Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations for the collection of test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.

Counterfactual explanations are increasingly used to address interpretability, recourse, and bias in AI decisions. However, we do not know how well counterfactual explanations help users to understand a systems decisions, since no large scale user studies have compared their efficacy to other sorts of explanations such as causal explanations (which have a longer track record of use in rule based and decision tree models). It is also unknown whether counterfactual explanations are equally effective for categorical as for continuous features, although current methods assume they do. Hence, in a controlled user study with 127 volunteer participants, we tested the effects of counterfactual and causal explanations on the objective accuracy of users predictions of the decisions made by a simple AI system, and participants subjective judgments of satisfaction and trust in the explanations. We discovered a dissociation between objective and subjective measures: counterfactual explanations elicit higher accuracy of predictions than no-explanation control descriptions but no higher accuracy than causal explanations, yet counterfactual explanations elicit greater satisfaction and trust than causal explanations. We also found that users understand explanations referring to categorical features more readily than those referring to continuous features. We discuss the implications of these findings for current and future counterfactual methods in XAI.

With the Artificial Intelligence Act (AI Act), we have – again – crossed the Rubicon. The die has been cast, there is no way back. We are setting standards for another industry that until now has been left mostly on its own, that has important social functions, and that is of central importance in the global tech rivalry. The European electorate was and still is quite united in demanding rules for digital players while maintaining easy digital access and a competitiveness for all things digital. With the AI Act and other legislation currently under way in such fields as cybersecurity, data, crypto and chips, the European Union is finalizing what it began with the General Data Privacy Regulation (GDPR), the Digital Services Act (DSA) and the Digital Markets Act (DMA). It will surely not be the last time digital policy is undertaken in Brussels, and updates to these regulations are partly already necessary.

Management by humans can be dismal. "In the old world of cabbing, the drivers were often abused," says James Farrar, director of non-profit organisation Worker Info Exchange (WIE). Drivers would pay the same fee to drive for a taxi company, but receive differing amounts of business. "You'd have so-called'fed' drivers [fed with work] and'starved' drivers, with favoured drivers getting all the nice work," he says, with some dispatchers who allocated work demanding bribes. As a result, many welcomed dispatchers being replaced by algorithms: Farrar recalls cheering this in a session for new Uber drivers.

Let's play a little game. Imagine that you're a computer scientist. Your company wants you to design a search engine that will show users a bunch of pictures corresponding to their keywords -- something akin to Google Images. You're a great computer scientist, and this is basic stuff! But say you live in a world where 90 percent of CEOs are male. Should you design your search engine so that it accurately mirrors that reality, yielding images of man after man after man when a user types in "CEO"? Or, since that risks reinforcing gender stereotypes that help keep women out of the C-suite, should you create a search engine that deliberately shows a more balanced mix, even if it's not a mix that reflects reality as it is today?

The Japanese government passed a bill Tuesday to introduce new rules for next-generation mobility, such as unmanned self-driving vehicles, automated delivery robots and electric kick scooters. The bill to revise the current road traffic law was approved at a plenary meeting of the House of Representatives, the lower chamber of the Diet, following its passage at the House of Councillors, the upper chamber, last week. Under the revised law, a license system will be introduced for operators of transport services using unmanned vehicles with Level 4 autonomy, which requires no driver in the remotely monitored vehicle within a limited area. Such vehicles are expected to be used for residents in depopulated areas. The new rules obligate the operators of unmanned vehicles to prepare a system to ensure that staff would be sent out to the site of any accidents.

Five years ago, this wouldn't have been possible. Neither the city's infrastructure nor existing video analytics could support sending and processing footage at the necessary scale. But then fiber coverage expanded, AI capabilities advanced, and companies abroad, seeing an opportunity, began dumping the latest surveillance technologies into the country. The local security industry, forged under the pressures of a high-crime environment, embraced the menu of options. The effect has been the rapid creation of a centralized, coordinated, entirely privatized mass surveillance operation.

AI ethics has always been a topic of concern for most organizations hoping to leverage the technology in some use cases. While AI has improved over the years, the reality is that AI has become integral to products and services, with some organizations now looking to develop AI codes of ethics. While the whole notion of AI ethics is still debatable in many ways, the use of AI can not be held back, especially with the world becoming increasingly influenced by modern technologies. Last year, UNESCO member states adopted the first-ever global agreement on the Ethics of AI. The guidelines define the common values and principles to guide the construction of necessary legal infrastructure to ensure the healthy development of AI. "Emerging technologies such as AI have proven their immense capacity to deliver for good. However, its negative impacts that are exacerbating an already divided and unequal world, should be controlled. AI developments should abide by the rule of law, avoiding harm, and ensuring that when harm happens, accountability and redressal mechanisms are at hand for those affected," stated UNESCO.

Virtually every industry stays updated on the year's latest technology trends. New technologies are rapidly advancing, and businesses across every sector want to invest in them to reap the benefits -- companies in the marketing industry are no exception. The digital marketing landscape is ever-changing, and a primary factor affecting marketers is new tech innovations. Many principles remain the same over time, but professionals can continue following them and integrate new technologies simultaneously. The metaverse is a network of unique, immersive, and virtual spaces where users have personal avatars.

AI ethics has always been a topic of concern for most organizations hoping to leverage the technology in some use cases. While AI has improved over the years, the reality is that AI has become integral to products and services, with some organizations now looking to develop AI codes of ethics. While the whole notion of AI ethics is still debatable in many ways, the use of AI can not be held back, especially with the world becoming increasingly influenced by modern technologies. Last year, UNESCO member states adopted the first-ever global agreement on the Ethics of AI. The guidelines define the common values and principles to guide the construction of necessary legal infrastructure to ensure the healthy development of AI. "Emerging technologies such as AI have proven their immense capacity to deliver for good. However, its negative impacts that are exacerbating an already divided and unequal world, should be controlled. AI developments should abide by the rule of law, avoiding harm, and ensuring that when harm happens, accountability and redressal mechanisms are at hand for those affected," stated UNESCO.