11 Mar When do you personally stop using a product, a service, or something that brings you convenience, after you have learned that it has seriously harmed people along the way as it was being constructed?? 2) What message do we give to the next generation when we ‘let it go’ and find reasons why our lack of direct involvement justifies the use of the product/service, etc. What are the material consequences of letting things go? 3) What is the biblical response to acquiescing to something when it is wrong, even when you are not directly involved in the wrongdoing? 4) What were the surprises you found in the PEF AI 2017 report? 5) What overall conclusions do you draw from this? 600 words, APAPEFAIreportSeptember2017WEB.pdf
Article – https://time.com/6247678/openai-chatgpt-kenya-workers/
The article on Kenya was selected here to exemplify how important it is to question, investigate, understand, and acknowledge a level of acquiescence that the users of a product or a service exhibit towards the irreparable damage done to people in the name of progress and convenience. This could be said of any product and any service out there that has become ubiquitous in society today. Please address the following:
1) When do you personally stop using a product, a service, or something that brings you convenience, after you have learned that it has seriously harmed people along the way as it was being constructed?
2) What message do we give to the next generation when we 'let it go' and find reasons why our lack of direct involvement justifies the use of the product/service, etc. What are the material consequences of letting things go?
3) What is the biblical response to acquiescing to something when it is wrong, even when you are not directly involved in the wrongdoing?
4) What were the surprises you found in the PEF AI 2017 report?
5) What overall conclusions do you draw from this?
600 words, APA
Artificial Intelligence: Practice and Implications for Journalism
Mark Hansen* Meritxell Roca-Sales** Jon Keegan** George King** * Brown Institute for Media Innovation ** Tow Center for Digital Journalism
Platforms and Publishers: Policy Exchange Forum I June 13, 2017 | Columbia Journalism School
Organized by the Tow Center for Digital Journalism and the Brown Institute for Media Innovation
Artificial Intelligence: Practice and Implications for Journalism 1
Executive Summary 2
Discussion I: Al in the Newsroom 7
Case Studies: ‘A Spectrum of Autonomy’ 8
Challenges for Publishers: Large Newsrooms and Small 9
Discussion II: Technology 10
Automation and Personalization of Stories 10
Commenting Systems and Audience Engagement 12
Proprietary Versus Open Algorithms 13
Challenges and Limitations 13
Discussion III: Algorithms and Ethics 14
Transparency and Accountability 14
Editorial Decisions and Bias 15
Ethical Use of Data 16
Concluding Remarks 17
The Policy Exchange Forums are a critical component of the Tow Center’s Platforms and Publishers research project. In these sessions, participants representing both the platforms and publishing sides of the news industry can engage on issues related to the ethical and civic values of journalism. The forum focuses on the relationships between technology, business, journalism, and ethics, and brings together diverse stakeholders to discuss current issues and surface potential new ones. The project is underwritten by the John D. and Catherine T. MacArthur Foundation, with additional support by the John S. and James L. Knight Foundation, the Foundation to Promote Open Society, and The Abrams Foundation.
Artificial Intelligence: Practice and Implications for Journalism 2
Executive Summary The increasing presence of artificial intelligence and automated technology is changing journalism. While the term artificial intelligence dates back to the 1950s, and has since acquired several meanings, there is a general consensus around the nature of AI as the theory and development of computer systems able to perform tasks normally requiring human intelligence. Since many of the AI tools journalists are now using come from other disciplines—computer science, statistics, and engineering, for example—they tend to be general purpose. Now that journalists are using AI in the newsroom, what must they know about these technologies, and what must technologists know about journalistic standards when building them? On June 13, 2017, the Tow Center for Digital Journalism and the Brown Institute for Media Innovation convened a policy exchange forum of technologists and journalists to consider how artificial intelligence is impacting newsrooms and how it can be better adapted to the field of journalism. The gathering explored questions like: How can journalists use AI to assist the reporting process? Which newsroom roles might AI replace? What are some areas of AI that news organizations have yet to capitalize on? Will AI eventually be a part of the presentation of every news story? Findings
– AI tools can help journalists tell new kinds of stories that were previously too resource-impractical or technically out of reach. While AI may transform the journalism profession, it will enhance, rather than replace, journalists’ work. In fact, for AI to be used properly, it is essential that humans stay in the loop.
