Allan Jones has seen the challenges of running a small business firsthand. When he was 14, his father was sued for wrongful termination by a former employee of his Compton, California mini-market. Without the guidance of a human resources department or the finances to fight the suit, he was forced to hire an attorney and dip into Jones' college savings to pay the fees. This experience stuck with Jones, and in 2016 inspired him to found Bambee, a Los Angeles-based company that pairs HR managers with small and midsize businesses on a monthly basis. "I knew that small businesses did not have HR, and the primary reason was price," says Jones, 34.

AI ethics is a sub-field of applied ethics, focusing on the ethical issues raised by the development, deployment and use of AI. Its central concern is to identify how AI can advance or raise concerns to the good life of individuals, whether in terms of quality of life, or human autonomy and freedom necessary for a democratic society. As with any powerful technology, the use of AI systems in our society raises several ethical challenges, for instance relating to their impact on people and society, decision-making capabilities, and safety. If we are increasingly going to use the assistance of or delegate decisions to AI systems, we need to make sure these systems are fair in their impact on people's lives, that they are in line with values that should not be compromised and able to act accordingly, and that suitable accountability processes can ensure this. Public anxiety over possible problems has led many nongovernment academic, corporate organizations to put forward declarations on the need to protect basic human rights in artificial intelligence and machine learning.

By using the power of Reveal Data's first class neural-net AI, along with the data and know-how available within a global law firm like DLA Piper, the AI has been taught to spot these cartel risks and enable our lawyers to quickly run a review and advise clients of their cartel risks. Specifically, Aiscension has been trained to uncover the following forms of cartel behavior: price fixing; bid rigging; market sharing; collective boycotts and exchanging competitively sensitive information.

Stakeholders who have had to get to grips with the GDPR will find many of the concepts in the Regulation familiar. From the risk-based approach, to the requirements around transparency and information provision as well as record-keeping, territorial scope and enforcement, cybersecurity and data governance, there are recognisable requirements. The Regulation defines an AI system as "software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations or decisions influencing the environments they interact with". The Regulation takes a risk-based approach to AI systems. Some types of AI as set out in Title II, are considered to carry unacceptable risk and are prohibited.

In wake of several mass shootings across the country, organizations are looking for ways to be able to stop these situations before they get a chance to happen. Artificial intelligence that can detect the presence of firearms in public places and automatically lock potential shooters out of buildings sounds futuristic, but the technology is already here. Defendry is a company working on optimizing and spreading this software to keep the public safe. The idea behind the AI centers around preventing a shooter from being able to get to large groups of people, all while giving police and nearby security live updates on the shooter's description and location. "If you can keep someone out of the building, you've just mitigated a huge potential for further damage. Further deaths," said Pat Sullivan, founder and CEO of Defendry.

The U.S. Federal Trade Commission just fired a shot across the bow of the artificial intelligence industry. On April 19, 2021, a staff attorney at the agency, which serves as the nation's leading consumer protection authority, wrote a blog post about biased AI algorithms that included a blunt warning: "Keep in mind that if you don't hold yourself accountable, the FTC may do it for you." The post, titled "Aiming for truth, fairness, and equity in your company's use of AI," was notable for its tough and specific rhetoric about discriminatory AI. The author observed that the commission's authority to prohibit unfair and deceptive practices "would include the sale or use of – for example – racially biased algorithms" and that industry exaggerations regarding the capability of AI to make fair or unbiased hiring decisions could result in "deception, discrimination – and an FTC law enforcement action." Bias seems to pervade the AI industry.

AI governance is therefore also a risk mitigation exercise, as adverse impacts show up as risks to brand, customer trust and legal liability. Most companies do not have the staying power to pull their products off the market voluntarily in order to respond to concerns (for instance as Microsoft, Amazon or IBM did in connection with the use of facial recognition technologies by law enforcement).

In the legal world, and in particular the world of electronic discovery, artificial intelligence (AI) has been around for more than a decade. It is no longer unusual or controversial for organizations to use AI technologies in litigation, especially where large or complex data sets are involved. Legal teams now routinely turn to AI to defensibly accelerate the process of identifying documents likely to be responsive to requests for evidence. Innovations like technology assisted review (TAR), for example, rely heavily on machine learning and natural language processing to make connections and identify patterns within a body of data in a matter of seconds. This is work that would take even the most qualified human reviewers many, many hours to do manually, and with less accuracy.

We present the Zero Resource Speech Challenge 2021, which asks participants to learn a language model directly from audio, without any text or labels. The challenge is based on the Libri-light dataset, which provides up to 60k hours of audio from English audio books without any associated text. We provide a pipeline baseline system consisting on an encoder based on contrastive predictive coding (CPC), a quantizer ($k$-means) and a standard language model (BERT or LSTM). The metrics evaluate the learned representations at the acoustic (ABX discrimination), lexical (spot-the-word), syntactic (acceptability judgment) and semantic levels (similarity judgment). We present an overview of the eight submitted systems from four groups and discuss the main results.

Natural Language Processing (NLP) and Information Retrieval (IR) in the judicial domain is an essential task. With the advent of availability domain-specific data in electronic form and aid of different Artificial intelligence (AI) technologies, automated language processing becomes more comfortable, and hence it becomes feasible for researchers and developers to provide various automated tools to the legal community to reduce human burden. The Competition on Legal Information Extraction/Entailment (COLIEE-2019) run in association with the International Conference on Artificial Intelligence and Law (ICAIL)-2019 has come up with few challenging tasks. The shared defined four sub-tasks (i.e. Task1, Task2, Task3 and Task4), which will be able to provide few automated systems to the judicial system. The paper presents our working note on the experiments carried out as a part of our participation in all the sub-tasks defined in this shared task. We make use of different Information Retrieval(IR) and deep learning based approaches to tackle these problems. We obtain encouraging results in all these four sub-tasks.