EMANUEL MOSS CUNY/Data & Society FRIEDERIKE SCHÜÜR Cityblock Health [s2If is_user_logged_in()][/s2If] [s2If current_user_can(access_s2member_level1)] Resources Case Studies, Recommendations, & Solutions (PDF) IBM Fairness 360 Datasheets for Data Sets Model Cards for Model Reporting Consequence Scanning Owning Ethics [/s2If] [s2If !is_user_logged_in()] Please sign in or become an EPIC Member to access video. [/s2If] [s2If current_user_is(subscriber)] Become an EPIC Member to access video: → Learn about Membership → Browse Video Library [/s2If] 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...

EMANUEL MOSS CUNY Graduate Center / Data & Society FRIEDERIKE SCHÜÜR Cloudera Fast Forward Labs [s2If is_user_logged_in()] Download PDF [/s2If] [s2If current_user_can(access_s2member_level1)] [/s2If] 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...