Learn models and principles to ensure organizations are creating, using, and deploying AI that coworkers, customers, and society can trust. Instructors: KATHY BAXTER, Principal Architect of Ethical AI Practice, Salesforce) & YOAV SCHLESINGER, Architect of Ethical AI Practice, Salesforce Overview This video has been edited to protect the privacy of participants in the live tutorial. Our lives are directed, enriched, influenced, and sometimes harmed by AI, in both obvious and not-so-obvious ways. Although many AI regulations have been implemented in the last few years and more regulation is coming, organizations cannot wait until they are compelled by external forces to develop responsible AI practices. For the sake of your business, customers, and society, everyone has a responsibility to ensure that the technology they help build, sell or deploy is fair, transparent and ethical. In this tutorial, we will walk participants through the steps of creating a responsible AI practice using a combination of lecture,...
The Ethnography of a ‘Decentralized Autonomous Organization’ (DAO): De-mystifying Algorithmic Systems
Dhanabir Sharma • 0 Comments
KELSIE NABBEN RMIT University, Centre for Automated Decision Making & Society / BlockScience MICHAEL ZARGHAM WU Vienna / BlockScience This paper details ethnographic methods, experiences, and insights from an ethnographer and an industry engaged complex systems engineer in how to study resilience in blockchain-based DAOs as a novel field site. Amidst digitization of numerous elements of government, work, and everyday life, ‘Decentralized Autonomous Organizations’ (DAOs) provide a field site for the generation of ethnographic insights into opportunities and limitations in organizational resilience in human-machine assemblages. As a broad organizational form, DAOs aim to enable people to coordinate and govern themselves through automated rules deployed on a public blockchain (Hassan & Di Filippi, 2021). DAOs are an experiment in ‘computer aided governance’. These adaptive, socio-technical infrastructures are envisioned as capable of restructuring the foundations of governance in human societies (Merkle, 2016;...
Ethnography for the AI Age: How to Get Started
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...
Tutorial: Ethics in Data-Driven Industries
Jennifer Collier Jennings • 0 Comments
EMANUEL MOSS CUNY/Data & Society FRIEDERIKE SCHÜÜR Cityblock Health Overview 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,...
Livable Relations with Metrics
Jennifer Collier Jennings
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...
Tutorial: Doing Ethnography of Data Science & Algorithmic Systems
Jennifer Collier Jennings
Instructor: IAN LOWRIE Description 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...