How to win friends and influence people.
https://link.springer.com/chapter/10.1007/978-3-030-43754-1_3
Privacy and Tracking
In order for ubiquitous computing to realize its full potential, it is necessary for service providers to detect the presence of user devices to identify the needs and wishes of the associated users—both as this relates to the performance of services and the implementation of privacy preferences. We take a large step in the direction of improving service performance by introducing an approach to tie a person’s online behavior (e.g., as represented by her HTML cookies) with her physical behavior (e.g., location and brick-and-mortar purchases). This enables insights from web browsing to be applied to in-store sales (e.g., using coupons), and data related to user location and behavior to be used to improve the understanding of the user’s online needs. We also show how to tie a user profile to a user communications channel, which enables messaging (e.g., alerts, reminders and coupons) that is tied to the detection of user actions, whether on- or offline. We show how to combine this advance in profiling capabilities with a practically manageable approach to enhance end-user privacy. One aspect of this is an extension of the domain of observations to which users can (or can deny to) grant permissions, e.g., allowing individual users a practical method to determine under what circumstances facial recognition can be used for personalization purposes. Our approach is backwards compatible with existing consumer devices; can be rolled out gradually; and is designed with attention given to the usability of the resulting system.
Is this article suggesting that mere humans can’t understand Facebook?
https://academic.oup.com/jeclap/advance-article-abstract/doi/10.1093/jeclap/lpaa077/5940770
The Use of Artificial Intelligence in the Future of Competition Law Enforcement
In a future in which the evidential underpinnings for initiating competition law investigations could begin to shift towards a reliance upon the use of artificial intelligence, this paper outlines some of the key opportunities and risks for businesses and regulators.
In October 2017, the European Commission (the Commission) published a consultancy tender seeking advice on how artificial intelligence (AI) could improve its processes of evidence management, legal drafting and—as is the focus of this paper—its market intelligence gathering.1 More recently, in its AI White Paper of February 2020, the Commission again expressed an intention to understand how AI can equip ‘law enforcement authorities with appropriate tools’.2
Looking at faces changes how we look at faces…
Facial recognition technologies and the new physiognomic era
The current overproduction of images of faces in digital photographs and videos, and the widespread use of facial recognition technologies have important effects on the way we understand ourselves and others. This is because facial recognition technologies create new circulation pathways of images that transform portraits and photographs into material for potential personal identification. In other words, different types of images of faces become available to the scrutiny of facial recognition technologies. In these new circulation pathways, images are continually shared between many different actors who use (or abuse) them for different purposes. Besides this distribution of images, the categorization practices involved in the development and use of facial recognition systems reinvigorate physiognomic assumptions and judgments (e.g., about beauty, race, dangerousness). They constitute the framework through which faces are interpreted. This paper shows that, because of this procedure, facial recognition technologies introduce new and far-reaching »facialization« processes, which reiterate old discriminatory practices.
A sure fire discussion stimulus for my classes. When should your car kill you?
Machine Ethics and Automated Vehicles
Road vehicle travel at a reasonable speed involves some risk, even when using computer-controlled driving with failure-free hardware and perfect sensing. A fully-automated vehicle must continuously decide how to allocate this risk without a human driver’s oversight. These are ethical decisions, particularly in instances where an automated vehicle cannot avoid crashing. In this chapter, I introduce the concept of moral behavior for an automated vehicle, argue the need for research in this area through responses to anticipated critiques, and discuss relevant applications from machine ethics and moral modeling research.
Resources.
https://www.makeuseof.com/4-unique-ways-to-get-datasets-for-your-machine-learning-project/
4 Unique Ways to Get Datasets for Your Machine Learning Project
No comments:
Post a Comment