FRIEDERIKE SCHÜÜR
Intelligences
Tutorial: Ethics in Data-Driven Industries
EMANUEL MOSS
CUNY/Data & Society
FRIEDERIKE SCHÜÜR
Cityblock Health
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Resources
Case Studies, Recommendations, & Solutions (PDF)
IBM Fairness 360
Datasheets for Data Sets
Model Cards for Model Reporting
Consequence Scanning
Owning Ethics
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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...
Intelligences
Hybrid Methodology: Combining Ethnography, Cognitive Science, and Machine Learning to Inform the Development of Context-Aware Personal Computing and Assistive Technology
MARIA CURY*
ReD Associates
ERYN WHITWORTH*
Facebook Reality Labs
*Lead co-authors[s2If is_user_logged_in()]DOWNLOAD PDF
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The not-too-distant future may bring more ubiquitous personal computing technologies seamlessly integrated into people's lives, with the potential to augment reality and support human cognition. For such technology to be truly assistive to people, it must be context-aware. Human experience of context is complex, and so the early development of this technology benefits from a collaborative and interdisciplinary approach to research — what the authors call “hybrid methodology” — that combines (and challenges) the frameworks, approaches, and methods of machine learning, cognitive science, and anthropology. Hybrid methodology suggests new value ethnography can offer, but also new ways ethnographers should adapt their methodologies, deliverables, and ways of collaborating for impact in this space. This paper outlines a few of the data collection...
Intelligences
How Modes of Myth-Making Affect the Particulars of DS/ML Adoption in Industry
EMANUEL MOSS
CUNY Graduate Center / Data & Society
FRIEDERIKE SCHÜÜR
Cloudera Fast Forward Labs
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Download PDF
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The successes of technology companies that rely on data to drive their business hints at the potential of data science and machine learning (DS/ML) to reshape the corporate world. However, despite the headway made by a few notable titans (e.g., Google, Amazon, Apple) and upstarts, the advances that are advertised around DS/ML have yet to be realized on a broader basis. The authors examine the tension between the spectacular image of DS/ML and the realities of applying the latest DS/ML techniques to solve industry problems. The authors discern two distinct ways, or modes, of thinking about DS/ML woven into current marketing and hype. One mode focuses on the spectacular capabilities of DS/ML. It expresses itself through one-off, easy-to-grasp marketable projects, such as DeepMind’s AlphaGo (Zero). The other mode focuses...