How Copyright Law Can Fix Artificial Intelligence\u27s Implicit Bias Problem

As the use of artificial intelligence (AI) continues to spread, we have seen an increase in examples of AI systems reflecting or exacerbating societal bias, from racist facial recognition to sexist natural language processing. These biases threaten to overshadow AI’s technological gains and potential benefits. While legal and computer science scholars have analyzed many sources of bias, including the unexamined assumptions of its oftenhomogenous creators, flawed algorithms, and incomplete datasets, the role of the law itself has been largely ignored. Yet just as code and culture play significant roles in how AI agents learn about and act in the world, so too do the laws that govern them. This Article is the first to examine perhaps the most powerful law impacting AI bias: copyright. Artificial intelligence often learns to “think” by reading, viewing, and listening to copies of human works. This Article first explores the problem of bias through the lens of copyright doctrine, looking at how the law’s exclusion of access to certain copyrighted source materials may create or promote biased AI systems. Copyright law limits bias mitigation techniques, such as testing AI through reverse engineering, algorithmic accountability processes, and competing to convert customers. The rules of copyright law also privilege access to certain works over others, encouraging AI creators to use easily available, legally low-risk sources of data for teaching AI, even when those data are demonstrably biased. Second, it examines how a different part of copyright law—the fair use doctrine—has traditionally been used to address similar concerns in other technological fields, and asks whether it is equally capable of addressing them in the field of AI bias. The Article ultimately concludes that it is, in large part because the normative values embedded within traditional fair use ultimately align with the goals of mitigating AI bias and, quite literally, creating fairer AI systems

e-Skills: The International dimension and the Impact of Globalisation - Final Report 2014

In today’s increasingly knowledge-based economies, new information and communication technologies are a key engine for growth fuelled by the innovative ideas of highly - skilled workers. However, obtaining adequate quantities of employees with the necessary e-skills is a challenge. This is a growing international problem with many countries having an insufficient numbers of workers with the right e-Skills. For example: Australia: “Even though there’s 10,000 jobs a year created in IT, there are only 4500 students studying IT at university, and not all of them graduate” (Talevski and Osman, 2013). Brazil: “Brazil’s ICT sector requires about 78,000 [new] people by 2014. But, according to Brasscom, there are only 33,000 youths studying ICT related courses in the country” (Ammachchi, 2012). Canada: “It is widely acknowledged that it is becoming inc reasingly difficult to recruit for a variety of critical ICT occupations –from entry level to seasoned” (Ticoll and Nordicity, 2012). Europe: It is estimated that there will be an e-skills gap within Europe of up to 900,000 (main forecast scenario) ICT pr actitioners by 2020” (Empirica, 2014). Japan: It is reported that 80% of IT and user companies report an e-skills shortage (IPA, IT HR White Paper, 2013) United States: “Unlike the fiscal cliff where we are still peering over the edge, we careened over the “IT Skills Cliff” some years ago as our economy digitalized, mobilized and further “technologized”, and our IT skilled labour supply failed to keep up” (Miano, 2013)

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

Employees on social media: A multi-spokespeople model of CSR communication

Increasing societal and stakeholder expectations, along with easy access to information through social media, means corporations are asked for more information. The traditional approach to CSR communication, with corporations controlling what and how much to share with stakeholders has been restructured by social media, with stakeholders taking control. As legitimacy on social media is created through the positive and negative judgements of stakeholders, corporations must plan how to meet stakeholder demands for information effectively and legitimately, and this includes choosing appropriate spokespeople. Corporations in India have now turned towards their employees as CSR spokespeople. By encouraging employee activity on social media, these corporations are attempting to meet stakeholder demands and generate legitimacy through spokespeople whom stakeholders perceive as equals. This article examines that strategy and discusses its viability of using employees as spokespeople for CSR communication and engagement with stakeholder