The Institutional Analysis and Development (IAD) framework is a conceptual toolbox put forward by Elinor Ostrom and colleagues in an effort to identify and delineate the universal common variables that structure the immense variety of human interactions. The framework identifies rules as one of the core concepts to determine the structure of interactions, and acknowledges their potential to steer a community towards more beneficial and socially desirable outcomes. This work presents the first attempt to turn the IAD framework into a computational model to allow communities of agents to formally perform what-if analysis on a given rule configuration. To do so, we define the Action Situation Language -- or ASL -- whose syntax is hgighly tailored to the components of the IAD framework and that we use to write descriptions of social interactions. ASL is complemented by a game engine that generates its semantics as an extensive-form game. These models, then, can be analyzed with the standard tools of game theory to predict which outcomes are being most incentivized, and evaluated according to their socially relevant properties.

Big tech companies still love to tout artificial intelligence systems as an innovative solution to endemic human bias and racism... all despite numerous reports and extensive analysis repeatedly refuting its effectiveness. AI-driven insurance claims service, Lemonade, has apparently not seen any of this evidence to the contrary, however. In fact, the company has managed to make a recent social media snafu even worse by trying to walk back boasts of its AI's supposed ability to detect incriminating "non-verbal cues" and other possible indicators of fraud. In doing so, the insurer hasn't just enraged its users, it's directly contradicted its own SEC filings. And it's made it sound a lot like it's using AI for the insurance equivalent of phrenology.

Developments in AI entail incredible progress for many fields in the near future. AI is increasingly influencing the opinions and behaviour of people in everyday life, and it is no longer merely confined to industry but can be found in many other fields, such as healthcare, education and retail environments. Nevertheless, the introduction and implementation of AI in society raises a variety of ethical, legal and societal concerns, and within this context there are still many areas in which there is substantial room for improvement. A more specific practical example of such room for improvement can be found in the fact that algorithms, among which automated gender recognition systems, do not always account for diversity, and this may have a detrimental impact on the lives of individuals. From a legal perspective, a question that has often been raised within this context is how we can best account for diversity in such AI systems?

ACUS issues a statement to help agencies make more informed decisions about artificial intelligence. Federal agencies increasingly rely on artificial intelligence (AI) tools to do their work and carry out their missions. Nearly half the federal agencies surveyed for a recent report commissioned by the Administrative Conference of the United States (ACUS) employ or have experimented with AI tools. The agencies used AI tools across an array of governance tasks, including adjudication, enforcement, data collection and analysis, internal management, and public communications. Agencies' interest in AI tools is not surprising.

Many of the metals needed for semiconductors are mined at great human and environmental cost.Credit: Beawiharta/Reuters It determines what we read and buy, whether we get a job, loan, mortgage, subsidies or parole. Two new books offer complementary visions of how society is being reshaped by those who build, use and manage AI. In The Alignment Problem, writer Brian Christian gives an intimate view of the people making AI technology -- their aims, expectations, hopes, challenges and desolations. Starting with Walter Pitts's work on a logical representation of neuron activity in the early twentieth century, he recounts the ideas, aims, successes and failures of researchers and practitioners in fields from cognitive science to engineering. Atlas of AI, from the influential scholar Kate Crawford, deals with how, practically, AI gets into and plays out in our lives.

When the European Union Commission released its regulatory proposal on artificial intelligence last month, much of the US policy community celebrated. Their praise was at least partly grounded in truth: The world's most powerful democratic states haven't sufficiently regulated AI and other emerging tech, and the document marked something of a step forward. Mostly, though, the proposal and responses to it underscore democracies' confusing rhetoric on AI. Over the past decade, high-level stated goals about regulating AI have often conflicted with the specifics of regulatory proposals, and what end-states should look like aren't well-articulated in either case. Coherent and meaningful progress on developing internationally attractive democratic AI regulation, even as that may vary from country to country, begins with resolving the discourse's many contradictions and unsubtle characterizations.

The Supreme Court's Artificial Intelligence Committee on Tuesday launched its Artificial Intelligence portal SUPACE(Supreme Court Portal for Assistance in Court's Efficiency). The event was attended by CJI Bobde, CJI designate Justice NV Ramana and Justice Nageswara Rao, who is also the Chairman of the Supreme Court's AI Committee, and High Court Judges. While launching the Court's Artificial Intelligence Portal, the CJI called the system a'perfect blend of human intelligence and machine learning' and'a hybrid system', which works together with human intelligence. He stated that the system being launched is unique as there is interaction between the human being and machine which creates remarkable results. During the event, CJI addressed the objections and criticisms that Artificial Intelligence faces, as for most people it means automated decision making.

AI, big data, and machine learning have witnessed exponential growth over the past few years. With the evolving technology, businesses realize the importance of adopting AI and big data in their operations. AI, big data, and machine learning create exciting new opportunities for companies and entrepreneurs. But this rapid adoption is also partnered with several complexities and risks, hence, comes the need for regulations. Regulators and policymakers find it difficult to keep track of the constant developments in technology and AI systems.

At Lyft, our mission is to improve people's lives with the world's best transportation. To do this, we start with our own community by creating an open, inclusive, and diverse organization. Here at Lyft, Data is the only way we make decisions. It is the core of our business, helping us create a transportation experience for our customers and providing insights into the effectiveness of our product launch & features. As a Data Engineer at Lyft, you will be a part of an early stage team that builds the data transport, collection, and storage, and exposes services that make data a first-class citizen at Lyft.

Nearly two-thirds of C-level AI leaders can't explain how specific AI decisions or predictions are made, according to a new survey on AI ethics by FICO, which says there is room for improvement. FICO hired Corinium to query 100 AI leaders for its new study, called "The State of Responsible AI: 2021," which the credit report company released today. While there are some bright spots in terms of how companies are approaching ethics in AI, the potential for abuse remains high. For example, only 22% of respondents have an AI ethics board, according to the survey, suggesting the bulk of companies are ill-prepared to deal with questions about bias and fairness. Similarly, 78% of survey-takers say it's hard to secure support from executives to prioritize ethical and responsible use of AI.