Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Starting in 2012, the Supreme Court's approach to digital privacy has undergone a seismic shift. In a series of recent cases on location tracking and cellular phone searches, the court has recognized that, when it comes to big data, old rules about our expectations of privacy may not apply. Because information can now be gathered, stored, and analyzed cheaply, the Supreme Court has recently found that Fourth Amendment protections must be carefully recalibrated to prevent unchecked police power. Supreme Court nominee Brett Kavanaugh, however, has exhibited a contrasting and outdated understanding of privacy.
Face recognition is a stark example of a technology that is being deployed faster than society and the law can adopt new norms and rules. It lets governments and private enterprise track citizens anywhere there is a camera, even if they're not carrying any devices. In general, people who are in public don't have any legal expectation of privacy and can be photographed or recorded. Because of this, the technology has the potential to be more intrusive than phone tracking, the legality of which the U.S. Supreme Court will soon decide. There are only two states, Texas and Illinois, that limit private companies' ability to track people via their faces.
Artificial intelligence is transforming the legal profession -- and that includes legal ethics. AI and similar cutting-edge technologies raise many complex ethical issues and challenges that lawyers ignore at their peril. At the same time, AI also holds out the promise of helping lawyers to meet their ethical obligations, serve their clients more effectively, and promote access to justice and the rule of law. What does AI mean for legal ethics, what should lawyers do to prepare for these changes, and how could AI help improve the legal profession? Together with our partners at Thomson Reuters, we at Above the Law have been examining these important subjects.
Artificial Intelligence (AI) is changing how our society operates. AI now helps make judiciary decisions, medical diagnoses, and drives cars. The use of AI in our society also has important environmental implications. AI can help improve resource use, improve energy efficiency, predict extreme weather events, and aid in scientific research. But while AI has the potential to improve human interaction with the environment, AI can also exacerbate existing environmental issues.
Digital services have frequently been in collision -- if not out-and-out conflict -- with the rule of law. But what happens when technologies such as deep learning software and self-executing code are in the driving seat of legal decisions? How can we be sure next-gen'legal tech' systems are not unfairly biased against certain groups or individuals? And what skills will lawyers need to develop to be able to properly assess the quality of the justice flowing from data-driven decisions? While entrepreneurs have been eyeing traditional legal processes for some years now, with a cost-cutting gleam in their eye and the word'streamline' on their lips, this early phase of legal innovation pales in significance beside the transformative potential of AI technologies that are already pushing their algorithmic fingers into legal processes -- and perhaps shifting the line of the law itself in the process.
This master's thesis discusses an important issue regarding how algorithmic decision making (ADM) is used in crime forecasting. In America forecasting tools are widely used by judiciary systems for making decisions about risk offenders based on criminal justice for risk offenders. By making use of such tools, the judiciary relies on ADM in order to make error free judgement on offenders. For this purpose, one of the quality measures for machine learning techniques which is widly used, the $AUC$ (area under curve), is compared to and contrasted for results with the $PPV_k$ (positive predictive value). Keeping in view the criticality of judgement along with a high dependency on tools offering ADM, it is necessary to evaluate risk tools that aid in decision making based on algorithms. In this methodology, such an evaluation is conducted by implementing a common machine learning approach called binary classifier, as it determines the binary outcome of the underlying juristic question. This thesis showed that the $PPV_k$ (positive predictive value) technique models the decision of judges much better than the $AUC$. Therefore, this research has investigated whether there exists a classifier for which the $PPV_k$ deviates from $AUC$ by a large proportion. It could be shown that the deviation can rise up to 0.75. In order to test this deviation on an already in used Classifier, data from the fourth generation risk assement tool COMPAS was used. The result were were quite alarming as the two measures derivate from each other by 0.48. In this study, the risk assessment evaluation of the forecasting tools was successfully conducted, carefully reviewed and examined. Additionally, it is also discussed whether such systems used for the purpose of making decisions should be socially accepted or not.
"What do judges know that we cannot teach a computer?" There is a substantial public sentiment that distrusts legal rules and state structures and looks to technology for solutions. After all, many trust their smartphones more than they trust their government. But what may seem as a fairly modern libertarian opinion, voiced in pitch decks and technology conferences, and buoyed by the success of the information economy, has much deeper roots. Such ambitions of a technology centric society were voiced more than forty years ago by John McCarthy, an influential computer scientist and professor at Stanford who coined the term, "artificial intelligence", and nurtured it into a formal field of research. It was not that such assertions were without prominent challengers, noticeably Joseph Weizenbaum whose 1976 book titled Computer Power and Human Reason put people at the centre of technological progress, rather than being its subjects.