Artificial intelligence researchers are facing a problem of accountability: How do you try to ensure decisions are responsible when the decision maker is not a responsible person, but rather an algorithm? Right now, only a handful of people and organizations have the power--and resources--to automate decision making. Organizations rely on AI to approve a loan or shape a defendant's sentence. But the foundations upon which these intelligent systems are built are susceptible to bias. Bias from the data, from the programmer, and from a powerful company's bottom line, can snowball into unintended consequences.

A bipartisan group of House lawmakers has introduced legislation that would give people more control over the algorithms that shape their online experience. If passed, the Filter Bubble Transparency Act would require companies like Meta to offer a version of their platforms that runs on an "input-transparent" algorithm that doesn't pull on user data to generate recommendations. The bill would not do away with "opaque" recommendation algorithms altogether but would make it a requirement to include a toggle that allows people to switch that functionality off. Additionally, platforms that continue to use recommendation algorithms need to have a notification that informs people those recommendations are based on inferences generated by their personal data. The prompt can be a one-time notice, but it would need to be presented in a "clear, conspicuous manner," according to the proposed bill. The legislation was introduced by Representatives Ken Buck (R-CO), David Cicilline (D-RI), Lori Trahan (D-MA) and Burgess Owens (R-UT).

The jury at Kyle Rittenhouse's murder trial Tuesday watched drone video that showed Rittenhouse wheeling around and shooting Joseph Rosenbaum at close range during a night of turbulent protests on the streets of Kenosha. The video, zoomed in and slowed down by a forensic imaging specialist, was played as the prosecution's case appeared to be winding down after a week of testimony in which some of its own witnesses often bolstered Rittenhouse's claim of self-defense. The footage showed Rosenbaum following Rittenhouse before Rittenhouse suddenly spins around and fires his rifle at him. Rosenbaum falls, and Rittenhouse runs around a car. Dr. Doug Kelley, a forensic pathologist with the Milwaukee County medical examiner's office, said Rosenbaum was shot by someone who was within 4 feet of him.

The draft text, presented today by the rapporteur, says that the public debate should shift towards a focus on the enormous potential of AI, which offers humankind the unique chance to improve almost every area of our lives. AI could help combat climate change, pandemics and global hunger, and enhance quality of life through personalised medicine. According to the draft document, AI can substantially increase productivity, innovation, growth and job creation. The EU should not regulate AI as a technology; instead, the type, intensity and timing of regulatory intervention should solely depend on the type of risk associated with a particular use of an AI system. The text warns that the EU is currently falling behind in the global tech race that will determine the future political and economic global power balance.

In the online world known as the "metaverse," there are many lines to be drawn and rules to be set. Continue reading to find out how humans and all of our flaws will fit into this new world. I was brought up and trained in the Internet Age by people who really believed that nation states were on the verge of crumbling…and we could geek around it. These people [and their nation states] were irrelevant. Ms. Crawford had a key tech role in the Obama Administration and is now a law professor.

Consider n agents forming an egalitarian, self-governed community. Their first task is to decide on a decision rule to make further decisions. We start from a rather general initial agreement on the decision-making process based upon a set of intuitive and self-evident axioms, as well as simplifying assumptions about the preferences of the agents. From these humble beginnings we derive a decision rule. Crucially, the decision rule also specifies how it can be changed, or amended, and thus acts as a de facto constitution. Our main contribution is in providing an example of an initial agreement that is simple and intuitive, and a constitution that logically follows from it. The naive agreement is on the basic process of decision making - that agents approve or disapprove proposals; that their vote determines either the acceptance or rejection of each proposal; and on the axioms, which are requirements regarding a constitution that engenders a self-updating decision making process.

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

The proposed European Artificial Intelligence Act (AIA) is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can (and should) be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the conformity assessments that providers of high-risk AI systems are expected to conduct, and the post-market monitoring plans that providers must establish to document the performance of high-risk AI systems throughout their lifetimes. We argue that AIA can be interpreted as a proposal to establish a Europe-wide ecosystem for conducting AI auditing, albeit in other words. Our analysis offers two main contributions. First, by describing the enforcement mechanisms included in the AIA in terminology borrowed from existing literature on AI auditing, we help providers of AI systems understand how they can prove adherence to the requirements set out in the AIA in practice. Second, by examining the AIA from an auditing perspective, we seek to provide transferable lessons from previous research about how to refine further the regulatory approach outlined in the AIA. We conclude by highlighting seven aspects of the AIA where amendments (or simply clarifications) would be helpful. These include, above all, the need to translate vague concepts into verifiable criteria and to strengthen the institutional safeguards concerning conformity assessments based on internal checks.

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As international AI regulation starts to take shape, organisations must educate themselves on lessons from the past. One thing is clear: implementing AI and automated decision-making should not mean imposing biased decisions on the public. While the ethical imperative for this principle is (hopefully) obvious, the legal and regulatory imperative is gathering pace. In April 2021, the EU put forward "the first ever legal framework on AI". It warns against bias in AI systems, and argues that firms must do their bit to monitor and prevent it.