Bias, unfairness and lack of transparency and accountability in Artificial Intelligence (AI) systems, and the potential for the misuse of predictive models for decision-making have raised concerns about the ethical impact and unintended consequences of new technologies for society across every sector where data-driven innovation is taking place. This paper reviews the landscape of suggested ethical frameworks with a focus on those which go beyond high-level statements of principles and offer practical tools for application of these principles in the production and deployment of systems. This work provides an assessment of these practical frameworks with the lens of known best practices for impact assessment and audit of technology. We review other historical uses of risk assessments and audits and create a typology that allows us to compare current AI ethics tools to Best Practices found in previous methodologies from technology, environment, privacy, finance and engineering. We analyse current AI ethics tools and their support for diverse stakeholders and components of the AI development and deployment lifecycle as well as the types of tools used to facilitate use. From this, we identify gaps in current AI ethics tools in auditing and risk assessment that should be considered going forward.

The rate of sweat evaporation from the arm, chest, back and thigh, aural temperature, skin temperature (arm, chest, back and thigh), heat production (derived from measurements of respiratory gas exchange) and heart rate were measured in 7 men during 15 minutes of leg or arm cycling at 32% of predicted maximum oxygen uptake (VO2 max). The regional sweat evaporation rates and changes in body temperature were similar during both forms of exercise. The peak rates of sweat evaporation from the arm, chest, back and thigh were 15.7 +/- 19.8, 25.0 +/- 21.6, 28.7 +/- 22.7 and 21.0 +/- 18.2 mg.cm-2 hr-1 during leg cycling and 13.2 +/- 11.6, 22.2 +/- 14.4, 27.6 +/- 14.7 and 19.2 +/- 13.3 (SD) mg.cm-2 hr-1 respectively during arm cycling. The sweat evaporation rates from the different body regions were not significantly different from one another.

Previous studies have focused on the biases and feedback loops that occur in predictive policing algorithms. These studies show how systemically and institutionally biased data leads to these feedback loops when predictive policing algorithms are applied in real life.We take a step back, and show that the choice in algorithm can be embedded in a specific criminological theory, and that the choice of a model on its own even without biased data can create biased feedback loops. By synthesizing "historical" data, in which we control the relationships between crimes, location and time, we show that the current predictive policing algorithms create biased feedback loops even with completely random data. We then review the process of creation and deployment of these predictive systems, and highlight when good practices, such as fitting a model to data, "go bad" within the context of larger system development and deployment. Using best practices from previous work on assessing and mitigating the impact of new technologies, we highlight where the design of these algorithms has broken down. The study also found that multidisciplinary analysis of such systems is vital for uncovering these issues and shows that any study of equitable AI should involve a systematic and holistic analysis of their design rationalities. CCS Concepts: • Applied computing → Law, social and behavioral sciences; • Software and its engineering → Designing software; • Theory of computation → Models of computation.

The influence of 1 h of surface cooling on body temperature, variables which contribute to thermoregulation and selected hormones and metabolites has been investigated in seven patients susceptible to malignant hyperpyrexia (MH) and in seven matched control subjects. Cooling was achieved using a liquid conditioned coverall worn next to the skin. Skin temperature decreased similarly in both groups of subjects. Heat production increased in both groups, with a slightly higher heat production being seen in the MH group. Core temperature increased in both groups of subjects at the start of the cooling period, with a significantly greater increase occurring in the MH group (control: + 0.13 +/- 0.13; MH: + 0.28 +/- 0.10 degrees C, P less than 0.05). There were no significant changes in plasma lactate, or pyruvate concentrations. Plasma glucose concentrations were lower in the control group; after 30 min of cooling plasma glucose was 4.25 +/- 0.37 mmol litre-1 in the control group and 5.34 +/- 0.21 mmol litre-1 in the MH group (P less than 0.05). There were no significant changes in plasma thyroxine or adrenaline concentrations. Plasma noradrenaline increased in both groups of subjects. The increase in plasma noradrenaline of the MH patients was greater than in most of the control subjects.

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