The field of machine ethics is concerned with the question of how to embed ethical behaviors, or a means to determine ethical behaviors, into artificial intelligence (AI) systems. The goal is to produce artificial moral agents (AMAs) that are either implicitly ethical (designed to avoid unethical consequences) or explicitly ethical (designed to behave ethically). Van Wynsberghe and Robbins' (2018) paper Critiquing the Reasons for Making Artificial Moral Agents critically addresses the reasons offered by machine ethicists for pursuing AMA research; this paper, co-authored by machine ethicists and commentators, aims to contribute to the machine ethics conversation by responding to that critique. The reasons for developing AMAs discussed in van Wynsberghe and Robbins (2018) are: it is inevitable that they will be developed; the prevention of harm; the necessity for public trust; the prevention of immoral use; such machines are better moral reasoners than humans, and building these machines would lead to a better understanding of human morality. In this paper, each co-author addresses those reasons in turn. In so doing, this paper demonstrates that the reasons critiqued are not shared by all co-authors; each machine ethicist has their own reasons for researching AMAs. But while we express a diverse range of views on each of the six reasons in van Wynsberghe and Robbins' critique, we nevertheless share the opinion that the scientific study of AMAs has considerable value.

Nearly half of the companies in Europe that call themselves AI start-ups don't in fact use artificial intelligence, a new report found. The research, published Tuesday by London-based venture capital firm MMC Ventures, found no evidence that artificial intelligence was an important part of the products offered by 40 percent of Europe's 2,830 AI start-ups. The report's authors individually reviewed the activities, functions and funding of start-ups across 13 EU countries. It did not name any of the start-ups involved in the study. The findings raise questions about how the term AI has become a blanket phrase for start-ups looking to attract investments and position themselves at the forefront of tech innovation.

Machine learning (ML) and artificial intelligence (AI) continue to capture media attention and business investments. IDC estimates machine learning and AI spending to increase from $19.1 billion in 2018 to $52.2 billion by 2021. With billions of dollars invested in machine learning and AI, it's no surprise that tech giants Google, Microsoft and Amazon are investing billions in cloud infrastructure and development tools to accelerate the delivery of custom machine learning applications. Case in point, machine learning was the third-highest category for the number of patents granted between 2013 and 2017. So, what exactly is machine learning?

The 11th meeting of Forum for Information Retrieval Evaluation 2019 will be held in Kolkata, India. Started in 2008 with the aim of building a South Asian counterpart for TREC, CLEF and NTCIR, FIRE has since evolved continuously to meet the new challenges in multilingual information access. It has expanded to include new domains like plagiarism detection, legal information access, mixed script information retrieval and spoken document retrieval to name a few. Continuing the trend started in 2015, the FIRE will consist of a peer-reviewed conference track along with evaluation tasks. We invite full and short papers from information retrieval, natural language processing, and related domains.

Machine-learning models are notoriously susceptible to algorithmic bias, particularly when it comes to people of color. Just a few years back, software used by the US criminal justice system was shown to disproportionately suggest black people were more likely to commit crimes. Then there was the time that Google's image-recognition system identified African Americans as gorillas. But now this could be an even bigger problem than originally thought. A new report suggests that the object-detection models used by self-driving cars are significantly less capable of identifying dark-skinned pedestrians than those with lighter complexions. While the study is certainly no smoking gun for opponents of this controversial technology – it has yet to be peer reviewed, and it used an approximated model and data set rather than one currently used by self-driving vehicle manufacturers – it still brings to light an important possibility.

US Senators Roy Blunt and Brian Schatz want to protect people's facial recognition data and make it much harder to sell now that information is treated as currency. The lawmakers have introduced the bipartisan Commercial Facial Recognition Privacy Act of 2019, which prohibits companies from collecting and resharing face data for identifying or tracking purposes without people's consent. The Senators have conjured up the bill because while facial recognition has been used for security and surveillance for decades, it's "now being developed at increasing rates for commercial applications." They argue that a lot of people aren't aware that the technology is being used in public spaces and that companies can collect identifiable info to share or sell to third parties -- similar to how carriers have been selling location data to bounty hunters for years. In addition to prohibiting companies from redistributing or disseminating data, the bill would also require them to notify customers whenever facial recognition is in use. FR technologies also need to undergo third-party testing prior to implementation to address accuracy and bias issues, seeing as they tend to have higher error rates when it comes to women and people of color.

As such, even though these technologies bring huge potential and opportunities, they still need to be closely monitored. The University of New South Wales Research Ethics and Compliance Support Director Dr Ted Rohr told HITNA that issues around ethics arise when healthcare access data from medical records for research, for example. "Ethics is all about deciding whether the use of technology is appropriate and is used for public good. For example, AI has its positives, but it can be misused. So, having an ethical framework allows the proper use of medical databases for research and experiments with patients using devices," he said.

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A PwC report estimates that by 2030, 70 percent of the profits generated by artificial intelligence (AI) technologies will be shared between the U.S. and China. While the two countries compete to develop the most advanced AI applications, there are also many opportunities for cooperation to mitigate the technology's potential risks. On March 12, The Center for Technology Innovation hosted a panel discussion where Brookings scholars Darrell West, Nicol Turner-Lee, and Ryan Haas were joined by Robb Gordon, the chief counsel and director of Intel's China legal team. The panel examined how the two nations have deployed artificial intelligence technologies so far and how they plan to use them in the future. China hopes to become a global leader in AI in the next decade, and has committed to investing $150 billion to achieve this goal.

Lord Burnett of Maldon, the current Lord Chief Justice, has set up a new Advisory Body with the aim of ensuring that the Judiciary of England and Wales is fully informed about developments in artificial intelligence (AI). Professor Richard Susskind, President of the Society for Computers & Law, has been named chair of the body, and in a recent interview stated that AI has taken off in the last six or seven years, to the point where it has become "affordable and practical". Professor Susskind believes that the new group will start a dialogue among the judiciary about "one of the most influential technologies that there is", and recognises the importance of judges being open to the opportunities that AI technology could offer to the court system (with "practical tasks" cited as an example). The 10-person team will be made up of both senior judges (including Lord Neuberger, past President of the UK Supreme Court, and Lady Justice Sharp, Vice-President of the Queen's Bench Division), as well as leading experts on AI and law (such as Professor Katie Atkinson, past President of the International Association for AI and Law). There is little doubt that automation already plays an essential role for the legal profession, for example, in large disclosure exercises.