It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency. We consider two variants of decision making, where "good" decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting "most preferred" goals. We then define, for each variant and notion of "goodness" (corresponding to a number of existing notions in the literature), explanations in two formats, for justifying the selection of an alternative to audiences with differing needs and competences: lean explanations, in terms of goals satisfied and, for some notions of "goodness", alternative decisions, and argumentative explanations, reflecting the decision process leading to the selection, while corresponding to the lean explanations. To define argumentative explanations, we use assumption-based argumentation (ABA), a well-known form of structured argumentation. Specifically, we define ABA frameworks such that "good" decisions are admissible ABA arguments and draw argumentative explanations from dispute trees sanctioning this admissibility. Finally, we instantiate our overall framework for explainable decision-making to accommodate connections between goals and decisions in terms of decision graphs incorporating defeasible and non-defeasible information.

It is critical for governments, leaders, and decision makers to develop a firm understanding of the fundamental differences between artificial intelligence, machine learning, and deep learning. Artificial intelligence (AI) applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, and decision trees. AI recognizes patterns from vast amounts of quality data providing insights, predicting outcomes, and making complex decisions. Machine learning (ML) is a subset of AI that utilises advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon's Alexa and Apple's Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.

Our previous blog posts, Artificial Intelligence as the Inventor of Life Sciences Patents? and Update on Artificial Intelligence: Court Rules that AI Cannot Qualify As "Inventor," discuss recent inventorship issues surrounding AI and its implications for life sciences innovations. Continuing our series, we now look at the appeal recently filed by Stephen Thaler ("Thaler") in his quest to obtain a patent for an invention created by AI in the absence of a traditional human inventor. As we previously reported, on September 3, 2021, the U.S. District Court for the Eastern District of Virginia ruled that an AI machine cannot qualify as an "inventor" under the Patent Act, in a case that Thaler filed seeking, among other things, an order compelling the USPTO to reinstate his patent applications. Those patent applications name an AI system called "Device for Autonomous Bootstrapping of Unified Sentience" aka "DABUS," as the sole inventor. Thaler, who developed DABUS, remains the owner of any patent rights stemming from these applications.

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Our approach is to (a) develop a methodology for an external audit of stability of predictions made by algorithmic personality tests, and (b) instantiate this methodology in an audit of two systems, Humantic AI and Crystal. Crucially, rather than challenging or affirming the assumptions made in psychometric testing -- that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job -- we frame our methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. In our audit of Humantic AI and Crystal, we find that both systems show substantial instability with respect to key facets of measurement, and so cannot be considered valid testing instruments. For example, Crystal frequently computes different personality scores if the same resume is given in PDF vs. in raw text format, violating the assumption that the output of an algorithmic personality test is stable across job-irrelevant variations in the input. Among other notable findings is evidence of persistent -- and often incorrect -- data linkage by Humantic AI.

As artificial intelligence plays an increasingly important role in the R&D process, the premise that invention is a uniquely human characteristic is being challenged. Patent offices and courts around the world have recently been grappling with the question of whether an AI system can be the inventor of a patent. This has been prompted by Dr. Stephen Thaler's applications to designate his AI system (known as'DABUS') as the inventor of patents filed in multiple jurisdictions. Most recently, the appeal board of the European Patent Office (EPO) refused Dr. Thaler's patent applications because there was no valid inventor. Dr. Thaler, as part of the Artificial Inventor Project, is pursuing parallel patent applications across over fifteen jurisdictions which designate his AI system, DABUS, as the inventor.

Gibson Dunn lawyers examine a number of the most significant developments from 2021 affecting companies as they navigate the evolving AI and autonomous systems landscape.

The success of Microsoft's biggest deal ever rides on rehabilitating Activision Blizzard's culture, Microsoft CEO Satya Nadella declared after announcing the $69 billion transaction. Accomplishing that will require Microsoft to deviate from its usual hands-off approach on acquisitions to tackle what amounts to a "clean up" job of fixing the famed maker of the Call of Duty games franchise, which faces multiple accusations of sexual harassment and misconduct, analysts and management experts say. Microsoft has traditionally allowed the companies it acquires to run autonomously, RBC Capital Markets analyst Rishi Jaluria said. In recent years, Microsoft purchased LinkedIn, GitHub, Skype and Mojang, the Stockholm-based creator of the video game series Minecraft, all of which have not seen major changes since their acquisitions. The Activision deal announced on Tuesday will require a heavier hand.

Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of this work is to publicly provide labeled datasets and exhaustive lexicons by collecting, annotating, and augmenting relevant sentences to facilitate the detection of gender bias in English text. Towards this end, we present an updated version of our previously proposed taxonomy by re-formalizing its structure, adding a new bias type, and mapping each bias subtype to an appropriate detection methodology. The released datasets and lexicons span multiple bias subtypes including: Generic He, Generic She, Explicit Marking of Sex, and Gendered Neologisms. We leveraged the use of word embedding models to further augment the collected lexicons. The underlying motivation of our work is to enable the technical community to combat gender bias in text and halt its propagation using ML and NLP techniques.

Federal regulators have also recently homed in on Autopilot over reports of crashes while it was activated. Over the summer the National Highway Traffic Safety Administration, the country's top federal auto safety regulator, launched a formal probe into a dozen crashes involving parked emergency vehicles while Autopilot was active. One person was killed and at least 17 people were injured in the crashes.

AI bias is already harming businesses and there's significant appetite for more regulation to help counter the problem. The findings come from the State of AI Bias report by DataRobot in collaboration with the World Economic Forum and global academic leaders. The report involved responses from over 350 organisations across industries. "DataRobot's research shows what many in the artificial intelligence field have long-known to be true: the line of what is and is not ethical when it comes to AI solutions has been too blurry for too long. The CIOs, IT directors and managers, data scientists, and development leads polled in this research clearly understand and appreciate the gravity and impact at play when it comes to AI and ethics."