A way to monetize ‘fake news?’ There is a security fix – print a one-time review ‘password’ on the check. (So much for privacy…)
https://ny.eater.com/2022/7/8/23200007/one-star-review-scam-extortion-nyc-restaurants
Scammers Are Threatening NYC Restaurants With 1-Star Reviews in Exchange for Online Gift Cards — Um, What?
An extortion scam affecting restaurant owners across the country has touched down in New York City. Restaurants including Avant Garden in the East Village, Dame in Greenwich Village, and Huertas in the East Village are among the first to be hit by the online scammers who are threatening to leave one-star reviews on restaurants’ business pages until their owners hand over gift cards to Google’s app store.
(Related)
Upscale SF Restaurants Targeted by Cookie-Cutter Bad Reviews From Online Trolls
Review aggregation websites like Yelp! and OpenTable have a massive impact on small businesses, like bars and eateries. It's been estimated that just an extra half-star rating increase can cause restaurants to sell out 19% more frequently; conversely, a full-star rating drop can be financially devastating to an eatery, especially when they're left on baseless accounts.
A simple question? Disclosure may trigger bias?
AI Ethics: When does an AI voice agent need to disclose itself as an AI agent?
There is an ongoing debate in the field of artificial intelligence (AI) about when, or even if, AI agents should reveal themselves as such to humans. The research investigates business policy and principles and academic research into when an AI agent needs to disclose itself to the end-user when might not be aware they are interacting with an AI agent. The research finds key situations and conditions when an AI agent needs to disclose itself to the end-user. Moreover, the investigation outlines the gap between the business and academic world towards AI disclosure to the human.
(Related)
https://link.springer.com/chapter/10.1007/978-3-030-95346-1_128
Humanizing the Terminator: Artificial Intelligence Trends in the Customer Journey: An Abstract
Current use of artificial intelligence (AI) in marketing is to assist and empower consumers or a human workforce. While AI is not yet replacing humans (Chen et al. 2019; Davenport et al. 2020), it is transforming many industries (Huang and Rust 2018; Rust 2020; Wirth 2018). Whether consumers recognize it or not, AI is already embedded into many aspects of today’s customer journey. In this process, tradeoffs between data privacy, AI driven technology, and resulting benefits have blurred and at times, been accepted by consumers via social complacency. There is evidence that this tradeoff can create a feeling of cognitive dissonance within some users of AI.
The theory of cognitive dissonance proposes that when a person has two inconsistent thoughts, beliefs, attitudes, or actions, dissonance (mental distress) will occur (Festinger 1957). Dissonance is uncomfortable, and thus people will seek to resolve that discomfort through various strategies, such as creating an explanation that allows the inconsistency to exist or rejecting new information that is in conflict with existing beliefs (Festinger 1964). Research by Levin et al. (2010) supports that cognitive dissonance is increased in human-robot interactions as compared to human-human interactions for similar purposes.
Much of the existing research has examined perceptions and behaviors of those aware of an AI-based interaction, not those who may be interacting with AI unknowingly. The purpose of this research is to explore the differences in attitudes and behaviors of consumers when they are and are not aware of the existence of AI and how cognitive dissonance may play a role in their AI interactions. This study will employ a mixed-methods approach consisting of a consumer survey and interviews to better understand this phenomena.
Criminals are people. AI is a criminal. Therefore, AI is a people?
https://elib.sfu-kras.ru/handle/2311/147462
Criminal Liability for Actions of Artificial Intelligence: Approach of Russia and China
In the Era of Artificial intelligence (AI) it is necessary not only to define precisely in the national legislation the extent of protection of personal information and limits of its rational use by other people, to improve data algorithms and to create ethics committee to control risks, but also to establish precise liability (including criminal liability) for violations, related to AI agents. According to existed criminal law of Russia and criminal law of the People’s Republic of China AI crimes can be divided into three types:
crimes, which can be regulated with existed criminal laws;
crimes, which are regulated inadequately with existed criminal laws;
crimes, which cannot be regulated with existed criminal laws.
