AI has been facing a PR problem. Too often AI has introduced itself as a misogynist, racist and sinister robot. Remember the Microsoft Twitter chatbot named Tay, who was learning to mimic online conversations, but then started to blur out the most offensive tweets? Think of tech companies creating elaborate AI hiring tools, only to realise the technology was learning in the male-dominated industry to favour resumes of men over women. As much as this seems to be a facepalm situation, this happens a lot and seems not so easy to solve in an imperfect world, where even the most intelligent people have biases.

If AI gains legal personhood via the corporate loophole, laws granting equal rights to artificially intelligent agents may result, as a matter of equal treatment. That would lead to a number of indignities for the human population. Because software can reproduce itself almost indefinitely, if given civil rights, it would quickly make human suffrage inconsequential 14 leading to the loss of self-determination for human beings. Such loss of power would likely lead to the redistribution of resources from humanity to machines as well as the possibility of AIs serving as leaders, presidents, judges, jurors, and even executioners. We might see military AIs targeting human populations and deciding on their own targets and acceptable collateral damage.

Eager to make extra money on the side, Washington, D.C., resident Paula Alves Silva turned to a gig emblematic of the digital age: She recorded sentences read aloud in the comfort of her home to help train artificial intelligence (AI) software. Silva completed the tasks in her native Portuguese tongue for Seattle-based startup DefinedCrowd, which develops machine learning algorithms that power products for businesses including heavyweights MasterCard and BMW. Such recordings could be used in voice recognition products introduced in new countries, or to train existing systems to recognize non-native speakers or regional accents, the company says. Silva earned $20 -- from 8 to 33 cents per sentence -- and considered that satisfactory given the short amount of time it took to complete the tasks. The knowledge that her task would contribute to a new artificial intelligence system was a bonus, she said.

There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments.

Technology is driving major shifts in the job market, and that will require corporations, governments, and individuals to embrace new strategies, according to a new report by Boston Consulting Group (BCG) and Burning Glass Technologies, What's Trending in Jobs and Skills, being released today. BCG and Burning Glass, a leading provider of real-time labor market information, studied 95 million online job listings in the US from 2015 through 2018. The authors analyzed the number and growth rate of job listings and skill requirements across broad sectors and within hundreds of specific job areas, as classified by the US Labor Department's O*NET occupation system. Through this analysis, the report identifies the fastest-growing jobs and the fastest-growing skills in the job market. "No other job market study to date has been as statistically extensive or exhaustive," says Rainer Strack, managing director and senior partner at BCG and a coauthor of the report.

Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine bias disparity over a range of different algorithms and for different item categories and demonstrate significant differences between model-based and memory-based algorithms.

In recent years, various governments, international organisations, civil society groups and technology companies have issued documents outlining their principles around the development and use of Artificial Intelligence (AI). Yet, the world appears to be no closer to a universal set of AI norms. This brief suggests a rethinking of how AI norms should be formulated and outlines key lessons. First, technology firms reflect certain human biases that do not do justice to their global consumer base and make them unsuitable to lead the setting of AI principles. Second, while norms are ambiguous by design, the misuse of this ambiguity by actors to justify rights violations sets a dangerous precedent. Third, no single regulation can account for the consequences of the same AI application deployed in different contexts.

In spite of incidents of inaccuracy and bias, agencies like Artificial Intelligence (AI) court judges are starting to get accepted. However, AI has a lot to learn before we allow it to judge our moral behavior. Ganes Kesari, Co-Founder and Head of Analytics at Gramener, tells The Sociable that right now AI is not ready to take decisions on cases, and even in the future, it would be better off in the court in an assistant's role. AI needs to acquire skills in'understanding' context and interpreting scenarios "Today, AI is more suited to play the role of a judicial assistant than that of a criminal judge. It is smart at processing details, summarizing cases and looking up references. It is not ready to take decisions on cases just as yet," he says.

Taylor Swift's lawyers threatened to sue Microsoft over the company's Tay chatbot. The Guardian reports that a new book by Microsoft president Brad Smith reveals lawyers for Taylor Swift weren't happy with the company using the name Tay for its chatbot. Microsoft's chatbot was originally designed to hold conversations with teenagers over social media networks, but Twitter users turned it into a racist chatbot in less than a day. Smith checked his emails during a vacation and found out that Taylor Swift's team was demanding a name change for the Tay chatbot. "An email had just arrived from a Beverly Hills lawyer who introduced himself by telling me: 'We represent Taylor Swift, on whose behalf this is directed to you.'"

Artificial Intelligence (AI) and cannabis: What has one got to do with the other? The answer is, "A lot," according to Michael Yorke, President and CEO of CROP Infrastructure Corporation based in Vancouver, Canada. CROP provides financing for cannabis growing land expansion and other needed services. It also provides turnkey greenhouse facilities, special growing and processing equipment and access to approved nutrients for select licensed cannabis producers in legal growing regions in Canada. "The use of AI in sensors and high-definition cameras can be used to keep track of and adjust multiple inputs in the growing environment such as water level, PH level, temperature, humidity, nutrient feed, light spectrum and CO2 levels," Yorke said.