Alex Khizhniak, director of Technical Evangelism at IT services provider Altros, stated, "Being connected to other cars on the road will eventually make driving much safer. Combined with predictive analysis, smart systems could substitute for a driver in case of emergency. Although these technologies are still developing - and some legislations should also be introduced- the future looks promising for self-driving and intelligent driving assistants." While many have been vocal about their concerns regarding the regulation of autonomous or connected cars, there are many advantages that must be considered before delving into the risks. One of the many key benefits of connected cars is that they could contribute to safer traffic patterns in cities with congestion issues as a consequence of rapid urbanization.

There's no shortage of promises when it comes to AI. Some say it will solve all problems while others warn it will bring about the end of the world as we know it. Both positions regularly play out in Hollywood plotlines like Westworld, Carbon Black, Minority Report, Her, and Ex Machina. Those stories are compelling because they require us as creators and consumers of AI technology to decide whether we trust an AI system or, more precisely, trust what the system is doing with the information it has been given. This content was produced by Nuance.

The public remains sceptical over the use of artificial intelligence (AI) to make decisions, research suggests, with nearly two-thirds wanting tighter regulation around its use. A survey by AI innovation firm Fountech.ai Artificial intelligence is becoming more prominent in large-scale decision-making, with algorithms now being used in areas such as healthcare with the aim of improving speed and accuracy of decision-making. However, the research shows that the public does not yet have complete trust in the technology – 69 per cent say humans should monitor and check every decision made by AI software, while 61 per cent said they thought AI should not be making any mistakes in the first place. The idea of a machine making a decision also appears to have an impact on trust in AI, with 45 per cent saying it would be harder to forgive errors made by technology compared with those made by a human.

Elliptic Labs announced today that it has signed a contract with a new smartphone OEM customer in Asia that will incorporate Elliptic's INNER BEAUTY AI Virtual Proximity Sensor into their next smartphone model. "We are excited that more smartphone OEMs see the value in our technology and appreciate the opportunities that Elliptic's AI Virtual Smart Sensor platform has to offer" said Laila Danielsen, CEO of Elliptic Labs. INNER BEAUTY is just one of several virtual smart sensors that Elliptic Labs provides OEMs that deliver greater user functionality and cleaner, sleeker phone designs. Another such example is INNER REFLECTION, a pioneering virtual presence sensor that offers sub-millimeter precision capable of detecting a person breathing. Elliptic Labs is a global AI software company and the world leader in AI virtual sensors for the smartphone, IoT, and automotive industries.

Every business in virtually every industry you can think of has had its world turned upside down by the Covid-19 pandemic. And as the lockdown has progressed, it's become increasingly clear that things won't be...

The theory is that the law should deal with like situations in like ways. The theory is that the law should deal with like situations in like ways. In some respects, however, Artificial Intelligence, especially the concept of machine learning, is virtually unprecedented, so the law is struggling with how to deal with it, or will be soon. Consider a few of the difficulties that the law will probably need to address: Who will pay for healthcare services dependent on AI, and who will be entitled to such payments? Will those payments be keyed to "value," the currently orthodox yardstick?

If your organization is implementing or thinking of implementing a contact-tracing app, it's wise to consider more than just workforce safety. Failing to do so could expose your company other risks such as employment-related lawsuits and compliance issues. More fundamentally, companies should be thinking about the ethical implications of their AI use. Contact-tracing apps are raising a lot of questions. For example, should employers be able to use them? If so, must employees opt-in or can employers make them mandatory?

As machine learning informs increasingly consequential decisions, different metrics have been proposed for measuring algorithmic bias or unfairness. Two popular fairness measures are calibration and equality of false positive rate. Each measure seems intuitively important, but notably, it is usually impossible to satisfy both measures. For this reason, a large literature in machine learning speaks of a fairness tradeoff between these two measures. This framing assumes that both measures are, in fact, capturing something important. To date, philosophers have not examined this crucial assumption, and examined to what extent each measure actually tracks a normatively important property. This makes this inevitable statistical conflict, between calibration and false positive rate equality, an important topic for ethics. In this paper, I give an ethical framework for thinking about these measures and argue that, contrary to initial appearances, false positive rate equality does not track anything about fairness, and thus sets an incoherent standard for evaluating the fairness of algorithms.

With the starting point that implicit human biases are reflected in the statistical regularities of language, it is possible to measure biases in static word embeddings. With recent advances in natural language processing, state-of-the-art neural language models generate dynamic word embeddings dependent on the context in which the word appears. Current methods of measuring social and intersectional biases in these contextualized word embeddings rely on the effect magnitudes of bias in a small set of pre-defined sentence templates. We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model, dispensing with templates. Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts. Furthermore, we develop two methods, Intersectional Bias Detection (IBD) and Emergent Intersectional Bias Detection (EIBD), to automatically identify the intersectional biases and emergent intersectional biases from static word embeddings in addition to measuring them in contextualized word embeddings. We present the first algorithmic bias detection findings on how intersectional group members are associated with unique emergent biases that do not overlap with the biases of their constituent minority identities. IBD achieves an accuracy of 81.6% and 82.7%, respectively, when detecting the intersectional biases of African American females and Mexican American females. EIBD reaches an accuracy of 84.7% and 65.3%, respectively, when detecting the emergent intersectional biases unique to African American females and Mexican American females (random correct identification probability ranges from 1.0% to 25.5%).

In the age of big data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. AI systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people's fundamental human rights. Despite increasing attention to these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically- and socially-aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process.