Though artificial intelligence is changing how businesses work, there are concerns about how it may influence our lives. This is not just an academic or a societal concern but a reputational risk for companies, no company wants to be marred with data or AI ethics scandals that impacted companies like Amazon. For example, there was significant backlash due to the sale of Rekognition to law enforcement. This was followed by Amazon's decision to stop providing this technology to law enforcement for a year since they anticipate the proper legal framework to be in place by then. Al algorithms and training data may contain biases as humans do since those are also generated by humans.

Just ask any marketing person--it's their job to keep demand for a product or service high. So they depend on advertising and other methods to create brand recognition and a sense of demand for what they sell. These days marketing firms are even more clever, recruiting social media influencers who promote a product or service directly or indirectly--sometimes without disclosing that they are a paid lackey. We're getting better at influencing humans, either by using traditional advertising methods, such as keyword advertising, or, even scarier, by leveraging AI technology as a way to change hearts and minds. Often "the targets" don't even understand that their hearts and minds are being changed.

In attempting to characterize the acquisition and policy implications of the application of AI & ML to a government context, instances of both actual and potential issues and consequences arising from such applications were researched and identified. In this context, implications are known current effects, as well as possible future effects of the use of these technologies across a number of different identified domains where those effects become manifest. Some of these implications are primary effects that occur as a direct result of the application of the technology (e.g., the need to review the ethics used in autonomous decision-making by AI & ML), while others are secondary effects that occur as a result of a primary effect (e.g., the need to access data that will then be used to train supervised ML).

With the advent of digital technology, it is more common that committed crimes or legal disputes involve some form of speech recording where the identity of a speaker is questioned [1]. In face of this situation, the field of forensic speaker identification has been looking to shed light on the problem by quantifying how much a speech recording belongs to a particular person in relation to a population. In this work, we explore the use of speech embeddings obtained by training a CNN using the triplet loss. In particular, we focus on the Spanish language which has not been extensively studies. We propose extracting the embeddings from speech spectrograms samples, then explore several configurations of such spectrograms, and finally, quantify the embeddings quality.

The EU's attempts to regulate Artificial Intelligence could be met with future challenges resulting from an agreement on e-Commerce at the level of the World Trade Organisation (WTO), according to a new study published on Tuesday (26 January). Talks have been ongoing since January 2019 between members of the WTO in a bid to agree on global rules to facilitate worldwide e-commerce transactions. However, concerns have been highlighted that the text currently backed by the EU could result in a prohibition on signatories from adopting legislation that obliges firms to provide access to the source code of their software. In this vein, a report published by the Federation of German Consumer Organisations (vzbv) says that a number of EU objectives in the field of digital policy currently on the table could be stifled by the WTO agreement. "The EU's possibility to adopt rules that, for example, mandate external audits of AI systems will be confined to the policy space that is allowed under trade law," the study notes, adding that the European Council and the Commission are responsible for ensuring that trade deals it makes are compatible with internal policy initiatives.

It's clear these newer entrants to the legal tech space are not afraid of embracing AI in order to drive efficiency and growth, particularly when that means that they can cut costs by bringing legal work in house.

In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer technical, psychological and legal benefits over other explanation techniques. We survey 100 distinct counterfactual explanation methods reported in the literature. This survey addresses the extent to which these methods have been adequately evaluated, both psychologically and computationally, and quantifies the shortfalls occurring. For instance, only 21% of these methods have been user tested. Five key deficits in the evaluation of these methods are detailed and a roadmap, with standardised benchmark evaluations, is proposed to resolve the issues arising; issues, that currently effectively block scientific progress in this field.

Who are the inventors of patents? Since George Washington signed the first patent in 1790, the United States has issued patents to people of various ages, ethnicities, and genders, with some patent inventors being as young as two when they filed[1]. The varied backgrounds of these inventors stems from the United States Patent and Trademark Office's ("USPTO") broad definition of an inventor, laying out an inventor to "mean[] the individual or, if a joint invention, the individuals collectively who invented or discovered the subject matter the invention."[2] But what happens when the inventor is a machine? This is the exact issue Dr. Stephen Thaler sought to resolve with the USPTO as well as other worldwide patent offices.

Algorithmic fairness is a motif that plays throughout our podcast series: as we look to AI to help us make consequential decisions involving people, guests have stressed the risks that the automated systems that we build will encode past injustices and that these decisions may be too opaque. In episode twelve of the Intel on AI podcast, Intel AI Tech Evangelist and host Abigail Hing Wen talks with Alice Xiang, then Head of Fairness, Transparency, and Accountability Research at the Partnership on AI--a nonprofit in Silicon Valley founded by Amazon, Apple, Facebook, Google, IBM, Intel and other partners. With a background that includes both law and statistics, Alice's research has focused on the intersection of AI and the law. "A lot of the benefit of algorithmic systems, if used well, would be to help us detect problems rather than to help us automate decisions." Algorithmic fairness is the study of how algorithms might systemically perform better or worse for certain groups of people and the ways in which historical biases or other systemic inequities might be perpetuated by AI.

Remember that artificial intelligence-worshipping church, the Way of the Future? Well, first of all: Yes, that existed. But secondly, founder Anthony Levandowski told TechCrunch this week that he has now decided to dissolve the church and donate all of its funds -- just over $175,000 -- to the NAACP Legal Defense and Education Fund. Levandowski still supports the church's mission to responsibly develop and support artificial general intelligence, but he said he was inspired by the Black Lives Matter movement to do something with a more immediate impact. "I wanted to donate to the NAACP Legal Defense and Education Fund because it's doing really important work in criminal justice reform and I know the money will be put to good use," Levandowski told TechCrunch.