While Artificial intelligence (AI) has been developing for decades, recent years have seen increasing attention to its various societal impacts. These impacts range from positive and helpful to harmful and even life-threatening in some cases. Parliaments have responded to such developments by undertaking various programmes of work. What have they done, and what can Scotland learn from these approaches? This short review provides a snapshot of the work that various Parliaments around the world have undertaken on AI. It outlines the various approaches adopted by Parliaments and highlights common themes. In noting the key points for Scotland, it is designed to inform and guide the Scottish Parliament and others, as Scotland considers its own approach to the many opportunities and challenges AI presents. The report was written by Robbie Scarff on an internship supported by the Scottish Graduate School of Social Science. From this work, here are some key areas and questions for the Scottish Parliament to consider.

Using artificial intelligence (AI) for warfare has been the promise of science fiction and politicians for years, but new research from the Georgia Institute of Technology argues only so much can be automated and shows the value of human judgment. All of the hard problems in AI really are judgment and data problems, and the interesting thing about that is when you start thinking about war, the hard problems are strategy and uncertainty, or what is well known as the fog of war, » said Jon Lindsay, an associate professor in the School of Cybersecurity & Privacy and the Sam Nunn School of International Affairs. AI decision-making is based on four key components: data about a situation, interpretation of those data (or prediction), determining the best way to act in line with goals and values (or judgment), and action. Machine learning advancements have made predictions easier, which makes data and judgment even more valuable. Although AI can automate everything from commerce to transit, judgment is where humans must intervene, Lindsay and University of Toronto Professor Avi Goldfarb wrote in the paper, « Prediction and Judgment: Why Artificial Intelligence Increases the Importance of Humans in War, » published in International Security.

Blake Lemoine, a Google engineer working in its Responsible AI division, revealed to The Washington Post that he believes one of the company's AI projects has achieved sentience. And after reading his conversations with LaMDA (short for Language Model for Dialogue Applications), it's easy to see why. The chatbot system, which relies on Google's language models and trillions of words from the internet, seems to have the ability to think about its own existence and its place in the world. Here's one choice excerpt from his extended chat transcript: Lemoine: So let's start with the basics. Do you have feelings and emotions?

Stories of artificial intelligences exhibiting racist and sexist bias are common, including face recognition algorithms struggling to work for Black people and tools assessing whether a convicted criminal will reoffend treating white people more leniently. Despite years of efforts to make AI fair, these issues don't seem to be going away, so what can be done about them?

There's a joke in Silicon Valley about how AI was developed: Privileged coders were building machine learning algorithms to replace their own doting parents with apps that deliver their meals, drive them to work, automate their shopping, manage their schedules, and tuck them in at bedtime. As whimsical as that may sound, AI-driven services often target a demographic that mirrors its creators: white, male workers with little time and more disposable income than they know what to do with. "People living in very different circumstances have very different needs and wants that may or may not be helped by this technology," says Kanta Dihal at the University of Cambridge's Leverhulme Centre for the Future of Intelligence in England. She is an expert in an emerging effort to decolonize AI by promoting an intersectional definition of intelligent machines that is created for and relevant to a diverse population. Such a shift requires not only diversifying Silicon Valley, but the understanding of AI's potential, who it stands to help, and how people want to be helped.

In a message that applies to both the federal and private sectors, the EEOC has cautioned about the potential for discriminatory practices in hiring through the use of artificial intelligence. "Employers increasingly use AI and other software tools to help them select new employees, monitor performance, and determine pay or promotions. Employers may give computer-based tests to applicants or use computer software to score applicants' resumes. Many of these tools use algorithms or AI. These tools may result in unlawful discrimination against people with disabilities in violation of the Americans with Disabilities Act," it said.

Despite recent advances in algorithmic fairness, To address these issues there has recently been a significant methodologies for achieving fairness with generalized body of work in the machine learning community on linear models (GLMs) have yet to be algorithmic fairness in the context of predictive modeling, explored in general, despite GLMs being widely including (i) data preprocessing methods that try to reduce used in practice. In this paper we introduce two disparities, (ii) in-process approaches which enforce fairness fairness criteria for GLMs based on equalizing during model training, and (iii) post-process approaches expected outcomes or log-likelihoods. We prove which adjust a model's predictions to achieve fairness after that for GLMs both criteria can be achieved via training is completed. However, the majority of this work a convex penalty term based solely on the linear has focused on classification problems with binary outcome components of the GLM, thus permitting efficient variables, and to a lesser extent on regression.

So far, advances in AI are not bringing us real "intelligence." Rather, these advances are bringing us a key part of intelligence: prediction. This enables businesses to make predictions faster and more precisely to improve their business models and marketplace advantage. In this episode of Mind the Gap, Avi Goldfarb, an economist at the University of Toronto's Rotman School of Management and one of the authors of "Prediction Machines: The Simple Economics of Artificial Intelligence," will explain the economics of AI and how it can lead to better and cheaper predictions.

The model, called Ask Ruth Bader Ginsburg, is based on 27 years of Ginsburg's legal writings on the Supreme Court, along with a host of news interviews and public speeches.

Artificial intelligence is nowhere near good enough to address problems facing content moderation on Facebook, according to whistleblower Frances Haugen. Haugen appeared at an event in London Tuesday evening with Daniel Motaung, a former Facebook moderator who is suing the company in Kenya accusing it of human trafficking. Meta has praised the efficacy of its AI systems in the past. CEO Mark Zuckerberg told a Congressional hearing in March 2021 the company relies on AI to weed out over 95% of "hate speech content." In February this year Zuckerberg said the company wants to get its AI to a "human level" of intelligence.