Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, and its implementation is planned to become more prevalent in coming years. AI is increasingly being embedded in our lives, supplementing our pervasive use of digital technologies. But this is being accompanied by disquiet over problematic and dangerous implementations of AI, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine and criminal justice. These developments have led to concerns about whether and how AI systems adhere, and will adhere to ethical standards. These concerns have stimulated a global conversation on AI ethics, and have resulted in various actors from different countries and sectors issuing ethics and governance initiatives and guidelines for AI. Such developments form the basis for our research in this report, combining our international and interdisciplinary expertise to give an insight into what is happening in Australia, China, Europe, India and the US.

AI is not really a significant discontinuity, but rather an acceleration of a process that's been going on for thousands of years. AI will lead to more rapid change in the workplace, but whether that leads to joblessness depends more on inequality / wealth distribution than AI. Therefore employment depends on financial governance / redistribution, which can be tricky because AI/ICT facilitate transnational wealth extraction, including trillions of micro barters for information that are not denominated and therefore are hard to tax. Here is a great example of automation not causing joblessness: there are fewer tellers per bank branch due to AI/ATMs, but there are more tellers altogether because there are now more bank branches since they are more profitable. But their jobs are more people oriented, less counting oriented.

Machine learning algorithms have been increasingly deployed in critical automated decision-making systems that directly affect human lives. When these algorithms are only trained to minimize the training/test error, they could suffer from systematic discrimination against individuals based on their sensitive attributes such as gender or race. Recently, there has been a surge in machine learning society to develop algorithms for fair machine learning. In particular, many adversarial learning procedures have been proposed to impose fairness. Unfortunately, these algorithms either can only impose fairness up to first-order dependence between the variables, or they lack computational convergence guarantees. In this paper, we use R\'enyi correlation as a measure of fairness of machine learning models and develop a general training framework to impose fairness. In particular, we propose a min-max formulation which balances the accuracy and fairness when solved to optimality. For the case of discrete sensitive attributes, we suggest an iterative algorithm with theoretical convergence guarantee for solving the proposed min-max problem. Our algorithm and analysis are then specialized to fair classification and the fair clustering problem under disparate impact doctrine. Finally, the performance of the proposed R\'enyi fair inference framework is evaluated on Adult and Bank datasets.

Fairness has emerged as a key issue in machine learning as it is increasingly used in areas like hiring [Dastin, 2018], healthcare[Gupta and Mohammad, 2017], and criminal justice [Equivant, 2019]. In particular, models' predictions should not lead to decisions that discriminate on the basis of a legally protected attribute, such as race or gender. Among the proposals to address this issue, a growing body of work focuses on learning et al., 2017, del Barrio et al., 2018, Feldmanfair representations of data for downstream modeling [Calmon 2015, Johndrow and Lum, 2019, Kamiran and Calders, 2012]. Most of these approaches are modelet al., agnostic, which provides flexibility when working with the learned representations, but comes at the cost of potentially suboptimal results in terms of both fairness and accuracy. In this work, we present a new approach for fair representation learning that takes into account the target hypothesis class of models that will be learned from the representation. Specifically, we show how to leverage information about the reproducing kernel Hilbert space (RKHS) to learn a fair representation for kernel-based models with provable fairness and accuracy guarantees. Our approach builds on the classic Sufficient Dimension Reduction (SDR) framework [Li, 1991, Cook 1991, Cook, 1998, Fukumizu et al., 2004, 2009, Wu et al., 2009, Cook and Forzani, 2009]and Weisberg, which is used to compute a low-dimensional projection of the feature vector X that captures all information related to the response Y. Our key insight is that we can instead perform SDR with respect to the protected attributes S, and then take the orthogonal complement of the resulting projection to obtain a fair subspace of the RKHS that captures information in X unrelated to S. We show that functions in the fair subspace 2.2), and we leverage this fact to prove that our approachwill be independent of S under mild conditions (§

"Data is the oil of the digital era," proclaims a 2017 Economist article. Big business--especially tech giants like Alphabet (Google's parent), Amazon, Facebook, and Apple among others-- are mining data like Standard Oil processed petroleum a century before. Why is the legal industry still running on gut and instinct while the businesses it serves are propelled by data? A recent survey by business analytics powerhouse RELX Group polled 1,000 U.S. senior executives across the health care, insurance, legal, science, banking industries as well as government. Law finished last among industries--just ahead of government--in utilizing big data in some form.

