Human-Centric Machine Learning (HCML) Reading Group

A reading group focused on human-centric machine learning organised by the PhD students at the ELLIS Unit Alicante. This reading group is open to all students from the ELLIS PhD & Postdoc program.

The HCML reading group aims to gather researchers and students interested in both getting a wide vision of the topic and also deeply diving into it. Reading papers about different topics inside HCML, and also discussing new problem set-ups, different approaches, and sources of bias will lead us to a broad understanding of how algorithmic and human decisions influence each other.

Communication:

Meetings

Towards a Theory of Justice for Artificial Intelligence

Author: Iason Gabriel (2021)

Article link: https://arxiv.org/abs/2110.14419

This paper explores the relationship between artificial intelligence and principles of distributive justice. Drawing upon the political philosophy of John Rawls, it holds that the basic structure of society should be understood as a composite of socio-technical systems, and that the operation of these systems is increasingly shaped and influenced by AI. As a consequence, egalitarian norms of justice apply to the technology when it is deployed in these contexts. These norms entail that the relevant AI systems must meet a certain standard of public justification, support citizens rights, and promote substantively fair outcomes – something that requires specific attention be paid to the impact they have on the worst-off members of society.

Presenter: Nazaal Ibrahim

Date: Thursday 20th of January at 15.00 CET


Rethinking of Marxist perspectives on big data, artificial intelligence (AI) and capitalist economic development

Authors: Nigel Waltona, Bhabani Shankar Nayak (2021)

Article link: https://www.sciencedirect.com/science/article/abs/pii/S0040162521000081

AI and big data are not ideologically neutral scientific knowledge that drives economic development and social change. AI is a tool of capitalism which transforms our societies within an environment of technological sin- gularity that helps in the expansion of the capitalist model of economic development. Such a development process ensures the precarity of labour. This article highlights the limits of traditional Marxist conceptualisation of labour, value, property and production relations. It argues for the rethinking of Marxist perspectives on AI led economic development by focusing on conceptual new interpretation of bourgeois and proletariat in the infor- mation driven data-based society. This is a conceptual paper which critically outlines different debates and challenges around AI driven big data and its implications. It particularly focuses on the theoretical challenges faced by labour theory of value and its social and economic implications from a critical perspective. It also offers alternatives by analysing future trends and developments for the sustainable use of AI. It argues for developing policies on the use of AI and big data to protect labour, advance human development and enhance social welfare by reducing risks.

Presenter: Bhargav Srinivasa Desikan

Open notes: shared document

Date: Thursday 16th of December at 15.00 CET


Machine Learning for the Developing World

Authors: De-Arteaga, M., Herlands, W., Neill, D. B., & Dubrawski, A. (2018)

Article link: https://dl.acm.org/doi/abs/10.1145/3210548

Researchers from across the social and computer sciences are increasingly using machine learning to study and address global development challenges. This article examines the burgeoning field of machine learning for the developing world (ML4D). First, we present a review of prominent literature. Next, we suggest best practices drawn from the literature for ensuring that ML4D projects are relevant to the advancement of development objectives. Finally, we discuss how developing world challenges can motivate the design of novel machine learning methodologies. This article provides insights into systematic differences between ML4D and more traditional machine learning applications. It also discusses how technical complications of ML4D can be treated as novel research questions, how ML4D can motivate new research directions, and where machine learning can be most useful.

Presenter: Felix Grimberg

Open notes: shared document

Date: Thursday 2nd of December at 15.00 CET

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