On the other hand, remarkable methods are being developed to help automate fact-checking and the detection of fake news and doctored media. Success in the modern business world requires you to understand these algorithmic currents, and to recognize the strengths, limits, and impacts of deep learning---especially when it comes to discerning the truth and differentiating fact from fiction. <br/><br/>This book tells the stories of this algorithmic battle for the truth and how it impacts individuals and society at large. In doing so, it weaves together the human stories and what’s at stake here, a simplified technical background on how these algorithms work, and an accessible survey of the research literature exploring these various topics.<br/><br/><br/>How Algorithms Create and Prevent Fake News is an accessible, broad account of the various ways that data-driven algorithms have been distorting reality and rendering the truth harder to grasp. From news aggregators to Google searches to YouTube recommendations to Facebook news feeds, the way we obtain information todayis filtered through the lens of tech giant algorithms. The way data is collected, labelled, and stored has a big impact on the machine learning algorithms that are trained on it, and this is a main source of algorithmic bias – which gets amplified in harmful data feedback loops. Don’t be afraid: with this book you’ll see the remedies and technical solutions that are being applied to oppose these harmful trends. There is hope.<br/><br/>What You Will Learn<br/><br/>The ways that data labeling and storage impact machine learning and how feedback loops can occur<br/>The history and inner-workings of YouTube’s recommendation algorithm<br/>The state-of-the-art capabilities of AI-powered text generation (GPT-3) and video synthesis/doctoring (deepfakes) and how these technologies have been used so far<br/>The algorithmic tools available to help with automated fact-checking and truth-detection<br/>Who This Book is For<br/><br/>People who don’t have a technical background (in data, computers, etc.) but who would like to learn how algorithms impact society; business leaders who want to know the powers and perils of relying on artificial intelligence. A secondary audience is people with a technical background who want to explore the larger social and societal impact of their work.<br/>Table of contents <br/><br/>Crafted by Computer<br/>Deepfake Deception<br/>Autoplay the Autocrats<br/>Prevarication and the Polygraph<br/>Gravitating to Google<br/>Avarice of Advertising<br/>Social Spread<br/>Tools for Truth<br/> |
| 650 10 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Fake news |
| 9 (RLIN) | 213283 |
| 650 20 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | deepfakes |
| 9 (RLIN) | 213284 |
| 650 30 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | text generation |
| 9 (RLIN) | 213285 |
| 650 40 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | algorithmic bias |
| 9 (RLIN) | 213286 |
| 650 50 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | recommendation algorithms |
| 9 (RLIN) | 213287 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Vendor | Cost, normal purchase price | Total Checkouts | Barcode | Date last seen | Cost, replacement price | Bill Date | Koha item type | Original Barcode |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | MCA | MKSSS s K.B. Joshi Institute of Information Technology Library | MKSSS s K.B. Joshi Institute of Information Technology Library | 11/09/2025 | 87 | 599.25 | KBJP-BK-2803 | 11/09/2025 | 799.00 | 11/09/2025 | Books | 2803 |
