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A biometric data privacy win in court is followed by a related FTC investigation and lawsuit

#artificialintelligence

Executives at facial recognition firm Clarifai may have sighed with relief in March 2021 when a judge agreed that they could not be sued for violating Illinois' biometric privacy law, but then the federal government came knocking. The Federal Trade Commission now wants to know how the face image that a woman posted on the OkCupid dating site ended up being used as training data by Clarifai without her consent or disclosing the transaction as required by Illinois' Biometric Information Privacy Act. Clarifai makes computer vision, deep learning AI and biometrics systems. Claiming that its investigation is being stonewalled by Match Group, owner of OkCupid, the FTC has filed suit (case number 1:22-mc-00054), according to Bloomberg Law. The government claims that OkCupid engaged in unfair and deceptive conduct by sharing biometric data with Clarifai in 2014.


Automated Detection of Doxing on Twitter

arXiv.org Artificial Intelligence

The term"dox" is an abbreviation for"documents," and doxing is the act of disclosing private, sensitive, or personally identifiable information about a person without their consent. Sensitive information can be considered as any type of confidential information or any information that can be used to identify a person uniquely. This information is called doxed information and includes demographic information [53] such as birthday, sexual orientation, race, ethnicity, and religion, or location information which can be used to precisely or approximately locate a person such as the street address, ZIP code, IP address, and GPS coordinates. Other categories of doxed information are identity documents like passport number and social security number, contact information like phone number and email address, financial information such as credit card and bank account details, or sign-in credentials such as usernames and passwords[15]. Such disclosure may have various consequences. It may encourage forms of bigotry and hate groups, encourage human or child trafficking and endanger people's lives or reputations, scare and intimidate people by swatting


Technology Ethics in Action: Critical and Interdisciplinary Perspectives

arXiv.org Artificial Intelligence

This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.


Fair ranking: a critical review, challenges, and future directions

arXiv.org Artificial Intelligence

Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large "fair ranking" research literature has been developed around making these systems fair to the individuals, providers, or content that are being ranked. Most of this literature defines fairness for a single instance of retrieval, or as a simple additive notion for multiple instances of retrievals over time. This work provides a critical overview of this literature, detailing the often context-specific concerns that such an approach misses: the gap between high ranking placements and true provider utility, spillovers and compounding effects over time, induced strategic incentives, and the effect of statistical uncertainty. We then provide a path forward for a more holistic and impact-oriented fair ranking research agenda, including methodological lessons from other fields and the role of the broader stakeholder community in overcoming data bottlenecks and designing effective regulatory environments.


When Creators Meet the Metaverse: A Survey on Computational Arts

arXiv.org Artificial Intelligence

The metaverse, enormous virtual-physical cyberspace, has brought unprecedented opportunities for artists to blend every corner of our physical surroundings with digital creativity. This article conducts a comprehensive survey on computational arts, in which seven critical topics are relevant to the metaverse, describing novel artworks in blended virtual-physical realities. The topics first cover the building elements for the metaverse, e.g., virtual scenes and characters, auditory, textual elements. Next, several remarkable types of novel creations in the expanded horizons of metaverse cyberspace have been reflected, such as immersive arts, robotic arts, and other user-centric approaches fuelling contemporary creative outputs. Finally, we propose several research agendas: democratising computational arts, digital privacy, and safety for metaverse artists, ownership recognition for digital artworks, technological challenges, and so on. The survey also serves as introductory material for artists and metaverse technologists to begin creations in the realm of surrealistic cyberspace.


How Facebook Undermines Privacy Protections for Its 2 Billion WhatsApp Users

#artificialintelligence

ProPublica is a nonprofit newsroom that investigates abuses of power. Sign up to receive our biggest stories as soon as they're published. When Mark Zuckerberg unveiled a new "privacy-focused vision" for Facebook in March 2019, he cited the company's global messaging service, WhatsApp, as a model. Acknowledging that "we don't currently have a strong reputation for building privacy protective services," the Facebook CEO wrote that "I believe the future of communication will increasingly shift to private, encrypted services where people can be confident what they say to each other stays secure and their messages and content won't stick around forever. This is the future I hope we will help bring about. We plan to build this the way we've developed WhatsApp." Zuckerberg's vision centered on WhatsApp's signature feature, which he said the company was planning to apply to Instagram and Facebook Messenger: end-to-end encryption, which converts all messages into an unreadable format that is only unlocked when they reach their intended destinations. WhatsApp messages are so secure, he said, that nobody else -- not even the company -- can read a word. As Zuckerberg had put it earlier, in testimony to the U.S. Senate in 2018, "We don't see any of the content in WhatsApp." If you like our stories, mind sharing this with a friend? For more ways to keep up, be sure to check out the rest of our newsletters.


Towards Personalized and Human-in-the-Loop Document Summarization

arXiv.org Artificial Intelligence

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.


The Top 100 Software Companies of 2021

#artificialintelligence

The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...


TikTok Has Started Collecting Your 'Faceprints' and 'Voiceprints.' Here's What It Could Do With Them

TIME - Tech

Recently, TikTok made a change to its U.S. privacy policy, allowing the company to "automatically" collect new types of biometric data, including what it describes as "faceprints" and "voiceprints." TikTok's unclear intent, the permanence of the biometric data and potential future uses for it have caused concern among experts who say users' security and privacy could be at risk. On June 2, TikTok updated the "Information we collect automatically" portion of its privacy policy to include a new section called "Image and Audio Information," giving itself permission to gather certain physical and behavioral characteristics from its users' content. The increasingly popular video sharing app may now collect biometric information such as "faceprints and voiceprints," but the update doesn't define these terms or what the company plans to do with the data. "Generally speaking, these policy changes are very concerning," Douglas Cuthbertson, a partner in Lieff Cabraser's Privacy & Cybersecurity practice group, tells TIME.


Tiktok's new privacy policy lets it harvest biometric data, including 'faceprints and voiceprints'

Daily Mail - Science & tech

TikTok quietly changed its US privacy policy this week to notify users it may start collecting'faceprint and voiceprint' and other biometric data. The app did not specify what the data would be used for but said it would ask for permission first, 'where required by law.' The update comes just three months after TikTok paid more than $90 million to settle a class-action lawsuit claiming it secretly recorded millions of members' facial features and other biomarkers. TikTok reportedly has 100 million users in the US alone. TikTok has updated its privacy policy to notify US users it may record the'faceprint and voiceprint' and other unique biometric data.