It was the veritable search for a needle in a haystack. With drug-resistant bacteria on the rise, researchers at MIT were sifting through a database of more than 100 million molecules to identify a few that might have antibacterial properties. Fortunately, the search proved successful. But it wasn't a human who found the promising molecules. It was a machine learning program.

Depending on their design and use in the workplace, digital technologies such as artificial intelligence, advanced robots and sensor technologies impact on many areas of working conditions, raising new ethical concerns about workers' fundamental rights. It will be critical to ensure policies on ethical issues also consider quality aspects of work and not just legal and compliance issues. COVID-19 may have boosted the use of digital technologies in the workplace, in some cases with little consideration for the ethical implications. Technologies used to limit the spread of COVID-19 in the workplace may be redeployed for other purposes, such as enhanced monitoring and surveillance of workers. It is therefore crucial to re-assess the ethical implications of the use of digital technologies both in terms of job quality and workers' rights.

The day is coming--some say has already arrived--when artificial intelligence starts to invent things that its human creators could not. But our laws are lagging behind this technology, UNSW experts say. It's not surprising these days to see new inventions that either incorporate or have benefitted from artificial intelligence (AI) in some way, but what about inventions dreamt up by AI--do we award a patent to a machine? This is the quandary facing lawmakers around the world with a live test case in the works that its supporters say is the first true example of an AI system named as the sole inventor. In commentary published in the journal Nature, two leading academics from UNSW Sydney examine the implications of patents being awarded to an AI entity.

If business wants to use AI at scale, adhering to the technical guidelines for responsible AI development isn't enough. It must obtain society's explicit approval to deploy the technology. Six years ago, in March 2016, Microsoft Corporation launched an experimental AI-based chatbot, TayTweets, whose Twitter handle was @TayandYou. Tay, an acronym for "thinking about you," mimicked a 19-year-old American girl online, so the digital giant could showcase the speed at which AI can learn when it interacts with human beings. Living up to its description as "AI with zero chill," Tay started off replying cheekily to Twitter users and turning photographs into memes. Some topics were off limits, though; Microsoft had trained Tay not to comment on societal issues such as Black Lives Matter. Soon enough, a group of Twitter users targeted Tay with a barrage of tweets about controversial issues such as the Holocaust and Gamergate. They goaded the chatbot into replying with racist and sexually charged responses, exploiting its repeat-after-me capability. Realizing that Tay was reacting like IBM's Watson, which started using profanity after perusing the online Urban Dictionary, Microsoft was quick to delete the first inflammatory tweets. Less than 16 hours and more than 100,000 tweets later, the digital giant shut down Tay.

Artificial Intelligence (AI) systems have been involved in numerous scandals in recent years. For instance, take the COMPAS recidivism algorithm. The algorithm evaluated the likelihood that defendants will commit another crime in the future. It was widely used in the US criminal justice system to inform decisions about who can be set free at all stages of the process. In 2016, ProPublica exposed that COMPAS's predictions were biased: its mistakes favored white over black defendants. Black defendants were twice as likely to be labeled as high risk to reoffend but not actually reoffend.

While the European Union is playing the long game in drafting regulation for AI, China has been surprising many with quick yet profound regulations. A report compares the situation for both jurisdictions. On a different track, the U.S. government looks at ways to improve its AI research infrastructure. The European Union has a long-standing reputation for regulating many facets of life. Its GDPR has been something of a global hit. With its upcoming AI Act it attempts to safeguard human rights and society generally.

The use of artificial intelligence in the workplace has been at the center of the Equal Employment Opportunity Commission's recent guidance on …

The data associated with machine learning can be extremely valuable, but, Kimberley Bayliss of Haseltine Lake Kempner writes in this co-edited article, before it can be monetized, there are some major issues to be resolved. One of the things I hear over and over again from inventors is that data is the most valuable asset in machine learning (ML). After all, an ML model is only as good as the quality and quantity of data on which it is trained. If data is really that valuable, the burning question is whether it can be successfully protected and monetized. Just as employees must be aware when they access a trade secret, and the responsibilities that come with it, employees must also be aware of their responsibilities when accessing and using company data.

Significant investments and research developments in artificial intelligence (AI) have made the technology a powerful asset in many industries -- including environmental studies. AI isn't a new technology, but businesses and consumers feel its impact and witness it seep into everyday life. AI is becoming more advanced and autonomous, and it's also broader in its use and impact. More use cases for AI are emerging, and if implemented responsibly, it can greatly benefit society. It's likely to play a role in tackling issues like climate change -- but how? Here's how AI is expected to impact the environment and usher in positive changes for a more sustainable future. It's critical to understand the breadth of environmental problems right now.

Data protection authorities (DPAs) broadly believe the regulation's underlying principles of lawfulness, fairness, and transparency make it "future-proof" to cover developments in artificial intelligence (AI), machine learning, cloud computing, and data in a way its predecessor, the 1995 EU Data Protection Directive, failed to do. Many legal experts also believe the GDPR is flexible enough to cope with emerging technologies. Will Richmond-Coggan, director and a specialist in data protection and new technology at law firm Freeths, said, "Although it is often presented as a conflict, the reality is there is very little which technology might make possible that the (U.K. or EU) GDPR would outright prohibit." James Castro-Edwards, privacy and cyber counsel at law firm Arnold & Porter, said, "While the GDPR may not have been drafted with these new technologies specifically in mind, the broad principles of lawfulness, fairness, and transparency still apply, along with a number of additional requirements for higher risk processing." "When data protection rules are difficult to apply in practice, organizations can fall into the trap of believing that avoiding them is a pragmatic approach."