1) Lecturer/Senior Lecturer (Assoc/Asst Prof) in Machine Learning: The University of Manchester is making a strategic investment in fundamentals of AI, to complement its existing strengths in AI applications across several prominent research fields in the University. Applications are welcome in any area of the fundamentals of machine learning, in particular probabilistic modelling, deep learning, reinforcement learning, causal modelling, human-in-the-loop ML, explainable AI, ethics, privacy and security. This position is meant to contribute to machine learning methodologies and not purely to their applications. You will be located in the Department of Computer Science and, in addition to the new centre for Fundamental AI research, you will belong to a large community of machine learning, data science and AI researchers. 2) Programme Manager – Centre for AI Fundamentals: The University of Manchester is seeking to appoint an individual with a strategic mindset and a track record of building and leading collaborative relationships and professional networks, expertise in a domain ideally related to artificial intelligence, excellent communication and interpersonal skills, experience in managing high-performing teams, and demonstrable ability to support the preparation of large, complex grant proposals to take up the role of Programme Manager for the Centre for AI Fundamentals. The successful candidate will play a major role in developing and shaping the Centre, working closely with its Director to grow the Centre and plan and deliver an exciting programme of activities, including leading key science translational activity and development of use cases in the Centre’s key domains, partnership development, bid writing, resource management, impact and public engagement strategies.

The Cooperative Graduate School Accessibility through AI-based Assistive Technology (KATE - www.kate.kit.edu) is a new cooperative and interdisciplinary graduate school between the Karlsruhe Institute of Technology (KIT) and the Hochschule Karlsruhe (HKA). The program revolves around investigating state-of-the-art artificial intelligence methods in order to improve the autonomy and participation of persons with special needs. Different dissertation topics ranging from AI-based methods for text, audio, and multimedia document processing, AI methods for interactive training and assistance systems, to investigating the consequences and ethical, legal, social, and societal implications of AI systems for people with disabilities will be offered. The sponsored persons will work on a selected topic scientifically in depth within the framework of their doctorate and will receive an overall view of all relevant topics - including medical causes as well as their effects, the needs of the target groups, AI-based approaches, ethics, technology assessment, and societal aspects - through the exchange within the doctoral college for this purpose.

13 PhD positions in Machine Learning, Statistics, Logic, Language Technology, and Ethics at Integreat, The Norwegian Centre for Knowledge-driven Machine Learning, University of Oslo & UiT - The Arctic University of Norway, Tromsø. The positions are part of an interdisciplinary PhD program with engaged supervisors and many fellow students. The projects include: Project 9: Embedded Sufficient Statistics (in Oslo), Project 4: Exploration and Control of the Inner Representation in Generative AI Models (in Tromsø), and Project 3: Developing Novel Information Theoretic Discrepancy Measures (in Tromsø).

SES neuroscience, using imaging and other methods, has revealed generalizations of interest for population neuroscience and the study of individual differences. But beyond its scientific interest, SES is a topic of societal importance. Does neuroscience offer any useful insights for promoting socioeconomic justice and reducing the harms of poverty? In this talk I will use research from my own lab and others’ to argue that SES neuroscience has the potential to contribute to policy in this area, although its application is premature at present. I will also attempt to forecast the ways in which practical solutions to the problems of poverty may emerge from SES neuroscience. Bio: Martha Farah has conducted groundbreaking research on face and object recognition, visual attention, mental imagery, and semantic memory and - in more recent times - has been at the forefront of interdisciplinary research into neuroscience and society. This deals with topics such as using fMRI for lie detection, ethics of cognitive enhancement, and effects of social deprivation on brain development.

Analogy offers a new interpretation of a common concern in ethics: whether decision making includes or excludes a consideration of moral issues. This is often discussed as the moral awareness of decision makers and considered a motivational concern. The possible new interpretation is that moral awareness is in part a matter of expertise. Some failures of moral awareness can then be understood as stemming from novicehood. Studies of analogical transfer are consistent with the possibility that moral awareness is in part a matter of expertise, that as a result motivation is less helpful than some prior theorizing would predict, and that many adults are not as expert in the domain of ethics as one might hope. The possibility of expert knowledge of ethical principles leads to new questions and opportunities.

A single person can be the source of billions of data points, whether these are generated from everyday internet use, healthcare records, wearable sensors or participation in experimental research. This vast amount of data can be used to make predictions about people and systems: what is the probability this person will develop diabetes in the next year? Will commit a crime? Will be a good employee? Is of a particular ethnicity? Predictions are simply represented by a number, produced by an algorithm. A single number in itself is not biased. How that number was generated, interpreted and subsequently used are all processes deeply susceptible to human bias and prejudices. This session will explore a philosophical perspective of data ethics and discuss practical steps to reducing statistical bias. There will be opportunity in the last section of the session for attendees to discuss and troubleshoot ethical questions from their own analyses in a ‘Data Clinic’.

Modern day “neurohackers” are radically self-experimenting, attempting genomic modification with CRISPR-Cas9 constructs and electrode insertion into their cortex amongst a host of other things. Institutions wanting to avoid the risks bought on by these procedures, generally avoid involvement with self-experimenting research. Modern day “neurohackers” are radically self-experimenting, attempting genomic modification with CRISPR-Cas9 constructs and electrode insertion into their cortex amongst a host of other things. Institutions wanting to avoid the risks bought on by these procedures, generally avoid involvement with self-experimenting research. But what is the ethical thing to do? Should researchers be allowed or encouraged to self-experiment? Should institutions support or hinder them? Where do you think that this process of self-experimentation could take us? This presentation by Dr Matt Lennon and Professor Zoltan Molnar of the University of Oxford, will explore the history, future and ethics of self-experimentation. It will explore notable examples of self-experimenters including Isaac Newton, Angelo Ruffini and Oliver Sacks and how a number of these pivotal experiments created paradigm shifts in neuroscience. The presentation will open up a forum for all participants to be involved asking key ethical questions around what should and should not be allowed in self-experimentation research.

Mahzarin Banaji and her colleague coined the term “implicit bias” in the mid-1990s to refer to behavior that occurs without conscious awareness. Today, Professor Banaji is Cabot Professor of Social Ethics in the Department of Psychology at Harvard University, a member of the American Academy of Arts and Sciences, the National Academy of Sciences and has received numerous awards for her scientific contributions. The purpose of the seminar, Blindspot: Hidden Biases of Good People, is to reveal the surprising and even perplexing ways in which we make errors in assessing and evaluating others when we recruit and hire, onboard and promote, lead teams, undertake succession planning, and work on behalf of our clients or the public we serve. It is Professor Banaji’s belief that people intend well and that the inconsistency we see, between values and behavior, comes from a lack of awareness. But because implicit bias is pervasive, we must rely on scientific evidence to “outsmart” our minds. If we do so, we will be more likely to reach the life goals we have chosen for ourselves and to serve better the organizations for which we work.