On the heels of a National Institute of Standards and Technology (NIST) study on demographic differentials of biometric facial recognition accuracy, Karen Hao, an artificial intelligence authority and reporter for MIT Technology Review, recently explained that "bias can creep in at many stages of the [AI] deep-learning process" because "the standard practices in computer science aren't designed to detect it." "Fixing discrimination in algorithmic systems is not something that can be solved easily," explained Andrew Selbst, a post-doctoral candidate at the Data & Society Research Institute, and lead author of the recent paper, Fairness and Abstraction in Sociotechnical Systems. "A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process," the paper's authors, which include Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi, noted, adding that "(b)edrock concepts in computer science – such as abstraction and modular design – are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce'fair' outcomes." "However," they pointed out, "we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five'traps' that fair-ML work can fall into, even as it attempts to be more context-aware in comparison to traditional data science."

Existing intellectual property laws don't allow AI systems to be recognized as inventors, which threatens the integrity of the patent system and the potential to develop life-changing innovations. Current legislation only allows humans to be recognized as inventors, which could make AI-generated innovations unpatentable. This would deprive the owners of the AI of the legal protections they need for the inventions that their systems create. The Artificial Inventor Project team has been testing the limitations of these rules by filing patent applications that designate a machine as the inventor-- the first time that an AI's role as an inventor had ever been disclosed in a patent application. They made the applications on behalf of Dr Stephen Thaler, the creator of a system called DABUS, which was listed as the inventor of a food container that robots can easily grasp and a flashing warning light designed to attract attention during emergencies.

Calibration and equal error rates are fundamental conditions for algorithmic fairness that have been shown to conflict with each other, suggesting that they cannot be satisfied simultaneously. This paper shows that the two are in fact compatible and presents a method for reconciling them. In particular, we derive necessary and sufficient conditions for the existence of calibrated scores that yield classifications achieving equal error rates. We then present an algorithm that searches for the most informative score subject to both calibration and minimal error rate disparity. Applied empirically to credit lending, our algorithm provides a solution that is more fair and profitable than a common alternative that omits sensitive features.

There is increasing regulatory interest in whether machine learning algorithms deployed in consequential domains (e.g. in criminal justice) treat different demographic groups "fairly." However, there are several proposed notions of fairness, typically mutually incompatible. Using criminal justice as an example, we study a model in which society chooses an incarceration rule. Agents of different demographic groups differ in their outside options (e.g. opportunity for legal employment) and decide whether to commit crimes. We show that equalizing type I and type II errors across groups is consistent with the goal of minimizing the overall crime rate; other popular notions of fairness are not.

IFLA has shared its comments on a draft issues paper prepared by the World Intellectual PropertyOrganisation about artificial intelligence. Artificial intelligence is the subject of more and more political attention around the world. With growing awareness of both its potential applications, and the risks that it can bring, there are calls for government intervention, both at the national and global levels. While much of the focus on artificial intelligence comes from the ethical perspective – including in the library field – there are also potentially importance implications for intellectual property (IP) policy. At least stage, there is an angle for libraries who have a mission both to help users carry out research, and a broader focus on supporting individual autonomy in an informed society.

The world never changes quite the way you expect. But at The Verge, we've had a front-row seat while technology has permeated every aspect of our lives over the past decade. Some of the resulting moments -- and gadgets -- arguably defined the decade and the world we live in now. But others we ate up with popcorn in hand, marveling at just how incredibly hard they flopped. This is the decade we learned that crowdfunded gadgets can be utter disasters, even if they don't outright steal your hard-earned cash. It's the decade of wearables, tablets, drones and burning batteries, and of ridiculous valuations for companies that were really good at hiding how little they actually had to offer. Here are 84 things that died hard, often hilariously, to bring us where we are today. Everyone was confused by Google's Nexus Q when it debuted in 2012, including The Verge -- which is probably why the bowling ball of a media streamer crashed and burned before it even came to market.

Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore - and it's just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents.

Nearly a decade into his life sentence for murder, Lydell Grant was escorted out of a Texas prison in November with his hands held high, free on bail, all thanks to DNA re-examined by a software program. "The last nine years, man, I felt like an animal in a cage," Grant, embracing his mother and brother, told the crush of reporters awaiting him in Houston. "Especially knowing that I didn't do it." Now, Grant, 42, is on a fast-track to exoneration after a judge recommended in December that Texas' highest criminal court vacate his conviction. His attorneys are hopeful a ruling is made in the coming weeks.

Last week I had the privilege to serve as a panelist at a Northwestern Pritzker School of Law event entitled "Symposium 2020: AI, the New Law Firm Attorney: Artificial Intelligence Entering the Legal Profession." The event's keynote speaker Seyfarth Shaw Chair Emeritus Stephen Poor and our panel explored the growing impact of Artificial Intelligence (AI) tools in the legal industry to help lawyers achieve more by getting out of the repetitive, routine and mundane tasks that lawyers have performed in the past so they can "practice at the top of their license" as Mr. Poor stated. I love the phrase "practice at the top of their license" as all lawyers will need to do more of this – especially as we see the rise of tech intensity as technology plays a bigger role in our professional and personal lives and leading technology like AI is increasingly used by lawyers, law firms and other legal organizations to deliver legal services to their clients. As AI tools seek to automate and perform certain tasks that have been traditionally performed by lawyers, I believe that the Emotional Intelligence or Emotional Quotient (EQ) skills that lawyers use everyday to deliver legal services to their clients will be more important than ever before as stronger EQ skills will help enable lawyers to truly "practice at the top of their license." Since AI, algorithms, machines and technology do not embrace EQ, the proverbial "soft skills" that are often associated with EQ can help lawyers provide even more high-impact/high-value legal counsel to their clients and differentiate their legal services from others.