As humans, we perceive the three-dimensional structure of the world around us with apparent ease. Think of how vivid the three-dimensional percept is when you look at a vase of flowers sitting on the table next to you. You can tell the shape and translucency of each petal through the subtle patterns of light and shading that play across its surface and effortlessly segment each flower from the background of the scene (Figure 1.1). Looking at a framed group por- trait, you can easily count (and name) all of the people in the picture and even guess at their emotions from their facial appearance. Perceptual psychologists have spent decades trying to understand how the visual system works and, even though they can devise optical illusions1 to tease apart some of its principles (Figure 1.3), a complete solution to this puzzle remains elusive (Marr 1982; Palmer 1999; Livingstone 2008).

From reminding potty-training toddlers to go to the loo to telling bedtime stories and being used as a "conversation partner", voice-activated smart devices are being used to help rear children almost from the day they are born. But the rapid rise in voice assistants, including Google Home, Amazon Alexa and Apple's Siri could, researchers suggest, have a long-term impact on children's social and cognitive development, specifically their empathy, compassion and critical thinking skills. "The multiple impacts on children include inappropriate responses, impeding social development and hindering learning opportunities," said Anmol Arora, co-author of an article published in the journal Archives of Disease in Childhood. A key concern is that children attribute human characteristics and behaviour to devices that are, said Arora, "essentially a list of trained words and sounds mashed together to make a sentence." The children anthropomorphise and then emulate the devices, copying their failure to alter their tone, volume, emphasis or intonation.

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You could see it coming. Flippy started acting weird, jerking and hitching. The worker on the fry station had witnessed this behavior before. Even Joe Garcia, the Miso Robotics "robot support specialist" assigned to troubleshoot at Jack in the Box, had seen it. Garcia, a mechanical engineering graduate from Loyola Marymount University who one day wants to work for NASA, is spending his days swooping in when Flippy occasionally loses his mind as he encounters tacos.

As worker shortages are felt across the hospitality sector, the owners of the Bella Italia chain are turning to robots to provide table service to customers. Big Table Group, which also owns Café Rouge and Las Iguanas, is testing out the robot at its Bella Italia restaurant in Center Parcs Whinfell Forest in Cumbria, in the first such trial by a big restaurant chain. The BellaBot, made by Chinese company Pudu, can carry up to 40kg on four trays and deliver and retrieve plates from tables with help from humans who load and unload its "body". Eric Guo, the chief executive of Spark which distributes Pudu robots in the UK, said there were 60 working across 20 British businesses and he expected more orders in the year ahead. Most are operating in restaurants, but hotels, supermarkets, care homes, snooker clubs and bowling alleys are also experimenting with the technology.

This survey article assesses and compares existing critiques of current fairness-enhancing technical interventions in machine learning (ML) that draw from a range of non-computing disciplines, including philosophy, feminist studies, critical race and ethnic studies, legal studies, anthropology, and science and technology studies. It bridges epistemic divides in order to offer an interdisciplinary understanding of the possibilities and limits of hegemonic computational approaches to ML fairness for producing just outcomes for society's most marginalized. The article is organized according to nine major themes of critique wherein these different fields intersect: 1) how "fairness" in AI fairness research gets defined; 2) how problems for AI systems to address get formulated; 3) the impacts of abstraction on how AI tools function and its propensity to lead to technological solutionism; 4) how racial classification operates within AI fairness research; 5) the use of AI fairness measures to avoid regulation and engage in ethics washing; 6) an absence of participatory design and democratic deliberation in AI fairness considerations; 7) data collection practices that entrench "bias," are non-consensual, and lack transparency; 8) the predatory inclusion of marginalized groups into AI systems; and 9) a lack of engagement with AI's long-term social and ethical outcomes. Drawing from these critiques, the article concludes by imagining future ML fairness research directions that actively disrupt entrenched power dynamics and structural injustices in society.

The motivation to integrate AI into mental health services has grown during the pandemic. The Kaiser Family Foundation reported an increase in adults experiencing symptoms of anxiety and depression, from 1 in 10 adults pre-pandemic to 4 in 10 adults in early 2021. Coupled with a national shortage of mental health professionals as well as limited opportunities for in-person mental health support, AI-powered tools could be used as an entry point to care by automatically and remotely measuring and intervening to reduce mental health symptoms. Many mental health startups are integrating AI within their product offerings. Woebot Health developed a chatbot that delivers on-demand therapy to users through natural language processing (NLP).

Digitization is penetrating more and more areas of life. Tasks are increasingly being completed digitally, and are therefore not only fulfilled faster, more efficiently but also more purposefully and successfully. The rapid developments in the field of artificial intelligence in recent years have played a major role in this, as they brought up many helpful approaches to build on. At the same time, the eyes, their movements, and the meaning of these movements are being progressively researched. The combination of these developments has led to exciting approaches. In this dissertation, I present some of these approaches which I worked on during my Ph.D. First, I provide insight into the development of models that use artificial intelligence to connect eye movements with visual expertise. This is demonstrated for two domains or rather groups of people: athletes in decision-making actions and surgeons in arthroscopic procedures. The resulting models can be considered as digital diagnostic models for automatic expertise recognition. Furthermore, I show approaches that investigate the transferability of eye movement patterns to different expertise domains and subsequently, important aspects of techniques for generalization. Finally, I address the temporal detection of confusion based on eye movement data. The results suggest the use of the resulting model as a clock signal for possible digital assistance options in the training of young professionals. An interesting aspect of my research is that I was able to draw on very valuable data from DFB youth elite athletes as well as on long-standing experts in arthroscopy. In particular, the work with the DFB data attracted the interest of radio and print media, namely DeutschlandFunk Nova and SWR DasDing. All resulting articles presented here have been published in internationally renowned journals or at conferences.

Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychometric attributes, emotions' co-occurrence patterns, and online behavioural traits, including social engagement and posting trends. Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary, such as biological processes. As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average. Overall, the monthly posting variance of the affected groups is higher than the control groups. Moreover, we found evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder than other less restrictive platforms. In particular, we observe on Twitter less quantifiable differences between affected and control groups compared to Reddit.