– There is both a knowledge gap and communication gap between technologists designing AI and journalists using it that may lead to journalistic malpractice.
– Readers deserve to be given a transparent methodology of how AI tools were used to perform an analysis, identify a pattern, or report a finding in a story.
– While the intersection of AI and data offers new kinds of opportunities for reader engagement, monetization, and news feed personalization, with this comes the challenge of finding a balance between creating echo chambers and remaining committed to journalism’s public service mission.
– Ethical use and disclosure of data (how information from users is collected, stored, used, analyzed, and shared) is a fundamental issue that journalists need to confront.
Artificial Intelligence: Practice and Implications for Journalism 3
– The potential for AI to augment the work of the human data journalist holds great promise, but open access to data remains a challenge.
- Artificial intelligence is unpredictable; we don’t feel that confident predicting where the biggest problems will crop up. Vigilance on the part of both technologists and journalists is necessary to keep these systems in check.
– Investment in training editors and reporters is crucial. As AI tools enter newsrooms, journalists need to understand how to use new resources for storytelling—not only ethically, but also efficiently.
– Developing and promoting the use of shared guidelines among journalists and technologists around ethical use of data and public disclosure of methodology is a must. Existing AI tools, like chatbots and commenting systems, should be used as opportunities for thinking about how to apply editorial values and standards to the early stages of new journalistic-specific technology.
– For custom-built AI, which is too expensive for smaller operations to afford, newsrooms should consider investing time in partnerships with academic institutions.
– There needs to be a concerted and continued effort to fight hidden bias in AI, often unacknowledged but always present, since tools are programmed by humans. Journalists must strive to insert transparency into their stories, noting in familiar and non-technical terms how AI was used to help their reporting or production.
Artificial Intelligence: Practice and Implications for Journalism 4
Introduction By Mark Hansen, director of Columbia’s Brown Institute for Media Innovation Our conversation at June’s forum began where these discussions often do: with the idea that we can enhance human ability through computation. Our specific focus was on journalism and tasks associated with reporting, writing, and designing impactful visualizations and other journalistic “experiences.” First and foremost, computation, as a tool, extends our ability to perform basic calculations—that’s the old magic of spreadsheets and the success of computer-assisted reporting. But advances in computation also bring the ability to recognize new data types, new digital objects that are open to computational techniques of analysis. And with new data types come new kinds of questions about the world around us. More and more of our world is being rendered in digital data, so that (in journalistic terms) our data sources are becoming more diverse—and the information we can draw from them, deeper and more interesting. It almost begs for a kind of aesthetic that prizes new computational voices in the same way we value a new human source with a unique perspective on a story. To ground what we mean by “enhancing our abilities” and the shift to new data types, let’s consider how standard journalistic practice has changed when it comes to wading through piles of documents, perhaps returned by a FOIA request. With machine learning, we can pore over thousands upon thousands of documents in a kind of mechanistic reading. “Reading” at this scale was not possible a couple decades ago, not without a lot of human effort. Now, instead of taking in text line-by-line and word-by-word—as you may now be doing with this text—machine learning, or more specifically Natural Language Processing, helps us to create summaries of texts or divides them into groups with common features (called clusters). Italo Calvino provides a simplified view of this in If on a Winter’s Night a Traveler. A character from the book named Ludmilla explains that she has a computer program that reduces a text to individual words and their frequencies. From here, she can much more easily “read”:
What is the reading of the text, in fact, except the recording of certain thematic re-occurrences, certain insistences of forms and meanings? In a novel of fifty to a hundred thousand words . . . I advise you to observe immediately the words that are repeated about twenty times. Look here . . .
blood, cartridge belt, commander, do, have, immediately, it, life, seen, sentry, shots, spider, teeth, together, you . . .
Artificial Intelligence: Practice and Implications for Journalism 5
Don’t you already have a clear idea what it’s about? With computation, we extend our abilities to “read” thousands or millions of documents. (Franco Moretti at Stanford formalizes this difference, contrasting “distant,” or machine-mediated reading, with “close,” or line-by-line, reading.) These new abilities, however, necessarily change how we think about collections of documents and the knowledge we pull from them—our abilities extend, but also our perspective changes. As with text sources, digital images, audio, and video are also all now open to computation. In the same way, ou
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