Since completing a degree in journalism, Aimee has had her fair share of covering various topics, including business, retail, manufacturing, and travel. She continues to expand her repertoire as a tech journalist with ZDNet. The Information Commissioner's Office (ICO) has fined controversial facial recognition company Clearview AI £7.5 million ($9.4 million) for breaching UK data protection laws and has issued an enforcement notice ordering the company to stop obtaining and using data of UK residents, and to delete the data from its systems. In its finding, the ICO detailed how Clearview AI failed to inform people in the UK that it was collecting their images from the web and social media to create a global online database that could be used for facial recognition; failed to have a lawful reason for collecting people's information; failed to have a process in place to stop the data being retained indefinitely; and failed to meet data protection standards required for biometric data under the General Data Protection Regulation. The ICO also found the company asked for additional personal information, including photos, when asked by members of the public if they were on their database.
The UK's data protection watchdog has confirmed a penalty for the controversial facial recognition company, Clearview AI -- announcing a fine of just over £7.5 million today for a string of breaches of local privacy laws. The watchdog has also issued an enforcement notice, ordering Clearview to stop obtaining and using the personal data of UK residents that is publicly available on the internet; and telling it to delete the information of UK residents from its systems. The US company has amassed a database of 20 billion facial images by scraping data off the public internet, such as from social media services, to create an online database that it uses to power an AI-based identity-matching service which it sells to entities such as law enforcement. The problem is Clearview has never asked individuals whether it can use their selfies for that. And in many countries it has been found in breach of privacy laws.
This special issue interrogates the meaning and impacts of "tech ethics": the embedding of ethics into digital technology research, development, use, and governance. In response to concerns about the social harms associated with digital technologies, many individuals and institutions have articulated the need for a greater emphasis on ethics in digital technology. Yet as more groups embrace the concept of ethics, critical discourses have emerged questioning whose ethics are being centered, whether "ethics" is the appropriate frame for improving technology, and what it means to develop "ethical" technology in practice. This interdisciplinary issue takes up these questions, interrogating the relationships among ethics, technology, and society in action. This special issue engages with the normative and contested notions of ethics itself, how ethics has been integrated with technology across domains, and potential paths forward to support more just and egalitarian technology. Rather than starting from philosophical theories, the authors in this issue orient their articles around the real-world discourses and impacts of tech ethics--i.e., tech ethics in action.
A new generation of increasingly autonomous and self-learning systems, which we call embodied systems, is about to be developed. When deploying these systems into a real-life context we face various engineering challenges, as it is crucial to coordinate the behavior of embodied systems in a beneficial manner, ensure their compatibility with our human-centered social values, and design verifiably safe and reliable human-machine interaction. We are arguing that raditional systems engineering is coming to a climacteric from embedded to embodied systems, and with assuring the trustworthiness of dynamic federations of situationally aware, intent-driven, explorative, ever-evolving, largely non-predictable, and increasingly autonomous embodied systems in uncertain, complex, and unpredictable real-world contexts. We are also identifying a number of urgent systems challenges for trustworthy embodied systems, including robust and human-centric AI, cognitive architectures, uncertainty quantification, trustworthy self-integration, and continual analysis and assurance.
The 24 March, 2020 will be remembered by some for the news that Prince Charles tested positive for Covid and was isolating in Scotland. In Athens it was memorable as the day the traffic went silent. Twenty-four hours into a hard lockdown, Greeks were acclimatising to a new reality in which they had to send an SMS to the government in order to leave the house. As well as millions of text messages, the Greek government faced extraordinary dilemmas. The European Union's most vulnerable economy, its oldest population along with Italy, and one of its weakest health systems faced the first wave of a pandemic that overwhelmed richer countries with fewer pensioners and stronger health provision. One Greek who did go into the office that day was Kyriakos Pierrakakis, the minister for digital transformation, whose signature was inked in blue on an agreement with the US technology company, Palantir. The deal, which would not be revealed to the public for another nine months, gave one of the world's most controversial tech companies access to vast amounts of personal data while offering its software to help Greece weather the Covid storm. The zero-cost agreement was not registered on the public procurement system, neither did the Greek government carry out a data impact assessment – the mandated check to see whether an agreement might violate privacy laws. The questions that emerge in pandemic Greece echo those from across Europe during Covid and show Palantir extending into sectors from health to policing, aviation to commerce and even academia.
After challenging the validity of these assumptions in real-world applications, we propose ways to move forward when they are violated. First, we show that group fairness criteria purely based on statistical properties of observed data are fundamentally limited. Revisiting this limitation from a causal viewpoint we develop a more versatile conceptual framework, causal fairness criteria, and first algorithms to achieve them. We also provide tools to analyze how sensitive a believed-to-be causally fair algorithm is to misspecifications of the causal graph. Second, we overcome the assumption that sensitive data is readily available in practice. To this end we devise protocols based on secure multi-party computation to train, validate, and contest fair decision algorithms without requiring users to disclose their sensitive data or decision makers to disclose their models. Finally, we also accommodate the fact that outcome labels are often only observed when a certain decision has been made. We suggest a paradigm shift away from training predictive models towards directly learning decisions to relax the traditional assumption that labels can always be recorded. The main contribution of this thesis is the development of theoretically substantiated and practically feasible methods to move research on fair machine learning closer to real-world applications.