Australia and the UK have opened a joint investigation into Clearview AI. Specifically, the regulatory bodies are concerned with Clearview's practice of using "scraped" data and biometrics. The two countries aren't the first to question Clearview AI, the company behind the controversial facial recognition program. Clearview AI claims to have a database with three billion images gathered from the open web. It offers that database to law enforcement, supposedly so they can identify criminals and victims.

Public scrutiny is critical for trust in, and democratic legitimacy for, the use of data-driven decision-making and algorithmic systems in our society. We stand at the intersection of monumental and ongoing ruptures that will transform the data governance landscape. If they are to have a positive long-term influence it will be because we have heeded their lessons. The Royal Society's new publication, The UK data governance landscape, is a valuable resource published at a moment of immense uncertainty, as well as possibility, in the data governance ecosystem. Midway through 2020 we stand at the intersection of three monumental and ongoing ruptures: the coronavirus pandemic, which is accelerating the application of data-driven technologies (PDF) to health as well as policymaking; the Black Lives Matter movement, which is drawing long-overdue attention to the unequal distribution of the benefits of digital transformation as well the problem of bias in algorithmic systems (PDF); and the impending departure of the United Kingdom from the European Union, which is generating questions about the future of international data flows and the opportunities the UK faces to expand its leadership in artificial intelligence (AI).

Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but do not investigate. In this article, we show that if police racially discriminate when choosing whom to investigate, analyses using administrative records to estimate racial discrimination in police behavior are statistically biased, and many quantities of interest are unidentified--even among investigated individuals--absent strong and untestable assumptions. Using principal stratification in a causal mediation framework, we derive the exact form of the statistical bias that results from traditional estimation. We develop a bias-correction procedure and nonparametric sharp bounds for race effects, replicate published findings, and show the traditional estimator can severely underestimate levels of racially biased policing or mask discrimination entirely.

Many real-world situations of ethical relevance, in particular those of large-scale social choice such as mitigating climate change, involve not only many agents whose decisions interact in complicated ways, but also various forms of uncertainty, including quantifiable risk and unquantifiable ambiguity. In such problems, an assessment of individual and groupwise moral responsibility for ethically undesired outcomes or their responsibility to avoid such is challenging and prone to the risk of under- or overdetermination of responsibility. In contrast to existing approaches based on strict causation or certain deontic logics that focus on a binary classification of `responsible' vs `not responsible', we here present several different quantitative responsibility metrics that assess responsibility degrees in units of probability. For this, we use a framework based on an adapted version of extensive-form game trees and an axiomatic approach that specifies a number of potentially desirable properties of such metrics, and then test the developed candidate metrics by their application to a number of paradigmatic social choice situations. We find that while most properties one might desire of such responsibility metrics can be fulfilled by some variant, an optimal metric that clearly outperforms others has yet to be found.

Both [1, 11] suggests "the ease of in the legal domain. We extract legal indicators from judicial access to information" is a solution to address the gap in accessing judgments to decrease the asymmetry of information of the legal justice. Access to free basic legal information could help the user system and the access-to-justice gap. We use NLP methods to extract to navigate the justice system easily, understand better the legal interesting entities/data from judgments to construct networks area his problem falls into, and choose a lawyer with experience of lawyers and judgments. We propose metrics to rank lawyers on the subject matter of the dispute. In our work, we extract and based on their experience, wins/loss ratio and their importance in represent information from past judgments to increase the transparency the network of lawyers. We also perform community detection in of judicial procedures and make them more accessible to the network of judgments and propose metrics to represent the laypersons.

In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

Machine learning algorithms are used in finance, medicine, and criminal justice, and therefore they can have a deep The advent of machine learning techniques has impact on society. With the recent success of AI applications made it possible to obtain predictive systems that in the private and public domain, legal professionals are now have overturned traditional legal practices. However, interested in artificial intelligence, especially since many rather than leading to systems seeking to startups disrupt the legal market space by seeking to benefit replace humans, the search for the determinants of these new AI techniques (Bex et al., 2017). in a court decision makes it possible to give a However, the arrival of these new techniques has brought better understanding of the decision mechanisms a number of ethical issues. Firstly, machine learning and carried out by the judge. By using a large amount data mining techniques are capable of exploiting personal of court decisions in matters of divorce produced and legal data that are more and more easily accessible on by French jurisdictions and by looking at the variables the Internet, leading to questions about privacy preserving, that allow to allocate an alimony or not, and or even attacks on democracy (Wylie, 2019). Secondly, to define its amount, we seek to identify if there artificial intelligence programs reason in a simplistic way, may be extralegal factors in the decisions taken but the real world is complex, especially in the legal field by the judges. From this perspective, we present which leaves a certain part to the human interpretation of an explainable AI model designed in this purpose the law and characterization of the fact. A machine learning by combining a classification with random forest program has great difficulty in dealing with the unexpected and a regression model, as a complementary tool events that happen in the real world. Intelligent system to existing decision-making scales or guidelines algorithms are black boxes that are impossible to understand, created by practitioners.

Explainable machine learning is a sub-discipline of artificial intelligence (AI) and machine learning that attempts to summarize how machine learning systems make decisions. Summarizing how machine learning systems make decisions can be helpful for a lot of reasons, like finding data-driven insights, uncovering problems in machine learning systems, facilitating regulatory compliance, and enabling users to appeal -- or operators to override -- inevitable wrong decisions. Of course all that sounds great, but explainable machine learning is not yet a perfect science. Figure 1: Explanations created by H2O Driverless AI. These explanations are probably better suited for data scientists than for business users.

There's no shortage of promises when it comes to AI. Some say it will solve all issues while others warn it will result in the end of the world once we know it. Both positions regularly play out in Hollywood plotlines like Westworld, Carbon Black, Minority Report, Her, and Ex Machina. Those stories are compelling because they require us as creators and consumers of AI technology to decide whether we trust an AI system or, more correctly, trust what the system is doing with the information it has been given. This content was produced by Nuance.

The practice to include Artificial Intelligence in industry application is skyrocketing for a decade now. It is evident since, AI and its constituent applications Machine Learning, computer vision, facial analysis, autonomous vehicles, deep learning form the pillars of modern digital empowerment. The ability to learn the data it is trained up to understand the binary, quantum computation of the world, and make decisions derived from its insights makes AI unique than earlier technologies. Leaders believe that possessing AI-based technologies equate to future industry successes. From healthcare, research, finance, logistics to military, law enforcement department AI holds the key to massive competitive edge and up-gradation with monetary benefits too.