Solution of the problem of criminal liability for AI crimes should depend on capacity of the AI agent to influence on ability of a human to understand public danger of committing action and to guide his activity or omission. If a machine integrates with an individual, but it doesn’t influence on his ability to recognize or to make decisions. In this case an individual is liable to be prosecuted. If a machine influences partially on human ability to recognize or to make decisions. In this case engineers, designers and units of combination should be prosecuted according to principle of relatively strict liability. In case, when AI machine integrates with an individual and controls his ability to recognize or to make decisions, an individual should be released from criminal prosecution
Relentless surveillance…
https://www.pogowasright.org/why-privacy-matters-a-conversation-with-neil-richards/
Why Privacy Matters: A Conversation with Neil Richards
Julia Angwin writes:
Hello, friends,
In the wake of the Supreme Court’s jaw-dropping ruling overturning constitutional protections for abortion in the United States, there’s been a lot of discussion about how to keep data about pregnant people private.
Google announced, for instance, that it would remove sensitive locations, such as abortion clinics, from the location data it stores about users of its Android phones. Many people—including me in this newsletter —worried about whether they or their loved ones should delete their period-tracking apps.
But as Vox reporter Sara Morrison wisely observed, “[D]eleting a period tracker app is like taking a teaspoon of water out of the ocean.” So much data is collected about people these days that removing a small amount of data from an app or a phone is not going to erase all traces of a newly criminalized activity.
The Electronic Frontier Foundation notes that pregnant people are ar more likely to be turned over to law enforcement by hospital staff, a partner or a family member than by data in an app —and that the types of digital evidence used to indict people are often text messages, emails, and web search queries.
So how do you protect yourself in a world of relentless surveillance? This seems like a good time to go back to the basics and understand what privacy is and why we seek it. Because it’s not just people fearing arrest who need it, but all of us.
And so this week, I turned to an expert on this topic, Neil Richards, Koch Distinguished Professor in Law at Washington University, in St. Louis. Richards is the author of two seminal privacy books: “Why Privacy Matters” (Oxford Press, 2022) and “Intellectual Privacy” (Oxford Press, 2015). He also serves on the board of the Future of Privacy Forum and the Electronic Privacy Information Center and is a member of the American Law Institute. He served as a law clerk to William H. Rehnquist, former chief justice of the Supreme Court.
Read Julia’s conversation with Neil Richards at The Markup.
A completely acceptable use for facial recognition?
https://futurism.com/the-byte/smart-pet-door-facial-recognition
SMART PET DOOR USES FACIAL RECOGNITION TO KEEP STRANGE ANIMALS OUT
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Principles for Facial Recognition Technology: A Content Analysis of Ethical Guidance
Ethical issues are a significant challenge for the facial recognition technology industry and the users of this technology. In response to these issues, private sector, government, and civil society groups created ethical guidance documents. The primary purpose of this qualitative content analysis study was to identify common ethical recommendations in these facial recognition technology ethical guidance documents. The research questions explored within this study included: What are the common recommendations in facial recognition ethical guidance; are there certain ethical recommendations that are more prevalent; and are there differences between recommendations from governments, the private sector, or other organizational groups? The scope of the study was limited to ethical guidance documents published within the United States or published by international groups that included representation from the United States. Using a qualitative content analysis research methodology with an inductive research design for theme development, eight themes were identified representing the common recommendations in facial recognition technology ethical guidance documents. The eight thematic categories of common recommendations were privacy, responsibility, accuracy and performance, accountability, transparency, lawful use, fairness, and purpose limitation. The research findings can inform ethical debates and might further the development of ethical norms within the industry. The findings also have significant implications for practice, providing organizations with a deeper understanding of the most common recommendations across all organizational groups and knowledge of differences between organizational groups. Thus, where there might be an opportunity for organizations to demonstrate ethical leadership.
I read a lot of science fiction, therefore I know what might be possible with the right AI. Anyone got a few million to invest in my start-up?
Why business is booming for military AI startups
Exactly two weeks after Russia invaded Ukraine in February, Alexander Karp, the CEO of data analytics company Palantir, made his pitch to European leaders. With war on their doorstep, Europeans ought to modernize their arsenals with Silicon Valley’s help, he argued in an open letter.
For Europe to “remain strong enough to defeat the threat of foreign occupation,” Karp wrote, countries need to embrace “the relationship between technology and the state, between disruptive companies that seek to dislodge the grip of entrenched contractors and the federal government ministries with funding.”
Militaries are responding to the call. NATO announced on June 30 that it is creating a $1 billion innovation fund that will invest in early-stage startups and venture capital funds developing “priority” technologies such as artificial intelligence, big-data processing, and automation.
Since the war started, the UK has launched a new AI strategy specifically for defense, and the Germans have earmarked just under half a billion for research and artificial intelligence within a $100 billion cash injection to the military.
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