In continuing my trend for AI ethics from the last post, Spinoza: Building an AI Ethics Framework From First Principles, I found some early research about AI ethics audit. So, the proposal is to audit the behavior of the organization as a structured process by which an entity's behavior is assessed for consistency with relevant principles or norms. However, defining the norms is the challenge. In a nutshell, the audits focus on the rationale behind the decision, code audits entail reviewing the source code, and impact audits investigate the effects of an algorithm's outputs. The process of ethics-based auditing should be continuous, holistic, dialectic, strategic and design-driven.

Higher-order tensor datasets are rising ubiquitously in modern data science applications, for instance, recommendation systems (Baltrunas et al., 2011; Bi et al., 2018), social networks (Bickel and Chen, 2009), genomics (Hore et al., 2016), and neuroimaging (Zhou et al., 2013). Tensor provides effective representation of data structure that classical vector-and matrix-based methods fail to capture. One example is music recommendation system (Baltrunas et al., 2011) that records ratings of songs from users on various contexts. This three-way tensor of user song context allows us to investigate interactions of users and songs in a context-specific manner. Another example is network dataset that records the connections among a set of nodes. Pairwise interactions are often insufficient to capture the complex relationships, whereas multi-way interactions improve the understanding of networks in molecular system (Young et al., 2018) and social networks (Han et al., 2020). In both examples, higher-order tensors represent multi-way interactions in an efficient way. Tensor estimation problem cannot be solved without imposing structures. An appropriate reordering of tensor entries often provides effective representation of the hidden salient structure.

Data on high-dimensional spheres arise frequently in many disciplines either naturally or as a consequence of preliminary processing and can have intricate dependence structure that needs to be understood. We develop exploratory factor analysis of the projected normal distribution to explain the variability in such data using a few easily interpreted latent factors. Our methodology provides maximum likelihood estimates through a novel fast alternating expectation profile conditional maximization algorithm. Results on simulation experiments on a wide range of settings are uniformly excellent. Our methodology provides interpretable and insightful results when applied to tweets with the $\#MeToo$ hashtag in early December 2018, to time-course functional Magnetic Resonance Images of the average pre-teen brain at rest, to characterize handwritten digits, and to gene expression data from cancerous cells in the Cancer Genome Atlas.

Since GDPR came into force in May 2018, companies have worked on their data practices to comply with this privacy law. In particular, since the privacy policy is the essential communication channel for users to understand and control their privacy, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. Therefore, it is unclear if they comply with GDPR. In this paper, we create a privacy policy dataset of 1,080 websites labeled with the 18 GDPR requirements and develop a Convolutional Neural Network (CNN) based model which can classify the privacy policies with an accuracy of 89.2%. We apply our model to perform a measurement on the compliance in the privacy policies. Our results show that even after GDPR went into effect, 97% of websites still fail to comply with at least one requirement of GDPR.

Ethics is one of the longest standing intellectual endeavors of humanity. In recent years, the fields of AI and NLP have attempted to wrangle with how learning systems that interact with humans should be constrained to behave ethically. One proposal in this vein is the construction of morality models that can take in arbitrary text and output a moral judgment about the situation described. In this work, we focus on a single case study of the recently proposed Delphi model and offer a critique of the project's proposed method of automating morality judgments. Through an audit of Delphi, we examine broader issues that would be applicable to any similar attempt. We conclude with a discussion of how machine ethics could usefully proceed, by focusing on current and near-future uses of technology, in a way that centers around transparency, democratic values, and allows for straightforward accountability.

Editor's Note: As an industry leader in the use of artificial intelligence to empower cyber discovery and legal discovery efforts, HaystackID is excited to share this new information paper from the EDRM and to highlight the participation of HaystackID eDiscovery expert Matt Sinner as a contributor to this important educational effort. We also strongly support the educational and standardization initiatives of the EDRM and continue to be a proud partner of theirs as they empower the leaders of eDiscovery. Originally published by the Electronic Discovery Reference Model (EDRM). Please see full Publication below for more information.

Through the pervasive use of massive amounts of data to automate decisions and processes, artificial intelligence (AI) constitutes one of the most impactful developments for businesses and organisations in general. However, this fast-paced and unstoppable trend raises ethical issues. How can we ensure that AI development is fair, when the algorithms at its core are designed with (often unconscious) racist, sexist, or other biases? Lorena Blasco-Arcas and Hsin-Hsuan Meg Lee propose a human-centred view for the design of specific frameworks and regulatory systems. "Okay, Google, what's the weather today?" "Sorry, I don't understand."

Algorithms rule the world … or, at least, the world is headed that way. How can you prepare your company and its financial underpinnings not only to survive but also thrive under this new big data paradigm? In his new book, Deep Finance: Corporate Finance in the Information Age, author Glenn Hopper provides a clear guide for finance professionals and non-technologists who aspire to digitally transform their companies into modern, data-driven organizations streamlined for success and profitability. Hopper, who comes to this subject armed with a unique background in finance and technology, contends that the finance department is perfectly placed to lead the digital revolution – bringing companies of all sizes into a new era of efficiency while future-proofing the role of chief financial officer. Deep Finance is written for a wide audience, ranging from those who don't know AI from A/R to those who are already working with data to drive business decisions.

We propose kernel ridge regression estimators for mediation analysis and dynamic treatment effects over short horizons. We allow treatments, covariates, and mediators to be discrete or continuous, and low, high, or infinite dimensional. We propose estimators of means, increments, and distributions of counterfactual outcomes with closed form solutions in terms of kernel matrix operations. For the continuous treatment case, we prove uniform consistency with finite sample rates. For the discrete treatment case, we prove root-n consistency, Gaussian approximation, and semiparametric efficiency. We conduct simulations then estimate mediated and dynamic treatment effects of the US Job Corps program for disadvantaged youth.

The 18th International Conference for Artificial Intelligence and Law (ICAIL 2021) was organized at the University of São Paulo School of Law, Brazil. ICAIL is a biannual conference organized under the auspices of the International Association for Artificial Intelligence and Law (iaail.org) For the first time, the ICAIL conference was organized entirely online, due to the overall Covid-19 pandemic situation. Despite these unusual circumstances, the conference came out as a considerable success, attracting almost 1400 registered participants, the highest number ever. The conference talks were streamed publicly on the YouTube channel and the discussions and networking were enabled on the platforms accessible for the registered participants.