We propose a distributionally robust support vector machine with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion. We use a type-$\infty$ Wasserstein ambiguity set centered at the empirical distribution to model distributional uncertainty and derive an exact reformulation for worst-case unfairness measure. We establish that the model is equivalent to a mixed-binary optimization problem, which can be solved by standard off-the-shelf solvers. We further prove that the expectation of the hinge loss objective function constitutes an upper bound on the misclassification probability. Finally, we numerically demonstrate that our proposed approach improves fairness with negligible loss of predictive accuracy.

"Framing" involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman 1983). Differences in lexical framing, the focus of our work, can have large effects on peoples' opinions and beliefs. To make progress towards reframing arguments for positive effects, we create a dataset and method for this task. We use a lexical resource for "connotations" to create a parallel corpus and propose a method for argument reframing that combines controllable text generation (positive connotation) with a post-decoding entailment component (same denotation). Our results show that our method is effective compared to strong baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear.

Google's decision to fire its AI ethics leaders is a matter of "urgent public concern" that merits strengthening laws to protect AI researchers and tech workers who want to act as whistleblowers. That's according to a letter published by Google employees today in support of the Ethical AI team at Google and former co-leads Margaret Mitchell and Timnit Gebru, who Google fired two weeks ago and in December 2020, respectively. Firing Gebru, one of the best known Black female AI researchers in the world and one of few Black women at Google, drew public opposition from thousands of Google employees. It also led critics to claim the incident may have "shattered" Google's Black talent pipeline and signaled the collapse of AI ethics research in corporate environments. "We must stand up together now, or the precedent we set for the field -- for the integrity of our own research and for our ability to check the power of big tech -- bodes a grim future for us all," reads the letter published by the group Google Walkout for Change.

Jay "Sinatraa" Won, a professional "Valorant" player and a former MVP of the Overwatch League, has been suspended from his team after his ex-girlfriend accused him of sexual abuse and emotional manipulation Tuesday. The esports organization Sentinels, for which Won plays, announced the suspension Wednesday, shortly after Riot Games issued its own statement that Won would be suspended from the upcoming Valorant Masters competition pending an investigation.

Clearview AI has amassed a database of more than 3 billion photos of individuals by scraping sites such as Facebook, Twitter, Google and Venmo. It's bigger than any other known facial-recognition database in the U.S., including the FBI's. The New York company uses algorithms to map the pictures it stockpiles, determining, for example, the distance between an individual's eyes to construct a "faceprint." This technology appeals to law enforcement agencies across the country, which can use it in real time to help determine people's identities. It also has caught the attention of civil liberties advocates and activists, who allege in a lawsuit filed Tuesday that the company's automatic scraping of their images and its extraction of their unique biometric information violate privacy and chill protected political speech and activity.

Artificial intelligence (AI) is already embedded in a range of digital services. Voice assistants such as Alexa, car routing or content translation all involve machine learning – the most popular form of artificial intelligence technology. There are many warnings these days about AI, such as the ethics behind these machine driven decision systems or threats of automation and the loss of many jobs. Very little is reported about how artificial intelligence can improve public services and can have positive social impact. Smart algorithms combined with cloud computing power allow unprecedented forms of data analysis that would take much longer if humans were doing it.

This recent TechRadar article explores the evolution of AI technologies that could conceivably outperform humans in creative disciplines previously perceived as uniquely human. How should the law deal with liability for infringement of third party materials used in the creation of independent AI-generated outputs? Read more about the topics of discussion this article has raised, how intellectual property law might be affected by AI technologies and AI-generated output, and the benefits and uncertainties facing the field, in our Cookie Jar article here. Thanks to developments in AI, the days of the human creative may be numbered...

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. This means that neural networks can be able to do learn and process the data in the same way that the humans do. So there is need of huge data to help these neural networks to learn things and loads of resources to compute them internally. They can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. These layers contain a lot of internal layers that are designed to process the data.

Africa is home to the world's fastest growing population, which is expected to double by 2050. This growth is directly linked to the increase in demand for energy – indeed the African Energy Chamber projects that the continent's demand for power will keep rising between 4-5% per year, possibly doubling by 2050. A reversal of fortune for the world's unelectrified population is one of the Sustainable Development Goals of the United Nations (SDG7). African governments have traditionally relied on centralised grid expansion to improve electricity access. This requires significant capital expenditure and is often not time or cost effective, especially in rural areas where much of Africa's unelectrified population live. At the same time, the Paris Agreement enshrines the global aim to achieve Net Zero in the next 3 decades in order to meet the goal of keeping global temperature rise well below 2 degrees Celsius above pre-industrial levels.

A survey published by KPMG today suggests that a large number of organizations have increased their investments in AI during the pandemic to the point that executives are now concerned about moving too fast. In fact, most of the survey respondents cited a definite need for increased AI regulation. The survey covered 950 business decision-makers and/or IT decision-makers with at least a moderate amount of AI knowledge at companies with more than $1 billion in revenue. It finds AI technologies are most likely to be moderately to fully employed in industrial manufacturing (93%), financial services (84%), technology (83%), retail (81%), life sciences (77%), health care (67%), and government (61%) sectors. Survey respondents all cited the pandemic as a factor that drove increased adoption of AI in the last year, though the degree varied by sector from industrial manufacturing (72%) to technology (57%), retail (53%), government (44%), financial services (42%), and health care and life sciences (37%). Many respondents also noted that AI technology is moving too fast for their comfort in industrial manufacturing (55%), technology (49%), retail (49%), life sciences (47%), financial services (37%), government (37%), and health care (35%) sectors.