Researchers at the Allen Institute for AI created Ask Delphi to make ethical judgments — but it turned out to be awfully bigoted and racist instead.

With the drafting of the "Artificial Intelligence Act" (April 2021), the European Commission has made its first attempt at comprehensively regulating the expansive world of AI. Whilst the draft legislation extensively addresses the regulation and classification of AI technology, it does not mention another area of concern regarding Artificial Intelligence, namely intellectual property rights. Identifying IP rights as a major issue, the EU Parliament adopted a resolution on IP rights for the development of AI technologies in October 2020. In it, the Parliament called upon the Commission to ensure a high level protection of intellectual property rights when regulating AI. Despite the report being forwarded to the Commission well before it finalized its proposal for the "Artificial Intelligence Act", the protection of intellectual property rights is not mentioned in the draft legislation. Merely an Annex published alongside it briefly mentions the challenges of protecting intellectual property rights in connection with AI-assisted outputs.

The company and law firm names shown above are generated automatically based on the text of the article. We are improving this feature as we continue to test and develop in beta. We welcome feedback, which you can provide using the feedback tab on the right of the page. October 22, 2021 - Imagine this. You are President of the United States.

The European Union (EU) has launched the world's first comprehensive legislative package to regulate AI. The Artificial Intelligence Act (AIA), which is currently progressing through the EU legislative process, will establish a risk-based framework for regulating use of AI anywhere within the EU, including by companies based outside the EU. A limited number of unacceptable AI use cases, such as social profiling by governments, would be completely banned; high-risk use cases would be subjected to prior conformity assessment and wide-ranging new compliance obligations; medium risk functions are subject to enhanced transparency rules, and low-risk use cases can largely be pursued without any new obligations under the AIA. By legislating now, the EU hopes to establish a de facto global standard for AI. The EU is certainly well ahead of the US in this area, with debate in the US more focused on the extent to which the US may be falling behind China in military applications of AI, although some think tanks are looking at the ethics of AI and new state privacy laws have tasked regulators to develop standards for transparency and choice.

In brief A man was detained in Japan for selling uncensored pornographic content that he had, in a way, depixelated using machine-learning tools. Masayuki Nakamoto, 43, was said to have made about 11 million yen ($96,000) from peddling over 10,000 processed porn clips, and was formally accused of selling ten hardcore photos for 2,300 yen ($20). Explicit images of genitalia are forbidden in Japan, and as such its porn is partially pixelated. Don't pretend you don't know what we're talking about. Nakamato flouted these rules by downloading smutty photos and videos, and reportedly used deepfake technology to generate fake private parts in place of the pixelation.

As a technical sub-field of artificial intelligence (AI), explainable AI (XAI) has produced a vast collection of algorithms, providing a toolbox for researchers and practitioners to build XAI applications. With the rich application opportunities, explainability has moved beyond a demand by data scientists or researchers to comprehend the models they are developing, to become an essential requirement for people to trust and adopt AI deployed in numerous domains. However, explainability is an inherently human-centric property and the field is starting to embrace human-centered approaches. Human-computer interaction (HCI) research and user experience (UX) design in this area are becoming increasingly important. In this chapter, we begin with a high-level overview of the technical landscape of XAI algorithms, then selectively survey our own and other recent HCI works that take human-centered approaches to design, evaluate, provide conceptual and methodological tools for XAI. We ask the question "\textit{what are human-centered approaches doing for XAI}" and highlight three roles that they play in shaping XAI technologies by helping navigate, assess and expand the XAI toolbox: to drive technical choices by users' explainability needs, to uncover pitfalls of existing XAI methods and inform new methods, and to provide conceptual frameworks for human-compatible XAI.

Unlike the courts in western countries, public records of Indian judiciary are completely unstructured and noisy. No large scale publicly available annotated datasets of Indian legal documents exist till date. This limits the scope for legal analytics research. In this work, we propose a new dataset consisting of over 10,000 judgements delivered by the supreme court of India and their corresponding hand written summaries. The proposed dataset is pre-processed by normalising common legal abbreviations, handling spelling variations in named entities, handling bad punctuations and accurate sentence tokenization. Each sentence is tagged with their rhetorical roles. We also annotate each judgement with several attributes like date, names of the plaintiffs, defendants and the people representing them, judges who delivered the judgement, acts/statutes that are cited and the most common citations used to refer the judgement. Further, we propose an automatic labelling technique for identifying sentences which have summary worthy information. We demonstrate that this auto labeled data can be used effectively to train a weakly supervised sentence extractor with high accuracy. Some possible applications of this dataset besides legal document summarization can be in retrieval, citation analysis and prediction of decisions by a particular judge.

The University of California has created recommendations to create a path toward the responsible use of artificial intelligence in future UC endeavors. UC's increasing dependence on the use of AI has increased its overall productivity as an institution, according to the UC Office of the President, or UCOP. However, with the implementation of AI, there is also potential for problems to arise. To combat this, former UC President Janet Napolitano and current president Michael Drake created the Presidential Working Group on Artificial Intelligence, or the Working Group, in August 2020. The Working Group's final report noted that the group consists of 32 faculty and staff from all 10 UC campuses and an additional number of representatives from UC Legal and the Office of Ethics, Compliance and Audit Services, among other groups.

The limitations of the models are summarized in Figure 12. Based on the models for Reddit and Twitter, for binary classification, the limitation is the models are over-fitting due to the class imbalance although it gave better accuracy. Furthermore, the war-crime examples seem to share quite similar texts, therefore lack diversity. This is another potential reason for which our models have decent performances, despite the dataset being small. The future scope for binary models is to find more efficient ways to clean the texts.

The EU's AI Act promises to mitigate the harmful use of machine intelligence but will require deeper education and evangelization to ensure more transparent and ethical use of AI, notes Ursula Morgenstern, Cognizant's President of Global Growth Markets. With new regulations proposed, AI ethics -- like data privacy -- has become a top priority for companies. As heads of state from European Union member nations begin to take up discussion of the EU's proposed Artificial Intelligence (AI) Act, companies are exploring what it will take to adhere to one of the first major policy initiatives focused on harmful AI. The answer is clear: Compliance will require business to educate and evangelize across their organizations. While it will likely take two years for these new rules to come into effect, it's not too soon to begin preparing.