Innovate UK, as part of UK Research and Innovation, will invest up to £3.5 million to develop data access methods to enable the application of artificial intelligence (AI) and data technologies in the accountancy, insurance and legal services sector. In this competition, individuals can apply, on behalf of their organisation, to take part in a 3-day residential Innovation Lab. The selected participants will work together to develop consortia and project proposals to enable a more accessible data environment within the accountancy, insurance and legal sectors. After the Innovation Lab, the developed project collaborations will have 3 weeks to finalise their proposals before submitting them for assessment. Competition briefing events will be held in London, Manchester and Edinburgh.

The Ethics Guidelines for Trustworthy AI provide an assessment list that operationalises the key requirements and offers guidance to implement them in practice. This assessment list will undergo a piloting process: all stakeholders are invited to test the assessment list and provide practical feedback on how it can be improved. This feedback will allow for a better understanding of how the assessment list, which is aimed to offer guidance for all AI applications, can be implemented within an organisation. It will also indicate where specific tailoring of the assessment list is needed given AI's context-specificity. All interested stakeholders can participate to the piloting process and start testing out the assessment list. An open survey or "quantitative analysis" which will be sent to all those who register to the piloting; The piloting phase will run from the 26th of June until the 1st of December 2019.

The performance of a machine learning system is usually evaluated by using i.i.d.\ observations with true labels. However, acquiring ground truth labels is expensive, while obtaining unlabeled samples may be cheaper. Stratified sampling can be beneficial in such settings and can reduce the number of true labels required without compromising the evaluation accuracy. Stratified sampling exploits statistical properties (e.g., variance) across strata of the unlabeled population, though usually under the unrealistic assumption that these properties are known. We propose two new algorithms that simultaneously estimate these properties and optimize the evaluation accuracy. We construct a lower bound to show the proposed algorithms (to log-factors) are rate optimal. Experiments on synthetic and real data show the reduction in label complexity that is enabled by our algorithms.

A substantial portion of the literature on fairness in algorithms proposes, analyzes, and operationalizes simple formulaic criteria for assessing fairness. Two of these criteria, Equalized Odds and Calibration by Group, have gained significant attention for their simplicity and intuitive appeal, but also for their incompatibility. This chapter provides a perspective on the meaning and consequences of these and other fairness criteria using graphical models which reveals Equalized Odds and related criteria to be ultimately misleading. An assessment of various graphical models suggests that fairness criteria should ultimately be case-specific and sensitive to the nature of the information the algorithm processes.

The rise of artificial intelligence (A.I.) based systems has the potential to benefit adopters and society as a whole. However, these systems may also enclose potential conflicts and unintended consequences. Notably, people will only adopt an A.I. system if it confers them an advantage, at which point non-adopters might push for a strong regulation if that advantage for adopters is at a cost for them. Here we propose a stochastic game theoretical model for these conflicts. We frame our results under the current discussion on ethical A.I. and the conflict between individual and societal gains, the societal value alignment problem. We test the arising equilibria in the adoption of A.I. technology under different norms followed by artificial agents, their ensuing benefits, and the emergent levels of wealth inequality. We show that without any regulation, purely selfish A.I. systems will have the strongest advantage, even when a utilitarian A.I. provides a more significant benefit for the individual and the society. Nevertheless, we show that it is possible to develop human conscious A.I. systems that reach an equilibrium where the gains for the adopters are not at a cost for non-adopters while increasing the overall fitness and lowering inequality. However, as shown, a self-organized adoption of such policies would require external regulation.