This paper introduces a collaborative, human-centered taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.

We explore the phenomena of subjective randomness as a case study in understanding how people discover structure embedded in noise. We present a rational account of randomness perception based on the statis- tical problem of model selection: given a stimulus, inferring whether the process that generated it was random or regular. Inspired by the mathe- matical definition of randomness given by Kolmogorov complexity, we characterize regularity in terms of a hierarchy of automata that augment a finite controller with different forms of memory. We find that the reg- ularities detected in binary sequences depend upon presentation format, and that the kinds of automata that can identify these regularities are in- formative about the cognitive processes engaged by different formats.

The release of ChatGPT has everyone abuzz about artificial intelligence. I've been getting lots of questions about our research project Artificial Intelligence and Academic Integrity: The Ethics of Teaching and Learning with Algorithmic Writing Technologies. We are ready to start data collection in January so I do not yet have results to share. Our team has two preliminary papers under review, but I won't say much about them until they are published. In the meantime, I wanted to share some high level thoughts on the topic since many of you have been asking.

Monitoring of workers and setting performance targets through algorithms is damaging employees' mental health and needs to be controlled by new legislation, according to a group of MPs and peers. An "accountability for algorithms act'" would ensure that companies evaluate the effect of performance-driven regimes such as queue monitoring in supermarkets or deliveries-per-hour guidelines for delivery drivers, said the all-party parliamentary group (APPG) on the future of work. "Pervasive monitoring and target-setting technologies, in particular, are associated with pronounced negative impacts on mental and physical wellbeing as workers experience the extreme pressure of constant, real-time micro-management and automated assessment," said the APPG members in their report, the New Frontier: Artificial Intelligence at Work. The report recommends bringing in a new algorithms act, which it says would establish "a clear direction to ensure AI puts people first". It warns that "use of algorithmic surveillance, management and monitoring technologies that undertake new advisory functions, as well as traditional ones, has significantly increased during the pandemic".

At a moment when vaccines promise to end the coronavirus pandemic, emerging new variants threaten to accelerate it. The astonishingly fast development of safe and effective vaccines is being stymied by the glacial pace of actual vaccinations while 3,000 Americans die each day. Minimizing death and suffering from COVID-19 requires vaccinating the most vulnerable Americans first and fast, but the vaccine rollout has been slow and inequitable. Prioritization algorithms have led to the most privileged being prioritized over the most exposed, and strict adherence to priority pyramids has been disastrously slow. Yet without prioritization, vaccines go to those with greatest resources rather than to those at greatest risk.