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The AI and Society discourse has previously drawn attention to the ways that digital systems embody the values of the technology development community from which they emerge through the development and deployment process. Research shows how this effect leads to a particular treatment of gender in computer systems development, a treatment which lags far behind the rich understanding of gender that social studies scholarship reveals and people across society experience. Many people do not relate to the narrow binary gender options of male or female, and many people express their gender identity in much richer ways than the sex/gender binary female/woman and male/man Boolean terms will allow. We ask: are "born-digital" gendered datasets in digital systems experienced as marginalising by those who express their identity beyond the male/female binary? Case Study: Ireland. To answer this universal question, this paper presents the findings of an empirical case study of people in Ireland with diverse gender identities and expressions, and their experiences with public data systems and new technologies. In spite of great social changes in Ireland which have led to constitutional change in favour of LGBTQI + people, born-digital systems were experienced by respondents as embodying socio-cultural values which were no longer accepted in society at large. For many of the respondents, digital technologies routinely marginalise them in all kinds of ways. These systems keep alive violence and oppression long after civil rights have been enshrined in constitutional law. This study is just one example of the way assumptions about digital are disengaged from society-at-large. It is a call to arms to all who are passionate about socially-responsible technology. [ABSTRACT FROM AUTHOR]

Inverse reinforcement learning (IRL) aims to estimate the reward function of optimizing agents by observing their response (estimates or actions). This paper considers IRL when noisy estimates of the gradient of a reward function generated by multiple stochastic gradient agents are observed. We present a generalized Langevin dynamics algorithm to estimate the reward function R(θ); specifically, the resulting Langevin algorithm asymptotically generates samples from the distribution proportional to exp(R(θ)). The proposed adaptive IRL algorithms use kernel-based passive learning schemes. We also construct multi-kernel passive Langevin algorithms for IRL which are suitable for high dimensional data and achieve variance reduction. The performance of the proposed IRL algorithms are illustrated on examples in adaptive Bayesian learning, logistic regression (high dimensional problem) and constrained Markov decision processes. We prove weak convergence of the proposed IRL algorithms using martingale averaging methods. We also analyze the tracking performance of the IRL algorithms in non-stationary environments where the utility function R(θ) has a hyper-parameter that jump changes over time as a slow Markov chain which is not known to the inverse learner. In this case, martingale averaging yields a Markov switched diffusion limit as the asymptotic behavior of the IRL algorithm. [ABSTRACT FROM AUTHOR]

Highlights • Co-simulation and building energy models provide a framework to test control algorithms. • Machine learning algorithms are effective to develop predictive optimisation models. • Predictive controls for residential can reduce heating systems costs up to 40%. • Smart predictive control in buildings results in lower carbon footprint up to 39%. • The smart grid dynamically controls dwellings to balance electricity supply/demand. Abstract This paper assesses the performance of control algorithms for the implementation of demand response strategies in the residential sector. A typical house, representing the most common building category in Ireland, was fully instrumented and utilised as a test-bed. A calibrated building simulation model was developed and used to assess the effectiveness of demand response strategies under different time-of-use electricity tariffs in conjunction with zone thermal control. Two demand response algorithms, one based on a rule-based approach, the other based on a predictive-based (machine learning) approach, were deployed for control of an integrated heat pump and thermal storage system. The two algorithms were evaluated using a common demand response price scheme. Compared to a baseline reference scenario, the following reductions were observed: electricity end-use expenditure (20.5% rule-based and 41.8% predictive algorithm), utility generation cost (18.8% rule-based and 39% predictive algorithm), carbon emissions (20.8% rule-based and 37.9% predictive algorithm). [ABSTRACT FROM AUTHOR]