Contracting is a common activity, but it is one that few companies do efficiently or effectively. In fact, it has been estimated that inefficient contracting causes firms to lose between 5% to 40% of value on a given deal, depending on circumstances. But recent technological developments like artificial intelligence (AI) are now helping companies overcome many of the challenges to contracting. The main challenge firms face in contracting arises from the sheer number of contracts they must keep track of; these often lack uniformity and are difficult to organize, manage, and update. Most firms don't have a database of all the information in their contracts – let alone an efficient way to extract all that data – so there's no orderly and fast way to, for example, view complex outsourcing agreements or see how a certain clause is worded across different divisions.

We may already feel cozy about artificial intelligence making ordinary decisions for us in our daily life. From product and movie recommendations on Netflix and Amazon to friend suggestions on Facebook, tailored advertisements on Google search result pages and auto corrections in virtually every app we use, artificial intelligence has already become ubiquitous like electricity or running water. But what about profound and life-changing decisions like in the judiciary system when a person is sentenced based on algorithms he isn't even allowed to see. A few months ago, when Chief Justice John G. Roberts Jr. visited the Rensselaer Polytechnic Institute in upstate New York, Shirley Ann Jackson, president of the college, asked him "when smart machines, driven with artificial intelligences, will assist with courtroom fact-finding or, more controversially even, judicial decision-making?" The chief justice's answer was truly startling.

Drones turned into missiles, fake videos manipulating public opinion and automated hacking are just three of the threats from artificial intelligence in the wrong hands, experts have said. The Malicious Use of Artificial Intelligence report warns that AI is ripe for exploitation by rogue states, criminals and terrorists. Those designing AI systems need to do more to mitigate possible misuses of their technology, the authors said. And governments must consider new laws. Speaking to the BBC, Shahar Avin, from Cambridge University's Centre for the Study of Existential Risk, explained that the report concentrated on areas of AI that were available now or likely to be available within five years, rather than looking to the distant future.

We've co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others. AI challenges global security because it lowers the cost of conducting many existing attacks, creates new threats and vulnerabilities, and further complicates the attribution of specific attacks. Like our work on concrete problems in AI safety, we've grounded some of the problems motivated by the malicious use of AI in concrete scenarios, such as: persuasive ads generated by AI systems being used to target the administrator of a security systems; cybercriminals using neural networks and "fuzzing" techniques to create computer viruses with automatic exploit generation capabilities; malicious actors hacking a cleaning robot so that it delivers an explosives payload to a VIP; and rogue states using omniprescent AI-augmented surveillance systems to pre-emptively arrest people who fit a predictive risk profile. We're excited to start having this discussion with our peers, policymakers, and the general public; we've spent the last two years researching and solidifying our internal policies at OpenAI and are going to begin engaging a wider audience on these issues.

Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.

Interpretability is an extremely important criterion when a machine learning model is applied in areas such as medicine, financial markets, and criminal justice (e.g., see the discussion paper by Lipton ([18]), as well as references therein). Many complex models, such as random forests, kernel methods, and deep neural networks, have been developed and employed to optimize prediction accuracy, which can compromise their ease of interpretation. In this paper, we focus on instancewise feature selection as a specific approach for model interpretation. Given a machine learning model, instancewise feature selection asks for the importance scores of each feature on the prediction of a given instance, and the relative importance of each feature are allowed to vary across instances. Thus, the importance scores can act as an explanation for the specific instance, indicating which features are the key for the model to make its prediction on that instance.

A recent issue of a popular computing journal asked which laws would apply if a self-driving car killed a pedestrian. This paper considers the question of legal liability for artificially intelligent computer systems. It discusses whether criminal liability could ever apply; to whom it might apply; and, under civil law, whether an AI program is a product that is subject to product design legislation or a service to which the tort of negligence applies. The issue of sales warranties is also considered. A discussion of some of the practical limitations that AI systems are subject to is also included.

A new dating site intended for Trump-admirers seeking other Trump-admirers for romance, Trump.Dating, up until the last few days featured the image of a convicted sex offender, Barrett Riddleberger, alongside his wife Jodi on its homepage, wearing his-and-hers hats reading "Trump" and "Make America Great Again." Details of Riddleberger's past were first reported by North Carolina paper Greensboro News & Record, in an article published in 2014, and resurfaced this week after local news outlets looked into Riddleberger's history. Riddleberger was convicted of "indecent liberty with a child", a felony, in 1995, according to North Carolina Department of Public Safety records. Although the file doesn't describe the crime in detail, the conviction reportedly stemmed from a 1993 incident in which Riddleberger videotaped himself having sex with a 15-year-old girl when he was 25, according to the News & Record account. The site claims to "make dating great again" by "wrecking the dating game and giving like-minded Americans a chance to meet without the awkwardness that comes with the first conversation about politics," according to their website, and has attracted criticism for only allowing users to sign up as "straight men" or "straight women."

This resonates with me very personally as a minority founder in the face recognition space. So deeply in fact, that I actually wrote about my thoughts in an October 2016 article titled "Kairos' Commitment to Your Privacy and Facial Recognition Regulations" wherein I acknowledged the impact of the problem, and expressed Kairos' position on the importance of rectification. I felt then, and now, that it is our responsibility as a Face Recognition provider to respond to this research and begin working together as an industry to eliminate disparities. Because when people become distrustful of technology that can positively impact global culture, everyone involved has a duty to pay attention. Joy Buolamwini of the M.I.T. Media Lab has released research [1] on what she calls "the coded gaze", or, algorithmic bias.

The legislation lays out very specific thresholds for fraud. If a merchant's PSP's fraud rate is above these thresholds then the merchant will be required to challenge the user for another form of authentication. This will open up an opportunity for competitive advantage in the payments market for those PSPs whose fraud rates are below the threshold. For this reason, Ravelin grasped the opportunity and it has extended machine learning capabilities to PSPs to ensure their merchants are below the PSD2 fraud thresholds. The company's product Ravelin Enterprise uses machine learning models to score a merchant's every customer interaction for fraud risk, forming a complete picture of a shopper's risk profile before they reach checkout.