By MARIA CURY, MIKKEL KRENCHEL and MILLIE P. ARORA, ReD Associates
To influence the development of artificial intelligence, ethnographers must build more partnerships and new kinds of outputs.
Artificial intelligence (AI) has made huge strides recently in areas like natural language processing and computer-generated images – every other week seems to bring another breathtaking headline. Engineers, developers, and policymakers in the AI community are more seriously grappling with the fundamental risks that AI poses to society, like perpetuating unfair biases, putting privacy and security at risk, harming mental health, or automating tasks that provide livelihoods for people. As people flock to the fields of 'responsible AI,’ ‘AI ethics,’ and ‘AI governance’ that are all about shaping AI towards what is helpful for humanity, it is time we ask: where are the ethnographers and applied anthropologists?
Many are doing ground-breaking work in AI, and reporting back to the EPIC community (see here, here, here, also here for just...
CUNY/Data & Society
Technology companies have discovered ethics in the wake of public pressure to consider the consequences of their products. This has been prompted by the finding that machine learning and artificial intelligence (ML/AI) systems, as fundamentally pattern-seeking technologies, can and do exacerbate long-term structural inequalities. Companies and employees also struggle with the challenges posed by the dual-use nature of technology.
This tutorial will prepare you to understand and contribute to the more ethical development and deployment of ML/AI systems. It covers:
An overview of ethical challenges in ML/AI today
An introduction to the development of ML/AI systems, designed to give you insight into the reasoning processes and workflows of technical colleagues and how they generally address issues like accuracy and fairness (no quantitative background required!)
A overview of current efforts to design more ethical ML/AI systems,...
by MINNA RUCKENSTEIN, University of Helsinki
It is easy to become pessimistic, if not dystopic, about tracking technologies. The current digital services landscape promotes scoring, selecting and sorting of people for the purposes of maximizing profit. Machine logics rely on profiling characteristics and predicting actions, and management by algorithms appears to be disproportionately affecting those with temporary and low-income jobs. Tracking technologies become complicit in deepening and accelerating social divisions and inequalities. The most vulnerable in societies have no say in how their actions are monitored and lives are harmed by algorithmically produced metrics.
In this context, Quantified Self (QS) – an international community of ‘self-trackers’ that shares insights gained through self-quantification and data analysis – seems rarified, an example of the privileged techno-elite positioned to use tracking data to pursue their own values and goals. With this limitation, QS hardly appears to be a useful prism...
Instructor: IAN LOWRIE
Approx 1 hr 43 min. This video presents the lecture portion of a half-day tutorial. Case studies and a bibliography are provided for your use.
Instructor Ian Lowrie describes the organizational and technological aspects of modern data pipelines, framing data science ethnographically as a knowledge practice and data scientists as a particular kind of expert. He also explores methodological approaches to studying data work in real-world contexts. Participants learned to:
Think ethnographically about data work as a knowledge practiceDevelop methodological strategies for studying data workChart the organizational and technological components of data infrastructureInterpret the mindset, jargon, and practical orientations of their data scientist and developer colleaguesUnderstand how algorithmic systems and data analytics impact organizational structures, work practices, and business models
In the second half of the tutorial, participants worked collaboratively to develop a pitch for...