It has been proposed that, "Privacy matters to the electorate, and smart business looks at how to use data to find out information while remaining in compliance with regulatory rules." Since "smart business" also consists of "the electorate" as employees, at least one burning question is whether privacy or ethical violations in technologies like artificial intelligence (AI) will really matter sufficiently to employees who may be more concerned about putting food on the table than about raising concerns or performing whistleblowing, with potentially negative job consequences for them? And what happens if the country, region, or sector is too immature to have meaningful regulatory rules to comply with? Does it then become a case of almost anything goes? After all, no laws will be broken by the "smart business" in this case.

This week the Chair of the European Parliament's committee on AI expressed concerns about the enforcement of the European Commission's proposed AI rules, which he said could create national fragmentation similar to that seen with the GDPR. So what are the issues involved, what is the proposed new EU law and how does GDPR already regulate AI? At the start of 2020, 42% of companies in the EU said they use technologies that depend on AI, and another 18% of companies said they are planning to use AI in the future (European Enterprise Survey – FRA, 2020). So, this is clearly an area that is justifiably generating considerable activity and interest from both industry and the regulators. It is important to note however that currently the available technologies involve varying levels of complexity, automation and human review and, despite some companies' optimism about their AI capabilities, many applications currently used remain in the development stage.

In April, the European Union took its first steps toward building a new comprehensive framework for regulating artificial intelligence (AI). Drafted by the European Commission, the Artificial Intelligence Act bans certain AI practices outright and mandates that AI applications deemed as "high risk" meet strict data governance and risk management requirements. The bill may be an inflection point in Europe's digital future. In the United States, policymakers are rightly focused on boosting America's competitiveness by supporting development and use of AI. In proposing the AI Act, European leaders seem to believe that their capacity and willingness to regulate is a competitive advantage over more innovative economies.

In-house legal teams including Vodafone, Featurespace and Ferrero are using London-based legaltech firm Luminance's new artificial intelligence platform to help their departments get a better grip on their contracting issues. The platform – Luminance Corporate – streamlines the contract lifecycle process by automating contract drafting, version control and renewal, enabling in-house lawyers to better understand, manage and negotiate their contracts. The platform's AI capabilities also provide insights on those contracts, meaning lawyers don't have to manually search for key information. Rosemary Martin, group general counsel and company secretary at Vodafone, said: "Good technology can make a really positive difference in corporate legal departments. Being able to rapidly analyse contracts and display critical information means lawyers no longer have to waste time trawling through their contracts."

Providing meaningful and actionable explanations to end-users is a fundamental prerequisite for implementing explainable intelligent systems in the real world. Explainability is a situated interaction between a user and the AI system rather than being static design principles. The content of explanations is context-dependent and must be defined by evidence about the user and its context. This paper seeks to operationalize this concept by proposing a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users mental models, (2) the end-users cognitive process, (3) the user interface, (4) the human-explainer agent, and the (5) agent process. We first define each component of the architecture. Then we present the Abstracted Explanation Space, a modeling tool that aggregates the architecture's components to support designers in systematically aligning explanations with the end-users work practices, needs, and goals. It guides the specifications of what needs to be explained (content - end-users mental model), why this explanation is necessary (context - end-users cognitive process), to delimit how to explain it (format - human-explainer agent and user interface), and when should the explanations be given. We then exemplify the tool's use in an ongoing case study in the aircraft maintenance domain. Finally, we discuss possible contributions of the tool, known limitations/areas for improvement, and future work to be done.

Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly. We model the social network as an And-or Graph, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. Moreover, we formulate a sequential structure prediction task, and propose an $\alpha$-$\beta$-$\gamma$ strategy to incrementally parse SocAoG for the dynamic inference upon any incoming utterance: (i) an $\alpha$ process predicting attributes and relations conditioned on the semantics of dialogues, (ii) a $\beta$ process updating the social relations based on related attributes, and (iii) a $\gamma$ process updating individual's attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph show that our model infers social relations more accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference.

We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. The proposed test is a flexible, interpretable, and statistically rigorous tool for auditing whether exhibited biases are intrinsic to the algorithm or due to the randomness in the data. The statistical challenges, which may arise from multiple impact criteria that define group fairness and which are discontinuous on model parameters, are conveniently tackled by projecting the empirical measure onto the set of group-fair probability models using optimal transport. This statistic is efficiently computed using linear programming and its asymptotic distribution is explicitly obtained. The proposed framework can also be used to test for testing composite fairness hypotheses and fairness with multiple sensitive attributes. The optimal transport testing formulation improves interpretability by characterizing the minimal covariate perturbations that eliminate the bias observed in the audit.

The transcription market in the US alone was valued at $19.8 billion USD in 2019 and is anticipated to expand by 6.1% from 2020 to 2027. Organizations across the globe generate large volumes of data every day that can be effectively used to obtain valuable insights. Today's businesses and organizations are using transcription for research projects, classes, webinars, legal proceedings, data analysis, blog posts, website content, search engine optimization (SEO) and to make workplace environments and external materials and videos more accessible. Verbit builds its own speech recognition engine in-house. The engine uses three models: The acoustic model: reduces background noise and echoes to cancel out factors that reduce the audio quality.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A U.S. airman from Florida accused of raping an 11-year-old girl in Alabama last year had met the child on a dating app, according to a report. Air Force Senior Airman Keith Williams, 25, of a Hurlburt Field maintenance squadron, met the 11-year-old on the Badoo dating app before the alleged rape in October 2020, Northwest Florida Daily News reported, citing an affidavit filed in Alabama's Morgan County District Court. The girl's parents did not learn of the alleged sexual encounter in the backyard of their home until Feb. 12, when Williams sent the girl a friend request on Facebook, the report said.

Artificial Intelligence ("AI") applies intelligent algorithms in a manner that enables machines to perform tasks that generally require human thinking. Needless to say, AI is viewed as "the way of the future" and it is expected that consumer experiences will soon be driven entirely by AI. While the development of AI has already changed the way brand owners interact with their consumers, it still has a long way to go. The rise of AI will undoubtedly make significant impacts on an entity's branding and trademark practices. Brands are akin to a company's "personality" and a brand's value is connected to the relationship it builds with its consumers – namely, how well consumers relate to it and come to trust it.