We all started to realize that the rapid development of AI was really going to change the world we live in. AI is no longer just a branch of computer science, it has escaped from research labs with the development of "AI systems", "software that, for human-defined purposes, generates content, predictions, recommendations or decisions influencing the environments with which they interact" (european union definition). The issues of governance of these AI systems – with all the nuances of ethics, control, regulation and regulation – have become crucial, as their development today is in the hands of a few digital empires like them Gafa-Natu-Batx… who have become the masters of real societal choices on automation and on the "rationalization" of the world. The complex fabric intersecting AI, ethics and law is then built in power relations – and connivance – between states and tech giants. But the commitment of citizens becomes necessary, to assert other imperatives than a solutionism technology where "everything that can be connected will be connected and streamlined".

I was open how it was made and put Midjourney on the cover page. It wasn't altered in any other way," Kashtova writes. I registered it as visual arts work. My certificate is in the mail and I got the number and a confirmation today that it was approved. My friend lawyer gave me this idea and I decided to make a precedent."

Machine Learning may push research in precision medicine to unprecedented heights. To succeed, machine learning needs a large amount of data, often including personal data. Therefore, machine learning applied to precision medicine is on a cliff edge: if it does not learn to fly, it will deeply fall down. In this paper, we present Active Informed Consent (AIC) as a novel hybrid legal-technological tool to foster the gathering of a large amount of data for machine learning. We carefully analyzed the compliance of this technological tool to the legal intricacies protecting the privacy of European Citizens.

Autonomously operating learning agents are becoming more common and this trend is likely to continue accelerating for a variety of reasons. First, cheap sensors, actuators, and high-speed wireless internet have drastically lowered the barrier to deploy an autonomous system. Second, autonomy creates the possibility of learning "on device", keeping experience local and off of any central servers. This makes it easier to comply with privacy requirements (Kairouz et al., 2019) and increases robustness by removing a single point of failure. Third, the autonomous approach is a potentially better fit for never-ending life-long learning (Platanios et al., 2019) since it does not require periodic syncing with updated centralized models. Indeed fully autonomous agents do not require any train-test separation at all, a property thought to be important for establishing open-ended autocurricula (Leibo et al., 2019; Stanley, 2019). However, the presence of multiple interacting autonomous systems raises a host of new challenges. Autonomously operating learning agents must be robust to the presence of other learning agents in their environment (e.g.

Augmented Reality or AR filters on selfies have become very popular on social media platforms for a variety of applications, including marketing, entertainment and aesthetics. Given the wide adoption of AR face filters and the importance of faces in our social structures and relations, there is increased interest by the scientific community to analyze the impact of such filters from a psychological, artistic and sociological perspective. However, there are few quantitative analyses in this area mainly due to a lack of publicly available datasets of facial images with applied AR filters. The proprietary, close nature of most social media platforms does not allow users, scientists and practitioners to access the code and the details of the available AR face filters. Scraping faces from these platforms to collect data is ethically unacceptable and should, therefore, be avoided in research. In this paper, we present OpenFilter, a flexible framework to apply AR filters available in social media platforms on existing large collections of human faces. Moreover, we share FairBeauty and B-LFW, two beautified versions of the publicly available FairFace and LFW datasets and we outline insights derived from the analysis of these beautified datasets.

The AI Policy Forum (AIPF) is an initiative of the MIT Schwarzman College of Computing to move the global conversation about the impact of artificial intelligence from principles to practical policy implementation. Formed in late 2020, AIPF brings together leaders in government, business, and academia to develop approaches to address the societal challenges posed by the rapid advances and increasing applicability of AI. The co-chairs of the AI Policy Forum are Aleksander Madry, the Cadence Design Systems Professor; Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science; and Luis Videgaray, senior lecturer at MIT Sloan School of Management and director of MIT AI Policy for the World Project. Here, they discuss talk some of the key issues facing the AI policy landscape today and the challenges surrounding the deployment of AI. The three are co-organizers of the upcoming AI Policy Forum Summit on Sept. 28, which will further explore the issues discussed here. Q: Can you talk about the ongoing work of the AI Policy Forum and the AI policy landscape generally?

Graphic footage: Fox News host Tucker Carlson weighs in on issues facing Americans ahead of the midterm elections on "Tucker Carlson Tonight." A Michigan man pleaded guilty last week to murdering, dismembering and eating the body parts of another man he met on a dating app. Mark David Latunski, 53, of Shiawassee County, Michigan, admitted in court last Thursday that he killed 25-year-old hairdresser Kevin Bacon after luring the University of Michigan-Flint student to his home in December 2019, according to local outlet Mlive.com. Latunski pleaded guilty as charged to mutilation of a body and to open murder, which encompasses murder in the first and second degree. Latunski acknowledged stabbing Bacon in the back and taking parts of his dead body to the kitchen, where he ate them, after meeting the young man on Grindr, which is a hookup app for gay, bisexual and transgender men.

Artificial Intelligence (AI) has an increasing say in the range of opportunities we are offered in life. Artificial neural networks might be used in deciding whether you will get a loan, an apartment, or your next job based on datasets collected from around the globe. Generative adversarial networks (GANs) are used to produce real-looking but fake content online that can affect our political opinion-formation and election freedom. In some cases, our only contact for a service provider is an AI system, which is used to collect and analyze the content of customer input and to provide solutions with natural language processing. In the context of Western democracies, threats and issues related to these tools are frequently viewed as problematic. On the one hand, AI technologies are shown to help include more people in collective decision-making and potentially decrease the cognitive bias occurring when humans make decisions, leading to fairer outcomes.On the other hand, studies indicate that certain AI technologies can lead to biased decisions and decrease the level of human autonomy in a way that threatens our fundamental human rights. While recognizing individual cases where rights and freedoms are being violated, we can easily neglect rapid and in some cases alarming changes occurring in the big picture: People seem to have ever less control over their own lives and decisions that affect them. This has been brought forward by several authors and academics, such as James Muldoon in Platform Socialism, Shoshana Zuboff in Surveillance Capitalism and Mark Coeckelbergh in The Political Philosophy of AI. Control over one's life and collective decision-making are both essential building blocks of the fundamental structure of most Western societies: democracy. Whereas some attempts have already been made to better understand the relationship between AI and democracy (see, e.g., Nemitz 2018, Manheim & Kaplan 2019, and Mark Coeckelberg's above-mentioned book), the discussion remains limited.

Oceans cover 71% of our planet and contain a volume of plastic that has become so high that "in just a few years, we might end up with a pound of plastic for every three pounds of fish in the sea" (1). Every year, more than 8 millions metric tons of plastics enter our oceans on top of an estimated 150 million metric tons already circulating through them (1). The Pacific Garbage Patch has become a poignant icon of ocean pollution across media outlets, environmental campaigns, and social media platforms. We've all seen the images of birds nesting in piles of garbage, sea turtles with plastic straws up their noses, and fish entangled in netting that are displayed across the internet. But even more horrifying is that "approximately half of all plastic pollution is submerged below the ocean surface, much of it in the form of microplastics so small that we may never be able to clean them up completely" (2).

Before we can discuss how this law applies, let's start with what the law actually says. This is a fairly broad bill, and one that requires a bit of unpacking before understanding the responsibilities of the employers. Unfortunately, the law, as written, does not give much guidance for employers. Let's attempt to unpack what the bill does provide. Before discussing what this means for employers, let's look at some definitions: The Bias Audit must include testing of the AI Tool to assess the tool's disparate impact on persons of any Component 1 Category.