By focusing on a dialogue with consumers through more robust, conversational AI, brands can deliver the best customer experience possible while also respecting consumer privacy. With increasing regulation and industry shifts by big tech companies, brands of all sizes need to reevaluate their data practices. Christian Ward, chief data officer, Yext, discusses how breakthroughs in conversational AI and natural language processing enable a consent-based dialogue between brands and consumers that provides a personalized customer experience at scale. We stand at a decisive moment for brands as they look to the future of consumer data strategies. With disparate state-by-state legislation, numerous proposals for a national regulatory framework in the US, and differing international laws, including the GDPR, the data governance guidelines for brands to follow are inconsistent – and potentially costly should they run afoul of the rules.

The European Commission will present a liability regime targeted to damage originating from Artificial Intelligence (AI) that would put causality presumption on the defendant, according to a draft obtained by EURACTIV. The AI Liability Directive is scheduled to be published on 28 September, and it is meant to complement the Artificial Intelligence Act, an upcoming regulation that introduces requirements for AI systems based on their level of risk. "This directive provides in a very targeted and proportionate manner alleviations of the burden of proof through the use of disclosure and rebuttable presumptions," the draft reads. "These measures will help persons seeking compensation for damage caused by AI systems to handle their burden of proof so that justified liability claims can be successful ." The proposal follows the European Parliament's own-initiative resolution adopted in October 2020 that called for facilitating the burden of proof and a strict liability regime for AI-enabled technologies.

Twitter decided two users could not be part of their world after an artificial intelligence scientist "whitewashed" actress Halle Bailey in "The Little Mermaid" trailer. In addition to suspending their respective accounts, the AI guy has been blased by other users on the site for digitally replacing Bailey -- who is black -- with a fake white actress. The viral tweet circulated days after the new Disney film's trailer reportedly received over 1.5 million dislikes on YouTube from "racist" viewers who are upset that the previously white-skinned, red-headed Ariel is now a black woman. Twitter user @TenGazillioinIQ took that to the next level and "fixed" the clip by using AI to make the live-action fish woman white. "Credits to our member Artificial Intelligence scientist @TenGazillioinIQ," the tweet -- made by another user, @vandalibm, read, according to screenshots taken by DailyMail before the account was suspended.

Forensic license plate recognition (FLPR) remains an open challenge in legal contexts such as criminal investigations, where unreadable license plates (LPs) need to be deciphered from highly compressed and/or low resolution footage, e.g., from surveillance cameras. In this work, we propose a side-informed Transformer architecture that embeds knowledge on the input compression level to improve recognition under strong compression. We show the effectiveness of Transformers for license plate recognition (LPR) on a low-quality real-world dataset. We also provide a synthetic dataset that includes strongly degraded, illegible LP images and analyze the impact of knowledge embedding on it. The network outperforms existing FLPR methods and standard state-of-the art image recognition models while requiring less parameters. For the severest degraded images, we can improve recognition by up to 8.9 percent points.

A novel New York City law that penalizes employers for bias in artificial intelligence hiring tools is leaving companies scrambling to audit their AI programs before the law takes effect in January. The law, which requires employers to conduct an independent audit of the automated tools they use, marks the first time employers in the US will face heightened legal requirements if they wish to use those any automated decision-making tools. Such tools--which can range from algorithms built to find ideal candidates to software that assesses body language--have faced scrutiny in recent years for their potential to perpetuate bias against protected groups. But without guidance from the city, employers aren't clear what, exactly, is expected of them and how to prepare. "Notably, the law does not define who or what is meant by an'independent auditor,'" said Danielle J. Moss, a partner at Gibson Dunn & Crutcher LLP.

Now is the time to talk, to put in place standards and regulations to mitigate the risk of a society ... [ ] based on surveillance and other nightmarish scenarios. Artificial Intelligence has become commonplace in the lives of billions of people globally. Research shows that 56% of companies have adopted AI in at least one function, especially in emerging nations. AI is used in everything from optimizing service operations through to recruiting talent. It can capture biometric data and it already helps in medical applications, judicial systems, and finance, thus making key decisions in people's lives. But one huge challenge remains to regulate its use.

In June, the Canadian government proposed new legislation to regulate artificial intelligence (AI). The proposed Artificial Intelligence and Data Act (AIDA) is part of Bill C-27, which also proposes a new privacy framework, the Consumer Privacy Protection Act (For more on federal privacy reform, see our recent blog). If passed, AIDA would be the first comprehensive law in Canada regulating AI. AIDA intends to promote the responsible use of AI. It aims to ensure high-impact AI systems are developed in a way that mitigates risk of harm and bias.

People in Japan, the U.S. and Germany show different concerns regarding artificial intelligence (AI) being used in entertainment, shopping services, or to help find criminals, reports a new study in AI and Ethics. Japanese people tended to report more concern in AI used to fight crime, while Germans and Americans tended to report more concern over the ethical and social aspects of using AI in entertainment, according to the study. "We found there is a difference in the AI and ELSI [ethics, legal, and social issues] levels of understanding between countries. I think it will become important to carry out thorough discussions about the legal and policy issues surrounding AI," said first author and Kanazawa University Associate Professor Yuko Ikkatai. AI is currently being used in a wide range of fields, which has raised positive and negative attitudes in the general public.

Astreya Partners is an equal employment and affirmative action employer. We evaluate qualified applicants on merit and business needs and not on race, color, religion, creed, gender, sexual orientation, national origin, ancestry, age, disability, genetic information, marital status, veteran status or any other factor protected by law.

It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. Traditional algorithmic fairness research has mainly focused on supervised learning tasks, particularly classification. While fairness in unsupervised learning has received some attention, the literature has primarily addressed fair representation learning of continuous embeddings. In this paper, we conversely focus on unsupervised learning using probabilistic graphical models with discrete latent variables. We develop a fair stochastic variational inference technique for the discrete latent variables, which is accomplished by including a fairness penalty on the variational distribution that aims to respect the principles of intersectionality, a critical lens on fairness from the legal, social science, and humanities literature, and then optimizing the variational parameters under this penalty. We first show the utility of our method in improving equity and fairness for clustering using na\"ive Bayes and Gaussian mixture models on benchmark datasets. To demonstrate the generality of our approach and its potential for real-world impact, we then develop a special-purpose graphical model for criminal justice risk assessments, and use our fairness approach to prevent the inferences from encoding unfair societal biases.